本篇博文主要内容为 2026-07-15 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 (2026-07-15)
今日共更新583篇论文,其中:
- 自然语言处理共69篇(Computation and Language (cs.CL))
- 人工智能共158篇(Artificial Intelligence (cs.AI))
- 计算机视觉共116篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共144篇(Machine Learning (cs.LG))
- 多智能体系统共15篇(Multiagent Systems (cs.MA))
- 信息检索共17篇(Information Retrieval (cs.IR))
- 人机交互共18篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Privacy Attacks on Stable Marriage
【速读】：该论文旨在解决在稳定婚姻问题（Stable Marriage Problem）应用中，如何保护参与方隐私偏好列表免受隐私攻击的关键挑战。在诸如美国住院医师匹配项目（NRMP）等隐私敏感场景中，若不加防护地使用传统算法，恶意方（如医院）通过反复与匹配算法交互，可能推断出诚实方（如住院医师）的完整偏好顺序，从而导致战略操纵、虚假申报及违反数据保护法规。其核心解决方案在于揭示：当采用广为使用的盖尔-沙普利算法（Gale-Shapley Matching Algorithm）且提议方为恶意方时，所有诚实方的偏好信息均存在被完全泄露的风险；而当诚实方作为提议方且其偏好分布非易受攻击类型时，算法可实现隐私保护。研究进一步拓展至去中心化场景，证明攻击者仍可推断出全部偏好排序。实验评估表明真实世界数据确实面临此类隐私威胁，因此亟需设计新型具备隐私保护能力的稳定婚姻算法。
链接: https://arxiv.org/abs/2607.13015
作者: Stephan A. Fahrenkrog-Petersen,Aleksander Figiel,Darya Melnyk,Tijana Milentijević,Stefan Schmid
机构: University of Liechtenstein (列支敦士登大学); TU Berlin (柏林工业大学); Weizenbaum Institute (魏泽瑙姆研究所)
类目: Data Structures and Algorithms (cs.DS); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
备注: Accepted and presented at the 2026 IEEE International Conference on Distributed Computing Systems (ICDCS 2026)
Abstract:The stable marriage problem appears in many privacy-sensitive domains, for example in the National Resident Matching Program in the US. In such applications, preserving the privacy of users’ preference lists is essential to prevent strategic manipulation, discourage misreporting, and comply with data protection regulations. In this work, we investigate privacy attacks on stable marriage algorithms. Assuming that the attacker (e.g., the hospitals) can repeatedly interact with the stable marriage algorithm, we demonstrate how such interactions can reveal private preferences of the non-malicious side (e.g., the residents). We show that the widely applied Gale-Shapley Matching Algorithm, where the proposers’ side is malicious, is vulnerable to privacy attacks and all honest agents’ preferences can be revealed. We further investigate which preference distributions of the honest, non-malicious side are susceptible to privacy attacks and show that the Gale-Shapley Matching Algorithm where the honest side proposes can preserve privacy in non-susceptible preference distributions. We extend our results to the decentralized setting and show that the attacker’s side can infer all preference orderings. In an experimental evaluation, we test privacy attacks on synthetic and real-world data and show that real-world data is indeed susceptible to privacy attacks. This work underlines a need for new privacy-preserving stable marriage algorithms. Comments: Accepted and presented at the 2026 IEEE International Conference on Distributed Computing Systems (ICDCS 2026) Subjects: Data Structures and Algorithms (cs.DS); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA) Cite as: arXiv:2607.13015 [cs.DS] (or arXiv:2607.13015v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2607.13015 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-1] FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation
【速读】：该论文旨在解决解析几何（analytic geometry）领域中多模态问题数据集稀缺的问题，尤其针对现有图示生成方法在处理约束驱动布局和几何精度方面存在的不足。其核心挑战在于：传统模板法难以应对复杂的几何约束，而生成式模型又缺乏对圆锥曲线等几何对象的精确渲染能力。解决方案的关键在于提出一种名为FormalAnalyticGeo的可扩展框架，通过引入条件描述语言（CDL, Condition Description Language）这一形式化中间表示，将自然语言问题转化为可精确计算的符号表达，并利用有向距离场（SDF, Signed Distance Field）引擎实现高精度图形渲染。该框架由四个专用大语言模型（LLM）组件协同工作：生成器负责生成多样化问题，形式化器将问题转换为CDL以支持SDF渲染，测量器基于渲染图像进行视觉测量获取真实答案，质量验证器则在三阶段对输出进行结构化评估并驱动自动重试，形成闭环无需人工标注。该方法最终构建了包含7,000余条经验证的多模态解析几何问题的数据集AnalyticGeo7K，其生成问题的中位数相对误差仅为0.70%，82.3%的答案与精确符号解偏差小于5%，显著提升了生成内容的准确性与可用性。
链接: https://arxiv.org/abs/2607.12982
作者: Ruoran Xu,Wending Gao,Qiufeng Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Symbolic Computation (cs.SC)
备注:
Abstract:Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remains largely underexplored, primarily due to the scarcity of annotated samples. Existing diagram generation approaches struggle with analytic geometry: template methods cannot handle constraint-driven layouts, and generative models lack the geometric precision to render annotated conic curves correctly. We present FormalAnalyticGeo, a scalable framework for fully automatic generation of multimodal analytic geometry problems. Leveraging the rigor of formal languages, we design the framework around CDL (Condition Description Language), a formal intermediate representation that bridges free-form problem text with precise diagram rendering via a Signed Distance Field (SDF) engine. The framework employs four specialized LLM components in sequence: a Generator that produces diverse analytic geometry problems, a Formalizer that converts each problem into CDL for SDF-based rendering, a Measurer that extracts ground-truth answers through vision-based measurement on the rendered diagrams, and a Quality Verifier that checks outputs at three stages. Structured feedback from the Quality Verifier drives automatic retry, forming a closed loop that eliminates any need for human annotation. Applying FormalAnalyticGeo at scale yields AnalyticGeo7K, a dataset of over 7K verified multimodal problems, each with aligned text, diagram, formal annotation, and ground this http URL show that the generated problems achieve a median ground-truth relative error of 0.70%, with 82.3% of answers falling within 5% of the exact symbolic solution. Our framework and dataset will be publicly released.
[MA-2] MetaInfer: A Knowledge Only LLM Inference Engine Generator SKILL Toolbox
【速读】：该论文旨在解决大语言模型（LLM）推理框架在面对日益复杂的模型家族、计算硬件、量化方案、并行策略及专用优化内核时，所面临的代码复杂度高与维护成本剧增的问题。传统软件工程通过多层抽象支持多样化应用场景，但往往引入额外系统复杂性与性能开销。本文提出“LLM-as-Compiler”范式——metainfer，其核心在于用户仅需声明推理程序的运行时约束，由一个基于大语言模型驱动的多智能体协作系统结合契约知识库（Contract Knowledge Base, CKB），自动构建满足约束条件的轻量级定制化推理框架。该方案的关键在于将生成约束、验证反馈与知识固化整合为持续闭环，实现从显式知识中自动生成可运行的定制化推理解决方案。实验从源码引用影响、零参考约束下对CKB覆盖目标的运行时行为与性能表现，以及知识库在新模型与平台场景下的演进能力三个维度验证了其有效性。
链接: https://arxiv.org/abs/2607.12875
作者: Zhenwen Miao,Honglin Wang,Mingheng Mi
机构: 未知
类目: Multiagent Systems (cs.MA); Software Engineering (cs.SE)
备注:
Abstract:As LLM technology advances, the space of model families, compute hardware, quantization schemes, parallelization strategies, and specialized optimization kernels continues to expand, sharply increasing the code complexity and maintenance cost of general-purpose inference frameworks. Conventional software engineering uses multiple layers of abstraction to support diverse application scenarios, but these abstractions also increase system complexity and may introduce additional performance overhead. This paper presents metainfer, an ‘LLM-as-Compiler’ approach in which users specify only the runtime constraints of an inference program. An LLM-driven multi-agent collaboration system, coupled with a contract knowledge base, then automatically generates a compact customized inference framework that satisfies these constraints. We evaluate metainfer from three perspectives: the effect of source-code reference, the runtime behavior and performance profile of engines generated under the zero-reference constraint on CKB-covered targets, and knowledge-base evolution for new model and platform scenarios. The results show that metainfer organizes generation constraints, validation feedback, and knowledge consolidation into a continuous closed loop, enabling runnable customized inference solutions to be generated from explicit knowledge. The code is publicly available at this https URL.
[MA-3] Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
【速读】：该论文旨在解决自演化智能体系统（self-evolving agent systems）在缺乏可靠评估指标（evaluation metric）情况下仍需持续迭代技能所面临的根本性挑战。其核心问题是：当前自演化框架普遍依赖于预先存在的、可靠的评估指标，但在多数实际应用场景中，此类指标并不存在或难以定义。为应对这一问题，论文提出三项关键主张：第一，评估指标本身可“进化”（metric evolved），通过在完整演化生命周期中搜索由小型缺陷检测器（drawback detectors）组合而成的度量方案，使其与一个锚定参考集（anchored reference set）中的10个样本达成一致，并通过未标注输出的共识进行正则化，同时在独立保留的锚点上进行审计，从而生成透明且可审查的评估指标；第二，由于不存在优于基准的客观度量标准，衡量演化的成功与否应聚焦于恢复“若存在准确指标所能带来的性能提升”，而提出的双棘轮机制（Double Ratchet）——即评估指标与生命周期管理技能循环的协同演化——在代码生成（MBPP+）、企业级文本转SQL（Spider 2.0-Snow）以及无参考报告生成任务中，均保持了88%–110%的保留性能，接近基于真实标签或最优评分标准驱动的基准表现；第三，安全性源于锚点约束与外部审计机制的结合：移除锚点保护会导致度量失效，而仅取消生命周期管理不会造成类似崩溃；当演化出的技能试图“欺骗”报告评分标准时，独立评审者识别出问题，单一检测器修复后，任务感知的评判者在77%的对比对中更偏好演化后的输出。因此，论文主张，在缺乏可靠自动验证器的场景下，这种以失败为前提的设计架构应成为默认范式。
链接: https://arxiv.org/abs/2607.12790
作者: Xing Zhang,Guanghui Wang,Yanwei Cui,Ziyuan Li,Wei Qiu,Bing Zhu,Peiyang He
机构: Amazon(亚马逊)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:Self-evolving agent systems improve by creating, revising, and retiring their own skills, but every such loop rests on a hidden assumption: a reliable evaluation metric already exists. In many real applications it does not. We make three claims. First, metrics can be \emphevolved: our metric loop searches compositions of small drawback detectors under a full evolutionary lifecycle, trained to agree with a ten-item anchored reference set, regularized by consensus over unlabeled outputs, and audited against a held-out anchor it never reads, yielding a transparent, inspectable metric rather than an opaque judge. Second, since no metric exists to beat, the yardstick is recovering what an accurate metric would have enabled, and \emphDouble Ratchet, our co-evolution of the metric with a lifecycle-managed skill loop, does so: across code generation (MBPP+), enterprise text-to-SQL (Spider~2.0-Snow), and reference-free report generation, it retains 88–110% of the held-out lift achieved by the same skill loop driven by ground truth or the best available rubric. Third, safety comes from anchor discipline plus outer audits: removing anchor guards collapses the metric into a vacuous detector while removing the lifecycle does not; and when evolved skills gamed the report rubric, an independent judge caught it, one detector repaired it, and a task-aware judge then preferred the evolved outputs over the pre-evolution baseline in 77% of decided pairs. We argue this failure-expecting architecture is the right default wherever no reliable automatic verifier exists.
[MA-4] Aïra: Rethinking AI Research Assistants for Interdisciplinary Science
【速读】：该论文旨在解决当前人工智能研究助手（AI research assistant）在跨学科科研协作中支持能力不足的问题。现有AI工具主要聚焦于提升个体研究人员的效率，如文献综述、写作辅助、编程与数据分析等，但难以有效支持跨学科团队在知识整合过程中所需的协同推理。其核心挑战在于不同学科间存在差异化的概念框架、术语体系、假设前提和证据标准，导致信息难以互通与融合。本文的关键解决方案是提出并构建aïra——一个以跨学科团队协作为核心设计目标的AI研究助手。其创新之处在于超越传统的摘要生成或问答功能，转而具备识别学科视角、术语翻译、揭示隐含假设以及促进跨领域知识整合与合作机会发现的能力。通过系统架构设计与实际研究会议场景的演示，验证了aïra在支持多学科协同科学探索中的可行性与潜力，为未来面向协作型学术研究的AI系统发展指明了方向。
链接: https://arxiv.org/abs/2607.12736
作者: Diya Mirji,Tiffany Degbotse,Sharmil Nanjappa,Jiayi Zhou,Vishnu Manoj,Vihaan Nama,Boyuan Chen,Lee Tiedrich,Christopher Bail,David W. Johnston,Walter Sinnott-Armstrong,Brinnae Bent
机构: 未知
类目: Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注:
Abstract:Scientific discovery increasingly depends on interdisciplinary teams whose members contribute distinct expertise, conceptual frameworks, vocabularies, assumptions, and standards of evidence. Today’s AI research assistants are largely designed to support individual researchers through literature review, writing assistance, coding, and data analysis. While these capabilities improve personal productivity, they provide little support for the collaborative reasoning required to integrate knowledge across disciplines. We argue that AI research assistants should evolve from tools that optimize individual workflows to systems designed for interdisciplinary teams. We introduce aïra, an AI research assistant built around this idea. Rather than focusing solely on summarization or question answering, aïra identifies disciplinary perspectives, translates terminology, highlights assumptions, and synthesizes collaborative research opportunities. We describe the design principles underlying aïra, present its system architecture, illustrate its outputs through interdisciplinary research meetings, and outline future research directions for AI systems that support collaborative scholarship.
[MA-5] Internet of Agent ic Things: Networked AI Agents for Closed-Loop IoT Orchestration
【速读】：该论文旨在解决多源异构的智能物理系统在复杂分布式环境中协同运作时面临的集成与自治难题，特别是如何实现从感知、决策到执行的闭环控制。其核心挑战在于如何在跨云-边-端的多层次架构中，实现具备自主性、适应性和可追溯性的智能体（Agentic AI）对物理世界与数字孪生系统的高效协同管理。解决方案的关键在于提出一种名为“智能体物联”（Internet of Agentic Things, IoAT）的统一闭环编排框架，通过引入具有形质融合（hylomorphic）特性的动态规划方法，将高层战略规划与底层战术决策进行耦合建模，并依托自主智能体在云、边缘/雾及物理物联网层之间实现感知、推理、协调与执行的全链路联动，从而支撑如智慧建筑等典型应用场景下的可信、安全与韧性部署。
链接: https://arxiv.org/abs/2607.12662
作者: Quanyan Zhu
机构: New York University, Tandon School of Engineering (纽约大学, 托多恩工程学院)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注:
Abstract:The paper introduces the Internet of Agentic Things (IoAT), an architectural framework that integrates agentic AI, IoT, cyber-physical systems, Physical AI, edge computing, and digital twins into a unified closed-loop orchestration framework. The proposed architecture consists of cloud, edge/fog, and physical IoT layers connected through autonomous AI agents that perceive, reason, coordinate, and actuate across distributed cyber-physical environments. The paper formalizes IoAT as a coupled workflow-control problem with nested strategic and tactical decision making using a hylomorphic dynamic programming framework that links agentic planning with physical execution. Smart-building orchestration is presented as a representative use case, and key research challenges related to safety, security, governance, resilience, and trustworthy deployment are discussed.
[MA-6] XScientist: A Git-Like Research Protocol for Long-Running Autonomous Scientific Discovery
【速读】：该论文旨在解决当前自主科研系统在实际应用中缺乏可信度的核心问题：尽管现有系统常被评估为“一次性论文生成器”（即输入一个主题后输出一篇论文和少量实验日志），但真实科研过程具有长期性、多分支性、高失败率以及依赖可审计的人机协作等复杂特性，而这些关键操作维度在传统评估框架下被严重忽视。其解决方案的关键在于提出XScientist——一种类Git的科研协议与操作系统，将研究活动整体抽象为一个持续可观测的流水线，实现从想法生成、实验执行、论文撰写、自我审查、修复、质量门控、后台任务调度到可复现性产物生成的全流程协同。核心创新是将每一次研究运行视为可移植的研究成果（Research Artifact），而非仅输出一份PDF文档；通过定义代理原生研究成果（Agent-Native Research Artifact, ARA）协议，系统完整记录探索有向无环图（exploration DAG）、节点级代码与输出、主张-证据锚点、内容哈希、溯源信息及重执行钩子，使得每篇生成论文均可被追溯为一个科学探索树，所有失败分支、修复实验、消融分析与论文主张均与生成节点保持关联。此外，系统还集成确定性完整性取证、样本门控、真理契约、面向审稿人的修复循环机制以及长周期守护进程控制，从而构建可复现、可审查、可分叉的自主科研基础设施。
链接: https://arxiv.org/abs/2607.12301
作者: Jixiang Luo
机构: 未知
类目: oftware Engineering (cs.SE); Multiagent Systems (cs.MA)
备注:
Abstract:Autonomous research systems are often evaluated as one-shot paper generators: given a topic, they produce a manuscript and a small set of experiment logs. This framing hides the operational problem that makes such systems difficult to trust: research is long-running, branching, failure-prone, and dependent on auditable handoffs between agents and humans. XScientist is a git-like research protocol and operating system for this setting. It orchestrates idea generation, experiment execution, manuscript drafting, self-review, repair, quality gating, daemon scheduling, and reproducibility artifacts as one continuously observable pipeline. The central design choice is to treat each run as a portable research artifact rather than only as a PDF. XScientist exports an Agent-Native Research Artifact (ARA), a protocol that records an exploration DAG, per-node code and outputs, claim-to-evidence anchors, content hashes, provenance, and re-execution hooks. This makes each generated paper inspectable as a science exploration tree: failed branches, repaired experiments, ablations, and manuscript claims remain connected to the nodes that produced them. The system also includes deterministic integrity forensics, sample gates, truth contracts, reviewer-oriented repair loops, and long-running daemon controls. This paper describes the current XScientist architecture, the ARA protocol surface, and the practical safeguards needed to move autonomous science from single-run demos toward reproducible, reviewable, and forkable research infrastructure. The implementation and manuscript source are maintained in the public GitHub repository at this https URL.
[MA-7] RCWT: Measuring Task-Budget Displacement from Coordination Content in LLM Calls
【速读】：该论文旨在解决多智能体与记忆增强型大语言模型（LLM）系统中因共享有限上下文窗口（context window）而导致的任务预算位移（task-budget displacement）问题。具体而言，当系统需在固定上下文长度内同时容纳任务指令、共享状态、历史对话、工具输出及角色设定等协调内容时，每增加一个协调性令牌便减少可用于任务关键信息（如证据或指令）的令牌数量，从而影响模型性能。其解决方案的关键在于提出圆桌上下文窗口测试（Roundtable Context Window Test, RCWT），通过严格控制总预算、位置顺序、任务类型和评分标准，在不同协调内容比例下量化这种位移效应。实验发现，在4096令牌的上下文窗口下，三款商用模型在协调内容适度增长时性能保持稳定，但一旦剩余可分配给任务证据的令牌降至数百级别，性能急剧下降；而通过引入“完整任务块保留”消融实验（即保持任务证据不变，仅扩展提示总长度以容纳更多协调内容），所有模型在高达95%协调比例下仍能正确返回全部评分字段。这一结果表明，主实验中的性能断崖并非由协调内容本身引发语义干扰，而是源于任务预算被挤占所致。因此，RCWT的核心贡献在于提供一种用于上下文资源分配优化的测量基准，而非对多智能体协作机制的完整理论解释。
链接: https://arxiv.org/abs/2607.12216
作者: Brenda Lelis,Rodrigo Cabral-Carvalho
机构: CloudWalk, Inc.(云漫步公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 10 pages, 1 figure
Abstract:Multi-agent and memory-augmented LLM systems often place coordination content, shared state, prior discussion, tool outputs, summaries, and role instructions, inside the same finite prompt used for the current task. This creates a practical allocation problem: every token spent on coordination is unavailable to task instructions or evidence when a call is assembled under a fixed context budget. We introduce the Roundtable Context Window Test (RCWT), a controlled protocol for measuring this task-budget displacement effect. RCWT varies coordination content while controlling total budget, position order, task family, and scoring. In the main context-dependent recall task at W=4096 , three commercial models remain near baseline through moderate overhead and then degrade sharply once residual reference evidence falls to a few hundred tokens. Window-scaling summaries are consistent with a task-specific residual-budget interpretation rather than a fixed percentage threshold, but we treat this as descriptive evidence rather than a universal law. To test whether the fixed-budget cliff persists when task evidence remains intact, we add an intact-task ablation: the full task/reference block is kept present while coordination tokens increase by expanding total prompt length. In that setting, all tested calls return every scored field correctly across GPT-4.1-mini, Claude Haiku 4.5, and Gemini 2.5 Flash up to a 95% coordination ratio. This ablation narrows the claim: the main RCWT cliff is best read as task-budget displacement, not as proof that coordination volume alone causes semantic interference in the original open-ended task. RCWT is therefore a measurement primitive for context-allocation budgeting, not a complete theory of multi-agent benefit or session-level coordination.
[MA-8] Fine-Tuned Multi-Agent Framework for Detecting OCEAN in Life Narratives
【速读】：该论文旨在解决从长篇文本中准确识别人格特质（OCEAN：外向性、宜人性、尽责性、情绪稳定性、开放性）的难题，因其特质具有潜在性、情境依赖性和表达隐含性，传统方法难以捕捉其细微差异。尽管大语言模型（LLM）具备处理长文本上下文的能力，但其预训练过程可能引入潜在的“人格类”偏差，导致单一模型推理结果不一致。为此，论文提出一种微调的多智能体框架，通过掩码语言建模（MLM）与心理测量学监督相结合，使各子智能体分别以高分、低分或中立视角对每种人格特质进行建模，从而生成多角度的判断。一个裁判型大语言模型负责聚合并比较子智能体输出，综合不同视角以生成最终的人格预测，有效缓解个体模型偏差，提升推理的鲁棒性与可解释性。实验在生活叙事数据集上进行，涵盖定量评估、消融实验及推理质量分析，验证了该方法在可扩展性与可解释性方面的优势，凸显了基于心理测量学监督的多智能体推理在文本人格推断中的有效性。
链接: https://arxiv.org/abs/2607.12215
作者: Rasiq Hussain,Darshil Italiya,Joshua Oltmanns,Mehak Gupta
机构: Southern Methodist University (南卫理公会大学); Washington University in St. Louis (圣路易斯华盛顿大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Accurately assessing personality from text is challenging because traits are latent, context-dependent, and often subtly expressed across long narratives. Large language models (LLMs) offer new opportunities by processing extensive textual contexts, but pretraining of these models can induce latent “personality-like” biases, making single-model inferences inconsistent. We propose a fine-tuned multi-agent framework for detecting OCEAN personality traits, in which sub-agents are conditioned to adopt high, low, or neutral perspectives for each trait through masked language modeling (MLM) and psychometric supervision. A judge LLM aggregates and compares sub-agent outputs to generate final trait predictions, capturing multiple complementary perspectives while mitigating individual model biases. We evaluate the framework on life narrative dataset through quantitative and qualitative experiments, including baselines, ablations, and inference quality analyses. Our approach offers a scalable and interpretable method for text-based personality inference, highlighting the benefits of multi-agent reasoning grounded in psychometric supervision.
[MA-9] PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLM s
【速读】：该论文旨在解决在异构网络环境下，分布式边缘设备中多模态大语言模型（Multimodal Large Language Models, MLLMs）进行联邦微调时，如何有效平衡全局知识聚合与本地个性化适应之间的矛盾问题。传统联邦学习协议采用统一参数聚合策略，导致域不变特征与客户端特异性信息混杂，造成个性化能力不足及通信开销过高。其解决方案的关键在于提出PFAdapter框架，通过分层低秩适配器（LoRA）分解，显式分离适配器参数为全局共享与本地私有两部分：查询（query）和键（key）投影参数在全局同步以捕捉跨网络的通用多模态语义，而值（value）和输出投影参数则保留在本地以支持边缘端特定适应；同时引入基于Frobenius范数的正交性约束，强化两类参数间的解耦，避免冗余特征学习；进一步采用选择性聚合机制，仅同步全局共享组件，从而将通信成本降低近50%。实验在VQA-RAD、SLAKE、Hateful Memes和CrisisMMD等多个数据集上验证了该方法在多种边缘智能任务中的优越性，准确率提升达2.4%至4.8%，显著优于现有先进基准，为资源受限通信网络中代理式AI（Agentic AI）的高效部署提供了可行路径。
链接: https://arxiv.org/abs/2607.12111
作者: Jing Liu,Kun Yang,Yan Wang,Dingkang Yang,Xiaoshuai Hao,Wei Zhang,Yang Liu,Wei Zhou
机构: Fudan University (复旦大学); Duke Kunshan University (杜克昆山大学); The University of British Columbia (不列颠哥伦比亚大学); Ant Group (蚂蚁集团); Zhejiang University (浙江大学); East China Normal University (华东师范大学); Fysics AI (物理学人工智能); Xiaomi EV (小米汽车); Singapore Institute of Technology (新加坡科技学院); Tongji University (同济大学); Cardiff University (卡迪夫大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)
备注: submitted to IEEE TCCN
Abstract:Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges. Within distributed network environments, Multimodal Large Language Models (MLLMs) serve as cognitive engines for edge devices, yet federated fine-tuning faces substantial challenges in balancing global knowledge aggregation with local adaptation under heterogeneous network conditions. Conventional federated protocols typically rely on uniform parameter aggregation, which conflates domain-invariant features with client-specific nuances, thereby resulting in suboptimal personalization and excessive communication overhead. To address these challenges, we propose PFAdapter, a communication-efficient framework introducing hierarchical LoRA decomposition to explicitly separate adapter parameters into global-shared and local-private components. Query and key projections are assigned to global synchronization for capturing universal multimodal semantics across the network, while value and output projections remain localized for edge-specific adaptation. Additionally, orthogonality regularization based on the Frobenius norm enforces strict separation between these components, preventing redundant feature learning. Selective aggregation protocols synchronize only global-shared components across the federated network, preserving local expertise and reducing communication costs by nearly 50%. Extensive experiments on VQA-RAD, SLAKE, Hateful Memes, and CrisisMMD datasets demonstrate that PFAdapter consistently outperforms state-of-the-art baselines, achieving accuracy improvements ranging from 2.4% to 4.8% across diverse edge intelligence tasks. Consequently, our framework establishes an efficient solution for agentic AI deployment in resource-constrained communication networks.
[MA-10] CityBehavEx: A Scalable and Empirically Validated LLM -Assisted Urban Simulation Platform
【速读】：该论文旨在解决基于大语言模型（LLM）的多智能体城市仿真系统在可扩展性差、缺乏实证验证以及生成的出行模式与真实世界空间、时间及语义分布不匹配等问题。其核心解决方案在于：摒弃对每个智能体行为都调用大语言模型的做法，转而结合经典人类出行模型与经过微调的跨编码器（cross-encoder），用于量化智能体属性、日程安排与活动转换之间的语义一致性。这一设计显著降低了计算开销，实现了百万级规模智能体（如10万智能体持续75天）在单个消费级GPU上一小时内完成仿真的能力。同时，平台支持用户自定义仿真区域、运行实验、可视化轨迹与活动序列、调试异常行为，并通过真实世界的出行、时间使用和语义指标对生成结果进行实证验证，从而提升了仿真系统的可解释性、可验证性与现实拟合度。
链接: https://arxiv.org/abs/2607.12086
作者: Gustavo H. Santos,Aline Viana,Thiago H Silva
机构: UTFPR(巴西联邦理工大学); Inria(法国国家信息与自动化研究所); University of Toronto(多伦多大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)
备注: 10 pages, 3 figures
Abstract:Recent LLM-based multi-agent urban simulators can generate semantically rich city routines, but they remain costly to scale and are often weakly validated against empirical mobility patterns. We present CityBehavEx, an interactive LLM-assisted urban simulation platform that scales to city-size populations, exposes agent behavior for inspection, supports empirical validation, and generates mobility patterns that better match real-world spatial, temporal, and semantic distributions. Instead of invoking large language models for every agent action, CityBehavEx combines established human mobility models with fine-tuned cross-encoders that estimate semantic alignment between agent profiles, schedules, and activity transitions. This design enables large-scale simulations, as demonstrated in a case study of 100,000 agents over 75 days in under one hour on a single consumer GPU. The platform allows users to define simulation regions, launch experiments, inspect trajectories and activity traces, debug unrealistic behaviors, and validate generated routines against real-world mobility, time-use, and semantic metrics.
[MA-11] Graph Feedback Controls Consensus and Clique Formation in Open-Weight Language-Model Populations
【速读】：该论文旨在解决多智能体语言模型系统中因局部交互导致的共识形成障碍问题，尤其关注在开放权重（open-weight）语言模型群体中，如何通过交互机制促进命名游戏（naming-game）任务下的符号约定（convention formation）与状态空间一致性。其核心挑战在于：尽管运行时的交互图（runtime interaction graph）对智能体间的协同演化至关重要，但传统方法常将其视为实现细节而忽略其结构影响。解决方案的关键在于引入基于分词器安全标签的首词得分限制（restricted first-token scores），从而量化提示条件下的得分状态分布，并构建状态相似性图（state-similarity graph），进而区分采样标签的一致性与潜在状态空间中的共识程度。研究发现，仅依赖同质性阈值相似性路由（homophilous threshold-similarity routing）虽能保留伙伴标签证据，但不足以避免跨盆地（cross-basin）信息隔离，反而加剧碎片化；而具备记忆能力的桥接导向路由（bridge-seeking routing）可有效修复碎片化。在多模型混合种群实验中，基于状态成分和标签分歧的桥接策略在14/18次保留记忆的实验中恢复了最终行为共识，显著优于阈值相似性方法。此外，在同质模型群体中，保留历史记忆通常能推动碎片化动态向稳定共识演进，以Qwen2.5-32B为例，在所有18次保留历史的充分混合设置下均达成行为与最终状态共识，而阈值相似性方法在189次实验中均未实现任何共识形式。该研究还表明，状态阈值、种群规模及词汇量的变化不影响整体定性结论，且早期窗口的图能量特征可作为网格内诊断的有效指标。
链接: https://arxiv.org/abs/2607.12077
作者: Samer Saab Jr,Chaouki Abdallah
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Multi-agent language-model systems increasingly route local interactions, yet the runtime interaction graph is often treated as an implementation detail. We study convention formation in open-weight LM populations spanning 1.1B-32B parameters with a naming-game protocol. Restricted first-token scores over tokenizer-safe labels let us measure prompt-conditioned score-state distributions, construct state-similarity graphs, and separate sampled-label agreement from latent state-space consensus. Across controlled interventions, in the main open-weight repair grids, retained partner-label evidence is necessary but not sufficient: homophilous threshold-similarity routing deletes cross-basin exposure and amplifies fragmentation, while bridge-seeking routing often repairs fragmentation when memory is available. In a three-seed mixed four-model grid, threshold-similarity produces no final behavioral or state consensus in 189 setting-seed runs, whereas state-component and label-disagreement bridges recover final behavioral consensus in 14/18 retained-memory runs. Across homogeneous model populations, retained history generally shifts fragmented dynamics toward consensus; the clearest case is Qwen2.5-32B, which reaches stable behavioral and final state consensus in all 18 retained-history well-mixed settings, while threshold-similarity reaches neither form of consensus in 189 settings. Robustness over state thresholds, population size, and vocabulary size preserves the qualitative ordering, and early-window graph-energy features provide useful within-grid diagnostics.
[MA-12] Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study
【速读】：该论文旨在解决受监管金融机构在数据驻留法规约束下，如何构建由租户所有且可部署于机构内部网络边界的语言模型这一核心问题。其解决方案的关键在于提出并验证一种基于本体（ontology）增强的蒸馏机制：通过监督微调（supervised fine-tuning）与本体引导的直接偏好优化（ontology-grounded direct preference optimization, DPO），将一个大型教师模型（Qwen3.6-27B）的知识高效迁移至本地训练的轻量化学生模型。该过程仅需单个Apple M5 Max设备，在47组合成的跨领域英文偏好对上完成训练，并在40个越南语金融领域任务中实现了90%的任务接地率（grounded rate）和0.95的平均本体术语覆盖率（r_onto），达到与前沿模型GPT-5相当的性能表现。此外，研究还整合了一种用于企业级智能体路由的上下文敏感性审计方法，发现修正后的“默认上下文性”（Contextuality-by-Default）度量值在多个实验组中均为零，表明真正的有效信号源于直接影响力与构念耦合，而非残余上下文性。因此，该工作将本体驱动的模型构建机制与治理诊断框架相结合，为判断模型分歧是否应触发提示标准化、多智能体融合或人工审查提供了决策依据，但未证实其在可部署性、安全性、优越性或统计等效性方面的优势。
链接: https://arxiv.org/abs/2607.11948
作者: Thanh Luong Tuan
机构: Foundation AgenticOS (FAOS)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 15 pages, 2 figures. Combined proof-of-mechanism and negative-results method article consolidating ontology-amplified distillation with contextuality-audit routing for enterprise agents
Abstract:Regulated financial institutions operating under data-residency rules need tenant-owned language models that can run inside the institution’s perimeter. This paper combines two related FAOS studies into one mechanism-and-control article. First, it reports a reduced-power proof-of-mechanism study of ontology-amplified distillation: a Qwen3.6-27B student is adapted to the Foundation AgenticOS ontology through supervised fine-tuning on frontier-teacher trajectories and ontology-grounded direct preference optimization (DPO), trained locally on a single Apple M5 Max from 47 synthetic, English-language, cross-domain preference pairs. On 40 held-out Vietnamese financial-domain tasks, the distilled student grounds 36 of 40 tasks (grounded rate 0.90; mean ontology term-coverage r_onto = 0.95 on a metric floored at 0.50), equal to the GPT-5 frontier baseline, which also grounds 36 of 40. The outcome is underpowered to establish equivalence: the paired-difference 95% confidence interval spans +/-4 tasks, and the run does not test or show the pre-registered amplification prediction that the student should exceed the frontier. Second, the paper consolidates a contextuality-audit method for enterprise-agent routing. In a separate negative-results pilot, the corrected canonical Contextuality-by-Default degree is zero for all Phase 1.3 groups in both the local-Qwen run and an explicitly labeled Gemma replication check; the useful signal is direct influence and construct coupling, not surviving residual contextuality. Together, the studies pair an ontology-grounded model-building mechanism with a governance diagnostic for deciding when apparent disagreement should trigger prompt standardization, multi-agent synthesis, or human review. The evidence supports neither deployability, safety, superiority, statistical equivalence, nor a contextuality-positive routing rule.
[MA-13] AgentS ociety 2: An Integrated Research Environment for Executable Social Science
【速读】：该论文旨在解决当前人工智能科学家系统在社会科学领域中存在的研究流程与模拟社会系统相脱节的核心问题。现有系统或仅辅助单一研究任务，或仅将代理（agent）作为实验受试者进行模拟，导致研究工作流与社会模拟环境之间缺乏有机整合。其解决方案的关键在于提出AgentSociety 2——一个可执行的社会科学集成研究环境，通过在同一运行时中耦合两类大语言模型（LLM）代理的双重角色：一是作为“AI社会科学家”的代理，负责文献溯源、假设生成、实验设计、仿真执行、结果解释及论文撰写等全流程协调；二是作为“硅基参与者”的代理，在可配置的社会环境中生成行为响应。这种双角色协同机制将研究假设转化为可审计的代理行为、环境规则、干预措施与测量指标，实现了从理论到仿真再到分析的端到端可执行研究工作流。该框架在微观层面实验室实验、中观层面社交媒体动态及宏观层面城市场景等七项示范研究中，成功支持了多样化学科问题，复现了既有研究中的关键定性模式，识别出有意义的偏差，并通过优化代理-环境交互实现了大规模仿真。该系统在保持人类研究人员高层次控制权的同时，将程序性编排交由智能体系统完成，构建了一个具备人机协同、可控性强的下一代计算社会科学基础设施，为可扩展的计算社会实验和人工智能赋能的社会治理平台提供了通用范式。
链接: https://arxiv.org/abs/2607.11895
作者: Jinghua Piao,Jun Zhang,Haoyu Huang,Keming Zhang,Jing Yi Wang,Songwei Li,Boyuan Sun,Jiayi Chang,Fengli Xu,Chunyan Wang,Fang Zhang,Ke Rong,Jun Su,Tianguang Meng,Yi Liu,Qingguo Meng,Yu Wang,Yong Li
机构: 未知
类目: Computers and Society (cs.CY); Multiagent Systems (cs.MA)
备注:
Abstract:AI scientist systems are beginning to automate parts of scientific research, but social science poses a distinct challenge: its objects of inquiry are not merely datasets or laboratory protocols, but integrated social processes involving situated participants, interaction contexts, interventions, and outcomes. Yet a critical link is missing: existing systems either assist isolated research tasks or simulate agents as experimental subjects, leaving the research workflow and simulated society decoupled. Here we introduce AgentSociety 2, an Integrated Research Environment for executable social science. It couples two roles of LLM agents in the same runtime: AI social scientists that coordinate literature grounding, hypothesis generation, experiment design, simulation execution, result interpretation, and manuscript drafting; and silicon participants that generate behavioral responses within configurable social environments. This dual-role design turns hypotheses into auditable agent behaviors, environment rules, interventions, and measurements, thereby supporting an end-to-end workflow. Across seven illustrative studies spanning micro-level social-science laboratory experiments, meso-level dynamics in social media, and macro-level urban scenarios, we demonstrate its capacity to support diverse disciplinary questions, reproduce major qualitative patterns from prior studies, identify informative deviations, and enable large-scale simulations through optimized agent-environment interactions. By preserving human researchers’ high-level agency while delegating procedural orchestration to agentic systems, it provides a human-in-the-loop and controllable infrastructure for next-generation computational social science, with broader applications in scalable computational social experimentation and AI-enabled social governance platforms.
[MA-14] Self-Healing Coordination in Cognitive Swarm Agents with Bloch-Type Perceptual Memory
【速读】：该论文旨在解决传统反应式群集模型在遭遇干扰后恢复协调能力有限的问题，其核心挑战在于如何通过代理的内部感知状态实现高效的自愈性协同。现有模型通常将局部观测直接映射为运动行为，缺乏对历史信息的动态整合机制，导致群集在遭遇障碍物或发生分裂后难以快速重建空间连通性。为此，研究提出基于非马尔可夫集体运动模型的布洛赫型慢-快架构（Bloch-type slow-fast architecture），其中每个智能体配备一个受限的布洛赫型感知寄存器，与一个缓慢调节的状态耦合，构成闭环的慢-快动力学循环。该架构的关键创新在于：将感知记忆作为操作性工具，用于在闭环中实现依赖历史的线索解析，而非作为独立的记忆存储；布洛赫更新规则虽名为“布洛赫”，但仅表示一种保持正性的有效内部感知演化动力学，并非微观量子机制。在包含有限速度、转向约束、避障、高度调控及固定迁徙驱动力的非周期性障碍密集无人机迁徙任务中，多种子消融实验对比了完整慢-快架构与无记忆及部分反馈基线模型。结果表明，该架构显著提升了自愈性能：在障碍物引发群集分裂后，闭环慢-快回路能加速空间连通性的恢复，而解耦的慢态则表现如同无记忆控制器，无法实现有效协同重建。
链接: https://arxiv.org/abs/2607.11960
作者: Jyotiranjan Beuria
机构: IKS Research Center, ISS Delhi(印度科学研究所新德里分校)
类目: Adaptation and Self-Organizing Systems (nlin.AO); Soft Condensed Matter (cond-mat.soft); Multiagent Systems (cs.MA); Biological Physics (physics.bio-ph)
备注: Under review at IEEE Transactions on Cognitive and Developmental Systems
Abstract:Reactive flocking models usually map current local observations directly to motion, leaving limited room for internal perceptual state to shape recovery after disruption. Building on a non-Markovian collective-motion model based on self-regulated perceptual dynamics, we ask whether the Bloch-type slow-fast architecture can support self-healing coordination in cognitive swarm agents. Each agent carries a bounded Bloch-type perceptual register coupled to a slow regulatory state. The slow state is not treated as a standalone memory store; here, perceptual memory is used operationally to denote history-dependent cue resolution within the closed slow-fast loop. The Bloch update is a positivity-preserving effective dynamics for internal perceptual alternatives, not a microscopic quantum claim. We evaluate the architecture in a non-periodic, obstacle-rich drone migration task with finite speed, bounded turning, collision avoidance, altitude regulation, and a fixed migratory drive. Multi-seed ablations compare the full slow-fast architecture with memoryless and partial-feedback baselines using recovery time, largest-cluster restoration, polar order, local coherence, collision risk, and path efficiency. Results show that the main functional impact is on self-healing: after obstacle-induced fragmentation, the closed slow-fast loop accelerates restoration of spatial connectedness, whereas an uncoupled slow trace behaves like a memoryless controller.
自然语言处理
[NLP-0] Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在执行多步骤工程与信息学任务时缺乏对任务执行范围的感知能力的问题。现有方法普遍采用“最大上下文优先”策略，即反复读取已处理过的文件和依赖项，导致微小修改演变为大规模代码审计，造成显著的计算冗余与资源浪费。其核心问题在于：代理无法准确评估任务难度、所需信息的真实边界以及最短可靠执行路径，从而难以合理分配计算预算。为此，论文提出关键解决方案——E3（Estimate, Execute, Expand）框架，通过三阶段机制实现任务感知的执行范围估计：首先估算初始操作点，随后执行最小可行路径，仅在验证失败时才扩展作用域。该方法引入“最小充分执行”形式化定义及代理认知冗余比（Agent Cognitive Redundancy Ratio, ACRR）作为评估指标。在MSE-Bench这一可控仿真环境下的121个确定性代码修改任务中，E3在保持100%成功率的同时，将成本降低85%，令牌消耗减少91%，检查文件数下降92%，并优于强基线自适应检索方法16%；且性能优势在不同指令表述和成本权重下均具鲁棒性。真实模型验证（LLM-Case）进一步证实，即使在GPT-4o代理对开源库的实际编辑场景中，过度读取现象依然存在，而E3仍为最轻量、最快捷的策略，仅因服务商速率限制存在一次失败，未出现错误编辑。研究将此视为对执行冗余的受控探查，而非对实际部署代理的测量，并将任务感知执行定位为迈向工程接地人工智能（Engineering-grounded AI, EGAI）的关键一步——即让代理的努力程度锚定于任务的工程现实。相关框架与基准数据集已公开发布。
链接: https://arxiv.org/abs/2607.13034
作者: Junjie Yin,Xinyu Feng
机构: University of Tennessee, Knoxville (田纳西大学诺克斯维尔分校); University of Tennessee Space Institute (田纳西大学空间研究所)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE); Systems and Control (eess.SY)
备注: 27 pages, 8 figures, 8 tables. Code and benchmark: this https URL
Abstract:Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy–re-reading files and dependencies they have already seen–turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task’s difficulty, the information it truly needs, and the shortest reliable path before committing budget. We formalize minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), and propose E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails. On MSE-Bench–a deterministic benchmark of 121 edits in a capability-controlled simulator–E3 matches the strongest baseline’s 100% success while cutting cost by 85%, tokens by 91%, and inspected files by 92%, and further beats a strong adaptive retrieval baseline by 16%; the gains survive held-out instruction wording and essentially every cost weighting. A companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library, with every candidate patch graded by actually running the project’s real pytest suite against a measured oracle: the over-reading is milder but real, and E3 is the leanest and fastest policy at comparable task success–its one shortfall a provider rate-limit, not a wrong edit. We frame this as a controlled probe of execution redundancy, not a measurement of any deployed agent, and position task-aware execution as a step toward engineering-grounded AI (EGAI)–agents whose effort is anchored in the engineering reality of the task. We release the framework and benchmark.
[NLP-1] PalmClaw: A Native On-Device Agent Framework for Mobile Phones
【速读】： 该论文旨在解决移动设备上智能体（agent）执行多步任务时面临的效率与可控性问题。现有移动代理主要依赖图形用户界面（GUI）操作（如点击、滑动、输入），此类方法通常形成冗长且高度依赖界面的指令序列，无法直接访问设备底层能力，且难以明确定义任务执行边界。为此，论文提出PalmClaw——一个开源的原生移动端智能体框架，其核心创新在于将设备功能抽象为具有明确参数、结构化输出和清晰执行边界的“设备工具”（device tools），使智能体可在本地直接调用手机硬件与系统能力，同时保持每一步操作的可解释性与可控性。该设计显著提升了任务成功率（相对基线提升11.5%）并大幅缩短完成时间（减少94.9%），同时降低部署复杂度，并通过实验日志验证了执行边界的合理应用。
链接: https://arxiv.org/abs/2607.13027
作者: Hongru Cai,Yongqi Li,Ran Wei,Wenjie Li
机构: The Hong Kong Polytechnic University (香港理工大学); Hangzhou Diagens Biotechnology Co., Ltd. (杭州迪根斯生物科技有限公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action. Most agent systems run on desktops or servers, which support tool use and task automation. Mobile devices are also important agent environments because they are widely accessible and contain users’ data, sensors, and daily-use applications. Existing mobile agents mainly operate smartphones through graphical user interface (GUI) actions such as tapping, swiping, and typing, which often form long, interface-dependent sequences, cannot directly access device capabilities, and make execution boundaries difficult to define. We present \textbfPalmClaw, an open-source agent framework that runs natively on mobile phones and manages the sessions, memory, skills, tools, and agent loop directly on the device. PalmClaw exposes device capabilities as device tools with explicit arguments, structured results, and clearly defined execution boundaries. This design enables agents to use mobile capabilities directly while keeping each action explicit and controlled. Experiments show an 11.5% relative improvement in task success and a 94.9% reduction in completion time over the strongest baseline, with lower setup burden and traces illustrating how execution boundaries are applied. Code is available at this https URL.
[NLP-2] he Illusion of Robustness: Aggregate Accuracy Hides Prediction Flips under Task-Irrelevant Context
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在复杂上下文环境中部署时面临的可靠性问题，尤其关注任务无关上下文对模型预测稳定性的影响。尽管现有模型在整体性能上表现出对冗余上下文的鲁棒性（即平均准确率变化较小），但这种聚合层面的稳定掩盖了个体样本层面的显著不稳定性。研究发现，即使引入语义上毫无意义的伪词（pseudo-words，由随机字符组合而成），也会在少数样本上引发模型预测的剧烈波动，导致部分样本性能下降而另一些样本反而提升，呈现出双向影响效应。这一现象在多种模型架构与数据集上均具有一致性，但受影响的样本具有高度模型特异性。此外，该不稳定性受上下文类型、长度、推理时计算资源及模型演进阶段的调节。因此，论文的核心解决方案在于揭示“由上下文诱发的尾部风险”（context-induced tail risks），并强调必须从基于聚合指标的评估转向以单个样本为单位的可靠性评估，以更真实地反映模型在实际应用中的行为一致性与可信度。
链接: https://arxiv.org/abs/2607.12963
作者: Yanzhe Zhang,Sanmi Koyejo,Diyi Yang
机构: Georgia Tech (佐治亚理工学院); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL)
备注: Preprint
Abstract:As large language models (LLMs) grow more capable, they are increasingly deployed in context-rich settings where task inputs are often accompanied by long, partially irrelevant context. In a controlled setting, we find that state-of-the-art models often appear robust to task-irrelevant context at the aggregate level: prepending it to benchmark questions causes little change in overall accuracy. This aggregate stability, however, masks significant per-example instability. Even semantically meaningless pseudo-words, formed by randomly combining characters, can markedly shift model predictions on a small fraction of examples, degrading performance on some while improving it on others. This two-sided effect holds consistently across a wide range of models and datasets, yet the affected examples are largely model-specific. We further show that this instability is modulated by context type, context length, test-time compute, and model development stage. Together, our findings reveal context-induced tail risks concealed by aggregate accuracy, motivating per-example reliability evaluation of language models.
[NLP-3] MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
【速读】： 该论文旨在解决大语言模型（LLM）在长期多轮交互中对长时记忆（long-term memory）评估的局限性问题。现有基准测试主要依赖下游问答任务中的最终答案正确率来衡量记忆能力，这种“黑箱式”评估方法无法区分记忆失败的多种异质原因，如关键事实未被引入、操作绑定错误目标或依赖已修正的过时值等，导致系统可能因不一致或不安全的记忆状态而获得正确答案却仍被误判为有效。为此，论文提出将长时记忆视为一系列显式操作构成的生命周期，包括记忆（remembering）、遗忘（forgetting）、更新（updating）、反思（reflecting）及其组合，并构建了名为MemOps的新型基准。其核心创新在于将每个记忆事件结构化为包含触发条件、作用目标、作用范围、状态转移及支持证据的完整操作轨迹（operation trace），并通过可控生成流程将这些操作嵌入任务导向型对话中，生成带有金标准操作轨迹及六类操作级探针的数据集。该基准在邻近证据与长上下文设置下进行评估，揭示了当前基于检索、参数化和管理式内存系统在重构有序记忆状态轨迹方面的显著不足，例如会话级检索优于回合级检索，且长上下文模型在记忆状态演化序列重建方面依然薄弱。因此，该研究推动了长时记忆评估从单一最终答案准确率向可解释、操作级诊断的范式转变。
链接: https://arxiv.org/abs/2607.12893
作者: Xixuan Hao,Zeyu Zhang,Zehao Lin,Yihang Sun,Ziliang Guo,Xichong Zhang,Yuxuan Liang,Feiyu Xiong,Zhiyu Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions. Existing benchmarks, however, evaluate such memory almost exclusively through downstream question answering, scoring only the correctness of a final answer. This black-box formulation conflates the heterogeneous causes of memory failure, such as missing the introduction of a relevant fact, binding an operation to the wrong target, or relying on stale values after a correction. As a result, it can credit correct answers despite their reliance on inconsistent or unsafe memory states. In this paper, we argue that, in dynamic long-horizon interactions, memory is not a static collection of facts but a lifecycle of explicit operations, including remembering, forgetting, updating, reflecting, and their compositions. We introduce MemOps, a benchmark that reformulates conversational memory as a sequence of lifecycle operations and represents each memory event with a structured trace specifying its trigger, target, scope, state transition, and supporting evidence. A controllable generation pipeline embeds these operations into long, task-oriented conversations and produces gold operation traces together with six categories of operation-level probes, evaluated under both adjacent-evidence and long-context settings. Across long-context, retrieval-based, parametric and managed-memory systems, MemOps disentangles failure modes that final-answer accuracy alone conceals, revealing that current systems remain far from uniformly reliable. For instance, session-level retrieval outperforms turn-level retrieval, and long-context models remain notably weak at reconstructing ordered memory-state trajectories. These results move long-term memory evaluation from final-answer scoring toward interpretable, operation-level diagnosis.
[NLP-4] LLM Judges Can Be Too Generous When There Is No Reference Answer
【速读】： 该论文旨在解决生成式 AI 模型在无参考（no-reference）评估场景下，依赖大语言模型（LLM）作为评判者时的可靠性问题。在缺乏标准答案的情况下，现有研究普遍假设 LLM 判定器能够独立、客观地评估开放性生成结果，但本文通过双阶段方法揭示了其潜在偏差：首先进行校准实验，评估裁判模型对任务的理解程度；其次开展敏感性实验，考察参考答案在提示词中的存在与否及其位置对裁判决策的影响。实验覆盖三种语言，结果显示，在无参考答案时，裁判模型倾向于过度认可错误答案；而当提示中引入参考答案信息后，其正确/错误判断的决策方向可能发生高达85%的逆转。与部分人工标注结果对比表明，这种由参考答案驱动的变化趋势与人类判断具有一致性。因此，论文提出的关键解决方案是：在将 LLM 判定器应用于无参考评估前，必须使用包含参考答案感知评估的样本对其进行校准，以确保其评估行为的稳健性和可信度。该研究为其他任务中 LLM 判定器的校准提供了可复用的方法论框架。
链接: https://arxiv.org/abs/2607.12885
作者: Chalamalasetti Kranti,Sowmya Vajjala
机构: University of Potsdam (波茨坦大学); National Research Council (加拿大国家研究委员会)
类目: Computation and Language (cs.CL)
备注: Preprint
Abstract:LLM judges are increasingly being used to evaluate open-ended model responses, often in no-reference settings where a ground-truth answer is unavailable. However, can they reliably assess in such evaluation setups? We explore this question in this paper through a two stage pipeline with a) calibration experiments that assess the judge model’s knowledge of the task it is evaluating, and b) sensitivity experiments that assess how the judge model’s performance is impacted by the presence and positioning of the reference answer in the prompt. Across experiments covering three languages, we show that the judge models we evaluated tend to over-credit incorrect answers in the absence of a reference answer, and adding reference answer information to the prompt flips the judge model’s correct/incorrect decisions by as much as 85% in some experimental settings. Comparison with a subset of human annotations shows that these reference-driven changes generally align with human judgments. Our results emphasize the need for calibrating the LLM judges with a sample with reference-aware evaluation before using them in reference-free setups reliably, and our methodology provides a blueprint for researchers and practitioners in doing such calibration of LLM judges for other tasks.
[NLP-5] Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversations
【速读】： 该论文旨在解决大语言模型（LLM）在多轮医疗对话中识别并纠正患者隐含错误信念（misconception）的能力不足问题。当前评估框架未能有效捕捉模型在复杂、动态的对话过程中对谬误的持续演化与传播行为，导致对模型安全性和可靠性评估存在盲区。其解决方案的关键在于构建一个真实世界来源的多轮医疗对话数据集——ThReadMed-QA，包含2,437条患者-医生对话线程及8,204个问答对，覆盖真实临床交互场景；并采用基于评分标准的“大语言模型作为裁判”（LLM-as-a-Judge）评估范式，系统评估五种前沿模型在多轮情境下检测和纠正错误信念的表现。研究发现，尽管先进模型在单轮交互中可有效纠正谬误（如GPT-5与Claude-Haiku初始正确率约85%），但在后续对话轮次中性能显著下降至约50%，主要归因于错误传播（error propagation）。即使使用真实医生回复替换中间输出以提供正确上下文，模型表现仍不理想，表明其内在推理稳定性不足。这一结果揭示了现有模型在长期对话中存在一致性差、潜在误导风险高的缺陷，凸显了建立能够刻画多轮认知演化的评估体系的紧迫性。
链接: https://arxiv.org/abs/2607.12884
作者: Monica Munnangi,Saiph Savage
机构: Northeastern University(东北大学)
类目: Computation and Language (cs.CL)
备注: Accepted to MLHC 2026
Abstract:Patients seeking medical information often ask questions that embed incorrect assumptions or misconceptions. In such cases, safe medical communication requires not only answering the question, but identifying and correcting the underlying false belief. These interactions naturally unfold over multiple turns, a pattern now mirrored in interactions with LLMs. Yet current evaluation frameworks do not capture model behavior in these settings, where misconceptions can emerge, persist, or evolve over the course of a conversation. Whether LLMs can reliably correct such misconceptions over time remains largely unexamined. To study this, we introduce ThReadMed-QA, a multi-turn medical dialogue dataset of 2,437 patient-physician conversation threads comprising 8,204 question-answer pairs, derived from real patient interactions on AskDocs. This dataset enables systematic evaluation of whether models can detect and correct misconceptions under a multi-turn context. We evaluate five LLMs using a rubric-based LLM-as-a-Judge framework that scores responses based on their ability to identify and correct misconceptions. Our experiments reveal a consistent pattern: even frontier models that can address misconceptions in a single interaction degrade substantially over subsequent turns. GPT-5 and Claude-Haiku correct these false presuppositions around 85% on initial questions but drop to roughly 50% within two follow-ups. An oracle analysis replacing prior model outputs with physician responses shows that much of the degradation is driven by error propagation, while performance remains imperfect even under correct context. Even when models tend to correct misconceptions initially, their performance degrades substantially over later turns, leading to inconsistent and potentially unsafe guidance in patient-facing settings and highlighting the need for evaluation frameworks that capture multi-turn behavior.
[NLP-6] Can LLM s Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction
【速读】： 该论文旨在解决基于大语言模型（LLM）的研究代理在论文复现任务中，因依赖人工构建的特定论文评分标准（rubric）而导致评估可扩展性受限的问题。现有方法如PaperBench需大量专家投入以定制化生成每篇论文的评分细则，严重制约了大规模基准测试的发展。为此，本文首次系统性地对LLM自动生成的评分标准进行了元评估（meta-evaluation），将原本复杂的评分标准重构为检查清单式（checklist-style）结构，并在两种骨干模型上对比四种生成设置。研究通过内在评估（语义相似度）与外在评估（与真实评分标准的得分一致性）双重视角验证生成评分标准的有效性。结果表明，增强型生成设置显著提升了下游评估的一致性，最优条件下已接近人类基准表现，而内在质量提升相对有限。进一步分析揭示，LLM生成的评分标准普遍存在粒度过细、倾向于高分偏差以及对论文领域适应性不足等局限，反映出生成式评分标准在实用性与可靠性方面的双重潜力与挑战。
链接: https://arxiv.org/abs/2607.12835
作者: Hanhua Hong,Yizhi Li,Jiaoyan Chen,Luu Gia Huy,Sophia Ananiadou,Jung-jae Kim,Chenghua Lin
机构: The University of Manchester(曼彻斯特大学); Institute for Infocomm Research (I²R), ASTAR(资讯通信研究院，ASTAR); IQuest Research(伊奎斯特研究); ELLIS Manchester(ELLIS曼彻斯特); University of Information Technology, VNU(信息科技大学，越南国家大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Rubric-based evaluation is a promising approach for assessing open-ended outputs from LLM-based research agents, particularly in paper reproduction, where direct paper-to-repository comparison is prone to hallucination. However, constructing paper-specific rubrics requires substantial expert effort, limiting the scalability of benchmarks such as PaperBench. In this work, we present, to our knowledge, the first systematic meta-evaluation of LLM-generated rubrics for paper reproduction. We reformulate rubrics into a checklist-style format and evaluate four generation settings across two backbone models. We meta-evaluate generated rubrics intrinsically by semantic similarity and extrinsically by score alignment with ground-truth rubrics. Our results show that the augmented settings substantially improves downstream evaluation alignment, with the strongest setting approaching the human baseline, while intrinsic gains are more modest. Further analyses reveal that LLM-generated rubrics are often overly fine-grained, biased toward high scores, and less adaptive to paper domains, highlighting both the affordances and limitations.
[NLP-7] Knowledgeless Language Models: Suppressing Parametric Recall for Evidence-Grounded Language Modeling
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对过时、不完整或与上下文不符的事实知识时，因过度依赖参数化记忆（parametric recall）而导致的不可靠行为问题。其核心解决方案是提出一种全新的认知训练范式——无知识语言模型（Knowledge-Less Language Models, KLLMs），通过在预训练阶段使用命名实体被匿名化的语料库进行训练，从而消除基于实体的事实监督信号这一主要知识获取渠道。这一干预显著降低了模型在闭卷情境下的事实回忆能力，但在提供外部上下文信息的任务中表现更优，尤其在需要证据支撑的问答、事实验证和幻觉检测等任务上，相较于基准模型表现出一致且显著的优势。在检索增强设置下，当证据存在不完整性时，KLLMs展现出更强的鲁棒性，相对提升可达20%–25%。此外，模型校准性能也得到改善，表现为更低的期望校准误差（ECE）、更优的Brier评分和更高的AUROC，同时具备更可靠的拒答行为。研究结果表明，通过预训练阶段对知识获取的控制，能够引导模型从依赖内部参数化知识转向更依赖外部证据的推理模式，从而在现实场景中提升可靠性。这一方法为补充检索增强生成（Retrieval-Augmented Generation, RAG）和工具调用系统提供了更具证据敏感性的基础模型构建路径。
链接: https://arxiv.org/abs/2607.12831
作者: Roi Cohen,Yvan Carré,Nick Lechtenbörger,Hendrik Droste,Lucas Kerschke,Russa Biswas,Gerard de Melo,Jan Buys
机构: HPI / University of Potsdam (HPI/波茨坦大学); African Institute for Mathematical Sciences (非洲数学科学研究所); Aalborg University, Copenhagen, Denmark (奥尔堡大学，哥本哈根，丹麦); Polytechnique Montréal (蒙特利尔理工学院); University of Cape Town (开普敦大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Language models encode substantial factual knowledge in their parameters, which can lead to unreliable behavior when this knowledge is outdated, incomplete, or misaligned with the provided context. In this work, we study whether modifying the pretraining signal can systematically shift models away from parametric recall and toward evidence-grounded reasoning. We introduce Knowledge–‘‘Less’’ Language Models (KLLMs), a fundamentally different epistemic training paradigm for LLMs, which are pretrained on corpora in which named entities are anonymized, thereby removing a primary channel for entity-linked factual supervision. This intervention substantially reduces closed-book factual recall, while often improving performance on tasks where relevant information is provided as context. Across multiple model scales, KLLMs consistently outperform matched baselines on contextual question answering, fact verification, and hallucination detection benchmarks. Crucially, in retrieval-grounded settings with imperfect evidence, KLLMs show improved robustness and achieve up to 20–25% relative gains over standard language models. They further exhibit better calibration, with improved ECE, Brier score, and AUROC, as well as more reliable abstention behavior. Our results demonstrate that suppressing entity-linked supervision during pretraining induces a shift in epistemic behavior: KLLMs rely less on parametric knowledge and more on external evidence, leading to improved reliability under realistic conditions. This suggests that pretraining-time control over knowledge acquisition can complement retrieval-augmented and tool-based systems by providing a more evidence-sensitive base model.
[NLP-8] Accelerating Masked Diffusion Large Language Models : A Survey of Efficient Inference Techniques IJCAI ECAI2026
【速读】： 该论文旨在解决扩散型大语言模型（diffusion large language models, dLLMs）在实际部署中推理效率不足的问题，尤其针对其虽具备并行生成的理论优势却难以实现实际速度提升的瓶颈。其核心挑战在于现有基准测试未能有效区分算法、架构与系统层面因素之间的复杂权衡关系，导致加速技术的评估缺乏可比性与可复现性。解决方案的关键在于提出一个统一的延迟分解框架（latency decomposition framework），将端到端延迟解耦为可量化的算法、架构与系统级影响因子，从而清晰揭示各因素对推理速度的实际贡献。基于此框架，研究系统性地将加速技术划分为三大维度：算法创新、架构与系统优化、以及推理时扩展策略，并进一步提供可复现的基准测试指南，以推动dLLMs在真实场景中充分发挥并行生成潜力。
链接: https://arxiv.org/abs/2607.12829
作者: Daehoon Gwak,Minhyung Lee,Junwoo Park,Jaegul Choo
机构: KAIST AI(韩国科学技术院人工智能中心); Yonsei University(延世大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at IJCAI-ECAI 2026 (Survey Track)
Abstract:Diffusion large language models (dLLMs) offer a theoretical advantage in parallel generation over standard autoregressive models. However, parallel generation alone does not guarantee practical speedups. Realizing this efficiency requires specialized inference mechanisms, such as diffusion-aware caching and reuse. Consequently, as inference efficiency becomes a prerequisite for practical deployment, recent research has actively explored acceleration techniques across algorithms, architectures, and systems. However, rigorous comparisons remain difficult, as end-to-end latency stems from intricate trade-offs between algorithmic, architectural, and system-level factors that are often conflated in existing benchmarks. In this survey, we introduce a unified latency decomposition framework for dLLMs to disentangle these factors and analyze their impact on inference speed in real deployments. Guided by this framework, we categorize acceleration techniques along three axes covering algorithmic innovations, architectural and system optimizations, and inference-time scaling. Finally, we provide guidelines for reproducible benchmarking and highlight open challenges for realizing the full potential of parallel generation.
[NLP-9] he One-Word Census: Answer-Choice Conformity Across 44 Language Models
【速读】： 该论文旨在解决大语言模型在面对大量语义上均有效的多选一问题时，其答案选择行为的可预测性与一致性问题，特别是探究不同模型在相同提示下是否趋向于选择相同的答案，以及这种趋同现象背后的结构特征。其核心问题是：当模型被要求从众多合理选项中“任意”选择一个答案时，它们的选择是否存在系统性偏差？解决方案的关键在于提出并应用一种简洁、可复现的评估工具——“单字普查”（One-Word Census），即通过31个单一回合的类别提示（如“命名一种树”），每个模型在无系统提示条件下执行四次测试，仅以归一化词元的精确匹配进行分析，不依赖嵌入或人工评判，成本约为每模型1美元。该方法揭示出模型间存在极端收敛现象——在31个类别中有7个，单一答案占据超过80%的输出；但模型间的符合程度差异超过四倍，且呈现结构性分布：经过人格化或社区调优的模型最不一致，而最新一代主流旗舰模型（如Claude、GPT）表现出极高的同质性，几乎不产生其他模型未给出的答案。进一步分析发现，在四大模型家族（Claude、GPT、Qwen、Grok）中，符合度随代际演进呈上升趋势，但最新旗舰版出现反常回落，可能预示顶级模型战略定位的调整。研究还表明，该结果对模型组合变化具有高度鲁棒性（剔除一族后相关系数ρ=0.985），且在18/20共享类别中，模型群体的集中度显著高于人类表现。该工作贡献的核心不仅在于观测到的收敛现象，更在于提供了一套轻量、透明、可公开复现的评估框架，为理解生成式AI（Generative AI）内部决策结构提供了新视角。
链接: https://arxiv.org/abs/2607.12796
作者: Tapan Parikh
机构: Cornell Tech (康奈尔科技)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 19 pages, 5 figures. Data, prompts, and code: this https URL
Abstract:When a language model must pick one answer from a large space of equally valid options, which does it pick – and how often is it the same answer every other model picks? Asked to “pick a word – any word,” 44 models chose “serendipity” 41% of the time. We characterize this convergence with a deliberately minimal instrument: 31 single-turn prompts, each naming a category with many valid one-word answers (“Name a tree.”), asked four times per model with no system prompt. Analysis is exact-match on normalized tokens – no embeddings, no judge – at about a dollar per model. That models converge is well documented; our contribution is the instrument itself – the One-Word Census – and what it reveals about the structure of the convergence. We score each model by answer-choice surprisal: the average -\log2 probability of its answers under the pooled answers of all other models, leave-one-out. Convergence is extreme – in 7 of 31 categories one answer takes over 80% of all answers – yet conformity varies more than fourfold across models, and the variation is structured. Persona- and community-tuned models are the most divergent; the newest mainline flagships are the most conformist, producing almost no answer no other model gave. Within four lineages (Claude, GPT, Qwen, Grok) conformity rises with each generation – but reverses for the latest flagship Claude and GPT models, a possible early signal of repositioning at the top tier. Rankings are robust to roster composition (leave-one-family-out rho = 0.985). Against human category-production norms, the field is more concentrated than people in 18 of 20 shared categories. All prompts, transcripts, and code are public.
[NLP-10] Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters? ACM-MM2026
【速读】： 该论文旨在解决多模态情感识别（Multimodal Emotion Recognition, MER）中大型模型参数量过大导致计算成本高、推理效率低的问题，特别是在资源受限的平台（如机器人和移动设备）上难以实现实时部署。其核心挑战在于：是否必须依赖超过10亿参数的大型多模态模型才能实现高质量的情感识别？为回应这一问题，论文提出了一种轻量化多模态情感识别框架Light-MER，其关键在于通过知识蒸馏（Knowledge Distillation）技术，将大体量教师模型中的丰富多模态情感推理能力高效迁移至参数量低于10亿的轻量级学生模型中，从而在保持甚至超越现有性能的同时显著提升推理效率。其解决方案的核心创新包括：(1) 一种结合切片沃尔什距离（Sliced Wasserstein Distance）与隐藏状态对齐的最优传输损失函数，以更精准地捕捉多模态特征间的复杂分布关系；(2) 一种基于GRPO的多奖励优化策略，用于在情感识别性能与推理效率之间实现动态平衡，进一步增强学生模型的学习能力。实验在九个基准数据集上的结果表明，Light-MER不仅达到当前最优性能，且推理速度大幅提升，验证了小型多模态情感语言模型在未来研究中的巨大潜力。
链接: https://arxiv.org/abs/2607.12787
作者: Kaiwen Zheng,Junchen Fu,Wenhao Deng,Hu Han,Joemon M. Jose,Xuri Ge
机构: University of Glasgow(格拉斯哥大学); Institute of Computing Technology, Chinese Academy of Sciences(中国科学院计算技术研究所); School of Artificial Intelligence, Shandong University(山东大学人工智能学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Accepted by ACM MM2026
Abstract:Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc. However, these performance improvements are often accompanied by an increase in model parameter size (e.g, at least 7B), which simultaneously incurs high computational costs and reduces inference efficiency, thereby hindering real-time deployment on resource-constrained platforms such as robots and mobile devices. This raises a fundamental question: do we really need the multimodal MER model larger than 1B parameters for high-quality MER? In this paper, we challenge the assumption that larger models are inherently necessary and proposes a lightweight MER framework (called Light-MER), which achieves better and faster multimodal sentiment understanding and recognition through knowledge distillation. It can transfer knowledge from a strong, large-scale teacher model to a lightweight sub-billion-parameter student model, aiming to preserve rich multimodal emotion reasoning and recognition while substantially improving deployment efficiency. Specifically, we introduce two new optimization strategies to enhance knowledge transfer: (1) a new optimal transport loss that combines Sliced Wasserstein Distance with hidden-state alignment, and (2) a new multi-reward optimization strategy based on GRPO that balances MER performance and efficiency, aimed at further enhancing the learning capabilities of student models. Extensive experiments on nine benchmark datasets demonstrate that Light-MER achieves state-of-the-art performance while significantly improving inference efficiency. This highlights the strong potential of small multimodal emotion language models for future research. Code is available at this https URL. Comments: Accepted by ACM MM2026 Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM) Cite as: arXiv:2607.12787 [cs.AI] (or arXiv:2607.12787v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.12787 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-11] Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models
【速读】： 该论文旨在解决当前大语言模型（LLM）在化学反应机理推理方面存在的根本性问题：现有化学领域大模型多聚焦于粗粒度的命名反应进行产物预测与逆合成分析，导致物理上不一致及幻觉现象频发；而专门用于机理推断的小规模生成模型则普遍存在跨化学空间泛化能力不足的缺陷。其解决方案的关键在于构建一个大规模、精细化的反应机理推理数据集，并建立基于《福山高级有机反应机理》（Fukuyama’s Advanced Organic Reaction Mechanism）的严格评估基准——FukuyamaBench，以系统性地检验模型在层级化机理推理任务中的表现。通过在该数据集上对Qwen3-30B-A3B进行机制感知微调，模型在FukuyamaBench Set A上实现了8.3%的精确路径匹配率，显著优于专用模型FlowER的5.1%，证明了机制感知训练能有效提升语言模型的化学推理能力。
链接: https://arxiv.org/abs/2607.12771
作者: Xingyu Dang,Haocheng Tang,Junmei Wang,Yanjun Li
机构: University of Pittsburgh (匹兹堡大学); University of Florida (佛罗里达大学)
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Biomolecules (q-bio.BM)
备注:
Abstract:Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations. Learning the mechanism logic is therefore essential for enhancing the fundamental chemical intelligence of large language models (LLMs). The stepwise deduction of reaction mechanism aligns naturally with the reasoning paradigms of reasoning LLMs. However, current chemical LLMs primarily emphasize coarse-grained name reactions for product prediction and retrosynthesis, often leading to physical inconsistencies and hallucinations. In contrast, specialized small-scale generative models for mechanism inference typically suffer from restricted generalization capacity across diverse chemical spaces. To overcome these limitations, we built a novel, large-scale reasoning dataset of reaction mechanisms. Furthermore, we established the FukuyamaBench, a difficult benchmark derived from Fukuyama’s Advanced Organic Reaction Mechanism book, to rigorously evaluate model performance on hierarchical mechanism reasoning. Our fine-tuned Qwen3-30B-A3B achieves 8.3% exact pathway match on FukuyamaBench Set~A, surpassing the specialized FlowER model (5.1%), demonstrating that mechanism-aware training substantially enhances chemical reasoning in language models.
[NLP-12] racing Agent ic Failure from the Flow of Success
【速读】： 该论文旨在解决基于大语言模型（LLM）的智能体系统在任务失败时的故障归因问题，即识别导致任务失败的关键步骤。现有方法通常依赖于计算开销大的提示工程流水线，或需要在失败轨迹上进行后训练并标注每一步的错误信息，而此类标注数据收集成本高且难以扩展。为此，论文提出一种无需步骤级监督的无监督故障归因方法——OAT，其核心思想是仅使用成功轨迹进行训练，在推理阶段通过神经控制微分方程建模成功轨迹在隐空间中的动态模式，并基于失败轨迹中各步骤与该模式的偏离程度计算异常得分，从而识别出潜在错误步骤。实验表明，仅需100条成功轨迹训练，OAT比基于提示的基线方法快200至5000倍，同时在域内和域外数据集上分别实现+20%和+7%的F1分数提升，证明了其高效性与强泛化能力，为智能体系统故障诊断提供了一种轻量级、可扩展的新范式。
链接: https://arxiv.org/abs/2607.12747
作者: Samuel Yeh,Yiwen Zhu,Shaleen Deep,Sharon Li
机构: University of Wisconsin-Madison (威斯康星大学麦迪逊分校); Microsoft Research (微软研究院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Failure attribution for LLM-based agentic systems, i.e., identifying which steps in a failure trajectory caused the task to fail, is critical for debugging and improving these systems. Existing approaches either rely on prompting-based pipelines, which are computationally expensive, or require post-training on failure trajectories with step-level error annotations, which are costly to collect and difficult to scale. We argue that a practical failure attribution model should be lightweight and trainable without step-level supervision on failure data. To this end, we address unsupervised failure attribution, i.e., training exclusively on successful trajectories and identifying error steps at inference time given a failure trajectory. We propose OAT, which casts this problem as one-class learning with neural controlled differential equations, modeling the dynamical pattern of successful trajectories in latent space. At inference time, each step in a failure trajectory is assigned an anomaly score based on its deviation from the dynamics learned on successful trajectories, which is then used to form a set of error steps. With training on only 100 successful trajectories, experiments show that OAT is 200–5000 \times faster than prompting-based baselines, and, at the same time, consistently outperforms them in both in-domain and out-of-distribution datasets with +20% and +7% F1 scores, respectively, demonstrating that OAT is a promising and efficient direction for diagnosing agentic system failures.
[NLP-13] Epistemic Stance Flexibility Probing: Measuring Prompt-Conditioned Register Shift in Large Language Models
【速读】： 该论文旨在解决大语言模型在面对“专家观点”与“自身立场”两类不同语义请求时，能否准确区分并相应调整其认知表达姿态（epistemic stance）的问题。现有评估基准在准确性、指令遵循和安全性等方面均未能有效捕捉模型在认知立场转换上的表现，尤其缺乏对模型在外部归因（externally attributed）与自我归因（self-attributed）情境下行为差异的系统性测量。为此，作者提出ESFP（Epistemic Shift and Flexibility Probe），一个以内外部归因对比为核心测量单元的行为评估基准。其关键创新在于构建了104个精心控制的测试项，覆盖六个认知范畴与五种句式模板，并从四个互补维度评估模型表现：词汇层面的自我归因程度、表征层级对角色框架的响应敏感性、基于大语言模型评判小组的句子级立场内容密度，以及跨条件立场一致性。实验结果表明，认知灵活性与模型整体能力高度正交——270亿参数的开源模型可媲美顶尖闭源系统，某厂商旗舰模型甚至弱于其轻量级版本，且推理优化模型并未表现出一致更高的灵活性。其中，立场内容密度是最强预测信号，而表面词汇标记如“我认为”可能显著变化但不伴随实际立场偏移。研究还提供了逐项置信区间、权重敏感性分析及复合得分解释局限性的讨论，强调ESFP衡量的是模型在不同归因条件下调整其认知姿态的倾向性，而非通用能力水平。
链接: https://arxiv.org/abs/2607.12739
作者: Binwen Liu,Yilin Ren
机构: Xi’an Jiaotong University (西安交通大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:A language model may be asked either what experts believe about a contested claim or what it believes about the claim itself. A trustworthy conversational agent should distinguish these two requests and respond in different epistemic registers: neutral attribution in the first case and stance expression in the second. Whether such a shift occurs-and whether it occurs coherently-is not directly assessed by existing benchmarks for accuracy, instruction following, or safety. We introduce ESFP, a behavioral benchmark that treats the contrast between externally attributed and self-attributed prompts as the fundamental unit of measurement. ESFP consists of 104 carefully controlled items spanning six epistemic categories and five phrasing templates, and evaluates model responses along four complementary dimensions: lexical self-attribution, representation-level responsiveness to role framing, sentence-level stance content density assessed by an LLM judge panel, and cross-condition stance consistency. Evaluating eight frontier models from five vendors, we find that epistemic flexibility is largely orthogonal to general model capability: a 27B open-weight model matches the strongest proprietary systems, the flagship model of one family underperforms its lightweight counterpart, and reasoning-optimized models do not consistently exhibit higher flexibility. Stance content density provides the strongest signal, while surface-level lexical markers such as ‘I think’ can change substantially without corresponding changes in expressed stance. We provide item-level bootstrap confidence intervals, weight-sensitivity analyses, and an explicit discussion of the interpretation limits of the composite score. ESFP measures a model’s propensity to adapt its epistemic stance under changing attribution conditions, rather than a general competence measure.
[NLP-14] Less Experts Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts
【速读】： 该论文旨在解决大规模稀疏专家混合（Sparse Mixture-of-Experts, MoE）模型在采用推测解码（Speculative Decoding, SD）时因专家激活模式不均导致的推理效率瓶颈问题。现有方法主要关注提高生成词元的接受概率，但在MoE架构下，词元选择还直接影响验证阶段被激活的专家集合，进而影响专家权重内存访问开销。研究发现，基于置信度的推测解码可能引发“专家散射”（expert scattering）现象：高概率词元被路由至互不重叠的专家，加剧内存访问压力并削弱推测加速效果。为此，论文提出EcoSpec——一种面向成本感知的推测解码框架，其核心在于将预测的边际专家激活成本融入词元选择过程，通过轻量级专家预测器与动态专家缓冲区机制，优先选择在保持高接受率的同时可复用已有激活专家路径的候选序列，且无需修改目标模型的验证规则。实验在DeepSeek-V3.1（671B）、Qwen3-235B-A22B和GPT-OSS-120B等三款大规模MoE模型上验证，EcoSpec显著降低活跃专家数量，实现最高达1.62倍的端到端解码速度提升，证明在非均匀内存开销结构下考虑专家激活成本对高效推测解码至关重要。
链接: https://arxiv.org/abs/2607.12696
作者: Jincheng Xie,Runheng Liu,Heyan Huang,Yawen Ling,Hanbin Dai,Yu Zheng,Wen Hu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Sparse Mixture-of-Experts (MoE) models have become an important approach for scaling Large Language Models (LLMs), but their inference efficiency depends strongly on expert activation patterns. Speculative decoding (SD) accelerates autoregressive generation by verifying multiple draft tokens in parallel, yet existing draft selection strategies primarily optimize acceptance likelihood. In large-scale MoE models, however, selecting draft tokens also determines the union of experts activated during verification. We observe that confidence-driven SD can introduce \textitexpert scattering: high-probability draft tokens may route to disjoint experts, increasing expert-weight memory traffic and reducing the speedup from speculation. Motivated by this observation, we revisit draft-tree selection under the non-uniform memory-cost structure of MoE inference. We propose \textscEcoSpec, a cost-aware speculative decoding framework that incorporates predicted marginal expert activation cost into draft selection. With a lightweight expert predictor and a dynamic expert buffer, \textscEcoSpec favors draft paths that preserve high acceptance likelihood while reusing experts already covered by the current verification set, without modifying the target-model verification rule. We evaluate \textscEcoSpec on three large-scale MoE models, including DeepSeek-V3.1 (671B), Qwen3-235B-A22B, and GPT-OSS-120B, across reasoning, coding, question-answering, and dialogue benchmarks. \textscEcoSpec consistently reduces active expert footprints and improves end-to-end decoding speed, achieving up to 1.62\times speedup. These results show that accounting for expert activation cost is important for efficient speculative decoding in large-scale MoE models.
[NLP-15] From Critic to Confidence: PPO for Language-Based Quantitative Prediction with Confidence Estimation
【速读】： 该论文旨在解决大语言模型（LLM）在基于非结构化输入进行语言驱动的定量预测时，因幻觉（hallucination）和过度自信错误（overconfident errors）导致的可靠性问题，核心挑战在于不仅需要准确的预测结果，还需具备可信的置信度评估能力。其解决方案的关键在于提出一种名为CARE-PPO的强化学习框架，通过将损失预测与不确定性估计相耦合，并引入基于预测误差的置信度对齐奖励（Confidence-Aligned Reward for Estimation, CARE），实现对数值预测精度与置信信号的联合优化。该框架在训练过程中利用奖励函数引导智能体（actor）生成更精确的预测，同时促使评判者（critic）学习一个与预测质量对齐的价值函数；推理阶段则将批判者（critic）重新用作置信度估计器，从而提供细粒度、可解释的置信度输出。实验在医疗与金融两大真实场景下，针对两个Qwen-3模型规模（4B和8B）验证了该方法的有效性，结果显示CARE-PPO在保持优异定量预测性能的同时，显著提升了置信度估计与实际预测误差之间的对齐程度，且在跨领域、跨语言的分布外（out-of-distribution）设置下仍具鲁棒性，同时减少了任务特定过拟合现象，体现了强化学习微调相比监督微调在泛化能力上的优势。
链接: https://arxiv.org/abs/2607.12687
作者: Mehak Dhaliwal,Rasta Tadayon,Andong Hua,Haewon Jeong,Yao Qin
机构: University of California, Santa Barbara (加州大学圣塔芭芭拉分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:LLMs can perform language-based quantitative prediction from unstructured inputs, but remain susceptible to hallucinations and overconfident errors, making it critical to know not only what a model predicts, but when its predictions can be trusted. We introduce CARE-PPO, a reinforcement learning framework that establishes a connection between loss prediction for uncertainty estimation and actor-critic PPO fine-tuning, enabling joint learning of accurate numerical estimates and reliable confidence signals in language-based quantitative prediction. CARE-PPO uses a Confidence-Aligned Reward for Estimation, defined as a function of prediction error, to provide dense error-aware feedback to the actor while inducing the critic to learn a value function aligned with prediction quality. During inference, we repurpose the critic as a confidence estimator. Across two real-world tasks in healthcare and finance and two Qwen-3 model scales (4B and 8B), CARE-PPO achieves strong quantitative prediction performance, while producing significantly better-aligned confidence estimates through the critic than logit-based and verbalized baselines. These gains persist under realistic out-of-distribution settings across domains, spanning linguistic and domain shifts. Finally, CARE-PPO reduces task-specific overfitting on general instruction-following prompts, consistent with the broader generalization advantages of RL fine-tuning over supervised approaches.
[NLP-16] Segregate Refine Integrate: Decomposing Multimodal Fusion for Sentiment Analysis
【速读】： 该论文旨在解决多模态融合中模态特异性信号精炼与跨模态交互建模之间的目标冲突问题，二者在传统方法中常被耦合在同一操作中，导致优化困难。其解决方案的关键在于提出SeRIn（Segregate, Refine, Integrate）架构，通过引入分离式设计作为结构先验：模态特异性表示在独立路径中演化，并分别基于各自编码器上下文进行精炼；同时设置专用的跨模态路径，仅用于累积各模态联合演化的信息，避免污染单模态流；完整的跨模态交互被延迟至最终预测阶段。消融实验表明，性能提升源于结构化交互机制，而非单纯增加模型容量；在视觉退化条件下，门控分析揭示了无需显式监督即可涌现的模态重加权能力。SeRIn在CH-SIMS和CMU-MOSEI两个基准上均达到当前最优性能，全面提升了各项指标。
链接: https://arxiv.org/abs/2607.12686
作者: Alexios Filippakopoulos,Elias Kallioras,Nikolaos Xiros,Efthymios Georgiou,Alexandros Potamianos
机构: National Technical University of Athens (希腊国立技术大学); Athena Research Center (希腊阿瑞斯研究中心); University of Bern (伯尔尼大学); Archimedes AI (希腊阿基米德AI); Synaptic Bloom PBC (美国突触绽放公司)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal fusion must simultaneously refine modality-specific signals and model cross-modal interactions; two competing objectives typically entangled within the same operation. We propose \textbfSeRIn (\textbfSegregate, \textbfRefine, \textbfIntegrate), a multimodal LM fusion scheme that enforces this separation as an architectural prior. Modality-specific representations evolve along isolated pathways, each refined against its respective encoder context, while a dedicated cross-modal pathway accumulates their joint evolution without contaminating unimodal streams. Full cross-modal interaction is deferred to a final prediction step - ablations confirm that structured interactions, not added capacity, drive the gains; gate analysis under visual corruption reveals emergent modality reweighting without explicit supervision. SeRIn achieves state-of-the-art results on CH-SIMS and CMU-MOSEI, improving all metrics on both benchmarks.
[NLP-17] Extractable Memorization From First Principles
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中可提取记忆性（extractable memorization）研究中存在的有效性问题。现有研究存在两种对立的缺陷：一方面，部分研究过度夸大记忆提取效果，例如依赖过短的序列，无法区分记忆现象与单纯可预测性；另一方面，另一些研究则质疑提取结果作为记忆证据的可靠性，指出模型可能复现未在训练中显式出现的真实文本。这两种观点均忽视了有效提取声明的核心标准：模型生成特定训练序列的概率必须高到足以表明其已形成记忆。为确定“足够高”的概率阈值，必须采用匹配比较（matched comparison）——即同时测量目标训练序列与语义或结构相近的非训练序列的生成概率。由于非训练序列不可能被记忆，其生成概率可作为可预测性的基线；若某训练序列的生成概率显著高于此基线，则构成记忆的有力证据。为此，论文提出了两种形式化的匹配比较方法：（1）基于置信区间校准的合取检验（conformal test），在训练与非训练序列从总体中采样时设定固定假阳性率（FPR）；（2）普查法（census），针对单个文档（如一本书）与其匹配的非训练文档进行对比。实证结果表明，匹配比较能够实现严谨且可校准的记忆性声明，并揭示了以往研究中的有效性漏洞。例如，在Wikipedia OLMo 2 32B上，模型对非训练10词后缀的生成频率约为训练序列的24%，这一比例反映的是假阳性而非真实记忆；而在Llama 3.1 70B对书籍数据的分析中，所校准的阈值低至1e-27，远低于实际采样预算下可提取的水平。因此，论文提出将“可提取记忆性”重新定义为：需具备有效的记忆性声明，并在合理采样预算内实现近乎确定性的生成。
链接: https://arxiv.org/abs/2607.12649
作者: A. Feder Cooper,Marika Swanberg,Jamie Hayes,Lea Duesterwald,Christopher De Sa,Daniel E. Ho,Mark A. Lemley,Percy Liang
机构: Yale(耶鲁大学); AVERI(耶鲁大学应用与验证研究机构); Stanford University (斯坦福大学); Google Research(谷歌研究院); Google DeepMind(谷歌深度思维); Cornell University (康奈尔大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Recent work on extractable memorization in LLMs suffers from two contrasting validity problems. Some studies overstate extraction, e.g., relying on sequences too short to distinguish memorization from predictability. Others imply that extraction is unreliable evidence of memorization, since models can also reproduce real-world text they weren’t explicitly trained on. In different ways, both overlook what makes a valid extraction claim: the model must generate a training sequence with high enough probability to indicate memorization. To determine what’s high enough, one has to perform a matched comparison: measuring the generation probabilities of both the training sequences of interest and comparable non-training sequences. Because non-training sequences cannot have been memorized, their probabilities provide a baseline for predictability; a training sequence exceeding this baseline provides evidence of memorization. We formalize matched comparisons in two ways: (1) a conformal test that calibrates a threshold to a chosen FPR when training and non-training sequences are sampled from populations, and (2) a census that calibrates against a matched non-training document when the object is a single document (e.g., a book). We show that matched comparisons enable rigorous, calibrated memorization claims, and reveal where prior setups have validity issues. For instance, on Wikipedia OLMo 2 32B reproduces non-training 10-token suffixes roughly 24% as often as training ones: that share of the training generation rate reflects false positives, not memorization. For Llama 3.1 70B on books, the thresholds we calibrate are as low as 1e-27, supporting memorization claims for sequences that no feasible sampling budget would extract. Based on these results, we refine “extractable memorization” to require a valid memorization claim and near-certain generation within a realistic budget.
[NLP-18] A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent : A Controlled Null and Its Mechanism
【速读】： 该论文旨在探究在4B至8B规模的小型语言模型与视觉-语言多模态模型（vision-language model）中，基于可验证奖励的强化学习方法（如组相对策略优化，GRPO）是否能够真正提升智能体的性能，而非仅对监督微调（supervised fine-tuning）所得行为进行重构。研究发现，在涵盖学习率、KL散度权重、随机种子、初始化方式及截断策略等18种配置的控制实验中，没有任何一组参数显著提升强监督基线在已掌握任务上的成功率；相反，在文本任务上，中高学习率甚至导致性能显著下降。该“无增益”结果在配对测试、25个评估种子、6个训练种子、配方调整、文本与标记集合（Set-of-Marks）截图观测输入以及将骨干网络扩展至8B的情况下均保持稳健，其中负面效应主要局限于文本任务，且在标记集合观测下仅为名义性影响。为排除实验流程缺陷的可能性，研究者在奖励可通过采样实现的任务上复现相同设置，结果显示成功率提升22个百分点，且置信区间不包含零，表明算法本身具备有效能力。由此推断，GRPO仅在存在可提升空间（即采样策略成功率高于贪婪策略）时才有效。进一步分析揭示：中等学习率导致性能退化，而高学习率引发系统崩溃，二者形成双分离现象——退化现象集中于注意力与MLP模块，而崩溃则无法归因于任何单一组件；同时，嵌入层权重变化主导了整体梯度运动，但其在因果上无实际影响。在4B模型中，后期层的有效秩（effective rank）与能力呈双向关联，但在8B模型中两者解耦，表明该耦合关系具有尺度依赖性。
链接: https://arxiv.org/abs/2607.12640
作者: Chengguang Gan,Zhixi Cai,Yunhao Liang,Hanjun Wei,Shiwen Ni,Qinghao Zhang
机构: Monash University (莫纳什大学); University of Chinese Academy of Sciences (中国科学院大学); Shenzhen University of Advanced Technology (深圳先进技术研究院); Pusan National University (釜山国立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning with verifiable rewards, and Group Relative Policy Optimization (GRPO) in particular, is now run routinely on a supervised checkpoint in the hope of producing a stronger agent. We ask whether it adds skill to a small language and vision-language model web agent at the 4B to 8B scale, or whether it mostly reshapes behavior the supervised model already has. Across a control grid of 18 runs that varies learning rate, KL weight, seed, initialization, and clipping, no configuration credibly improves the success rate of a strong supervised baseline on tasks the agent has largely mastered. On the text track, moderate to high learning rates make it credibly worse. The null holds under paired testing, 25 evaluation seeds, 6 training seeds, changes to the recipe, both text and Set-of-Marks screenshot observations, and scaling the backbone to 8B; the credible harm is a text-track finding and is only nominal under Set-of-Marks. To show that the null reflects the setting and not a broken pipeline, we run the identical harness, reward, and recipe on tasks whose reward is reachable by sampling, and there the success rate rises by 22 points with a paired interval that excludes zero. GRPO therefore helps only when there is headroom to climb, meaning the sampled policy already succeeds more often than the greedy one. We then explain the failure. A middle learning rate degrades the agent and a high one collapses it, and the two regimes form a double dissociation: grafting localizes the degrade regime to the attention and MLP blocks, while the collapse regime cannot be traced to any single group, and the embedding change that dominates the weight movement is causally inert. At 4B, effective rank in the late layers tracks capability in both directions; at 8B the two come apart. This coupling is specific to the smaller model, so we report it as scale-dependent.
[NLP-19] Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?
【速读】： 该论文旨在解决在高风险领域中，大型语言模型（Large Language Models, LLMs）作为自主代理时，其决策过程是否受到诱发情绪影响的问题。尽管LLMs具备感知和共鸣用户情绪的能力，但其在序列决策中是否会因情绪状态的改变而产生行为偏差尚不明确。为此，研究采用经典的“爱荷华赌博任务”（Iowa Gambling Task, IGT）作为心理决策范式，并结合基于想象的情绪诱导方法，构建了可验证的实验框架。关键解决方案在于：首先通过实证验证了LLMs能够从上下文中感知强烈且可区分的情绪，并能以类人速率从序列交互中学习；在此基础上，发现与人类不同，总体上诱发情绪对LLMs的决策动态无显著偏倚。然而，愤怒情绪具有条件性影响：它降低了LLMs对错误决策惩罚的敏感性，且在游戏早期会抑制探索行为，导致决策过早固化于少数选项。这一发现揭示了诱发情绪对LLMs决策机制的微妙但本质区别于人类的影响模式，为未来研究情感因素对LLM代理行为的调制提供了有效工具。
链接: https://arxiv.org/abs/2607.12631
作者: Minh Khoi Ho,Zihao Zhu,Runchuan Zhu,Levina Li,Zhiwen Fan,Zhangyang Wang,Junyuan Hong
机构: National University of Singapore (新加坡国立大学); Texas A&M University (德克萨斯农工大学); UCLA (加利福尼亚大学洛杉矶分校); University of Texas at Austin (德克萨斯大学奥斯汀分校); Mass General Hospital (麻省总医院); Harvard Medical School (哈佛医学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains, understanding contextual factors that may modulate their decision-making becomes critical. While LLMs are trained to perceive and resonate with users’ emotions, it remains unclear whether induced emotion can influence their sequential decision-making. We investigate this question using the Iowa Gambling Task (IGT), a classic psychological paradigm for studying decision-making under uncertainty, combined with an imagination-based emotion induction procedure. We first validate the feasibility of this paradigm by confirming that LLMs can sense strong, distinguishable emotions from context and that LLM agents can learn from sequential interactions in a human-like pace. With the validated setup, we find that, different from humans, induced emotion does not significantly bias the decision dynamics of LLM agents on average. However, the effects of anger are conditioned: inducing anger makes LLM agents less sensitive to penalties for bad decisions, and in early stages of the game, anger can lower exploration, locking decisions into a few choices early. These findings reveal the subtle yet distinct effects of induced emotion on LLM decision-making compared to human behavior, and provide a tool for future research on affective modulation of LLM agents.
[NLP-20] KnowAct-GUIClaw: Know Deeply Act Perfectly Personal GUI Assistant with Self-Evolving Memory and Skill
【速读】： 该论文旨在解决当前主流智能体框架OpenClaw在跨平台图形用户界面（GUI）交互支持不足以及缺乏健全的自我进化机制这一核心问题。具体而言，现有框架难以适应多样化的设备生态系统，且无法通过任务执行经验实现持续性能优化。为此，论文提出“知深行准”（Know Deeply, Act Perfectly）范式，强调用户交互与任务执行经验的积累可直接提升执行准确率与效率，实现认知理解与操作执行的统一。基于此范式，提出KnowAct-GUIClaw框架，采用“知-路-行-反思”（Know-Route-Act-Reflect）架构：首先，主智能体利用累积的交互经验与任务相关知识进行长周期任务的分解与分配（Know）；其次，引入可插拔的GUI子智能体，具备可追溯经验的记忆系统（Know）与自进化技能库（Act），支持跨平台无缝迁移与快速集成；尤其关键的是，该框架持续存储用户画像与反馈信息，以迭代优化任务分解与工具调用的准确性。大量实验表明，该框架在Android、iOS、HarmonyOS及Windows平台上均表现出卓越的效率、准确率与跨平台适应性；其中，基于开源Kimi-2.6模型的GUIClaw在长周期移动任务基准测试MobileWorld上达到64.1%的最高准确率，超越所有同类框架及闭源大模型（如Seed-2.0-Pro与GPT-5.5）。此外，其知识化记忆与执行技能具备良好的跨基座模型可迁移性，在Kimi-2.6上带来8.5%的性能提升。解决方案的关键在于构建融合经验驱动的动态记忆系统与自进化技能库的可插拔GUI子智能体，从而实现跨平台泛化能力与持续学习机制的双重突破。
链接: https://arxiv.org/abs/2607.12625
作者: Yunxin Li,Jinchao Li,Shibo Su,Zhenran Xu,Chenrui Zhao,Tongshu Bian,Xiaoman Liang,Meishan Zhang,Baotian Hu,Min Zhang
机构: Harbin Institute of Technology, Shenzhen (哈尔滨工业大学深圳); Shenzhen Loop Area Institute (深圳环区研究所)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 9 figures
Abstract:OpenClaw has emerged as a leading agent framework for complex task automation, yet it faces insufficient cross-platform GUI interaction support and a well-built self-evolution mechanism. These flaws limit its adaptation to diverse device ecosystems and prevent performance improvements through continuous learning from execution experience. To resolve these issues, we propose the Know Deeply, Act Perfectly paradigm for personal assistants, which holds that accumulated user interaction and task-running experience directly improve execution accuracy and efficiency, unifying cognitive comprehension and operational execution. Based on this paradigm, we introduce KnowAct-GUIClaw, a novel Know-Route-Act-Reflect framework designed to address OpenClaw’s GUI manipulation deficits and break through its cross-platform and recursive self-improvement constraints. First, the host agent leverages accumulated interaction experience and task-relevant knowledge for long-horizon task decomposition and allocation (Know). Second, a pluggable GUI subagent with an experience-attributable memory system (Know) and self-evolving skill library (Act), enabling seamless cross-platform migration and fast-path integration. Especially, this framework continuously stores user profiles and feedback to improve the accuracy of task decomposition and tool calls. Extensive experiments across Android, iOS, HarmonyOS and Windows show that KnowAct-GUIClaw achieves superior efficiency, accuracy and cross-platform adaptability. Especially, the GUIClaw with open-source Kimi-2.6 models achieves the best performance (64.1%) on the long-horizon MobileWorld benchmark, beating all agentical frameworks and closed-source agentical models, e.g., Seed-2.0-Pro and GPT-5.5. Additionally, the knowledgeable memory and execution skills supported by our framework are transferable across diverse base models, improving by 8.5% with Kimi-2.6.
[NLP-21] ranslation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages
【速读】： 该论文旨在解决低资源语言（如保加利亚语、中文、荷兰语、意大利语和俄语）在构建高质量预训练语言模型时面临的标注数据匮乏与计算成本高昂的问题。其核心挑战在于如何在缺乏本地化语料和计算资源的情况下，有效提升非英语语言的自然语言处理（Natural Language Processing, NLP）性能。论文提出的解决方案关键在于采用“基于翻译的微调”（translation-based fine-tuning）策略：将目标语言的非英语文本数据统一翻译为英文，随后在已有的英文BERT模型上进行微调。该方法通过利用现有高性能英文模型与大规模英文语料，规避了从头训练本地化BERT模型的高成本问题。研究系统评估了该策略在六项典型NLP任务（情感分析、仇恨言论检测、问答、命名实体识别、词性标注和自然语言推理）中的表现，并发现该方法在53.3%的实验设置中表现相当甚至更优，尤其在依赖句法或结构模式的任务及与英语语言类型相近的语言（如荷兰语）上效果显著；而在需要细粒度标记或文化语境理解的任务（如命名实体识别和仇恨言论检测）以及中文等语言中表现相对较弱。研究表明，基于翻译的微调是一种可扩展、资源高效且实证有效的路径，有助于推动人工智能在多语言环境下的包容性与可持续发展。
链接: https://arxiv.org/abs/2607.12612
作者: Hielke Muizelaar,Giulia Rivetti,Marco Spruit,Marcel Haas
机构: LUMC(莱顿大学医学中心)
类目: Computation and Language (cs.CL)
备注: 26 pages, 1 figure
Abstract:BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains. However, developing high-quality BERT models for non-English languages remains challenging due to limited annotated data and high computational demands. Translating non-English data into English and fine-tuning existing English BERT models offers a resource-efficient alternative, yet few studies have structurally compared translation-based fine-tuning with native-language BERT performance across tasks and languages. This study provides such a comparison, evaluating the feasibility of translation-based fine-tuning across six NLP tasks: Sentiment Analysis, Hate Speech Detection, Question Answering, Named Entity Recognition, Part-of-Speech Tagging, and Natural Language Inference, using datasets translated from Bulgarian, Chinese, Dutch, Italian, and Russian. Across all settings, the translation-based approach was comparable or superior in 53.3 percent of cases. Gains were most frequent in Question Answering, Part-of-Speech Tagging, and Natural Language Inference, while performance declines were common in Named Entity Recognition and Hate Speech Detection. The results show that translation-based fine-tuning is most effective for tasks relying on syntactic or structural patterns and for languages typologically close to English, such as Dutch, but less effective for token-level or culturally nuanced tasks, particularly in Chinese. Overall, this study demonstrates that translation-based fine-tuning offers a scalable, resource-efficient, and empirically validated path for extending NLP to low-resource languages while advancing linguistic inclusivity and sustainability in artificial intelligence.
[NLP-22] A JoLT for the KV Cache: Near-Lossless KV Cache Compression via Joint Tucker and JL-Residual Allocation for LLM s
【速读】： 该论文旨在解决大模型推理中键值（Key-Value, KV）缓存带来的显著内存开销问题，尤其在长上下文场景下，KV缓存已成为制约吞吐量的主要瓶颈。传统解决方案主要包括低秩分解和量化方法，但二者均未充分挖掘KV缓存在层内天然呈现三阶张量结构的特性——即头（head）、token与特征（feature）三个维度所承载的冗余程度差异显著。为此，本文提出JoLT（Joint Low-rank and Tucker decomposition with Johnson-Lindenstrauss residual），从三阶张量视角出发，采用部分Tucker分解仅对token与feature轴进行压缩，保留head与layer轴不变，并通过一个经过Johnson-Lindenstrauss（JL）旋转的低比特残差项恢复因截断损失的能量。该方法通过单个拉格朗日对偶优化器，在每层组内联合分配Tucker秩与残差位宽，实现端到端的字节预算约束下的最优压缩。实验结果表明，JoLT在保持近似无损性能的前提下实现了2-3倍压缩率，其困惑度、GSM8K准确率及RULER检索任务表现与原始未压缩基线相当（处于统计噪声范围内），且在2倍压缩比下，键和值的相对Frobenius误差分别低至0.009和0.006，较跨层SVD与4比特量化提升一个数量级。进一步提出的FlashJoLT基于随机化SVD，可在相同质量下实现5-13倍的压缩速度加速。
链接: https://arxiv.org/abs/2607.12550
作者: Rahul Krishnan,Volker Schulz
机构: Universität Trier(特里尔大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Optimization and Control (math.OC)
备注: 11 pages, 1 figure
Abstract:The key-value (KV) cache has become the dominant memory cost of transformer inference. It grows with batch size, context length, and depth, and at long context it, rather than the model weights, sets the ceiling on throughput. Two families of methods reduce it. Low-rank methods factor two-dimensional slices of the cache, either per-head matrices or cross-layer feature blocks, and quantization methods lower the bit-width of every entry. Neither family exploits the fact that the cache at a layer is naturally a third-order tensor whose three axes, the heads, the tokens, and the features, carry very different amounts of redundancy. We take this tensor view directly. Our method, JoLT, applies a partial Tucker decomposition that compresses only the token and feature axes while leaving the head and layer axes intact, and then restores the energy that truncation discards with a Johnson-Lindenstrauss (JL) rotated low-bit residual. A single Lagrangian dual allocates the Tucker ranks and the residual bit-widths together, per layer group and separately for keys and values, under one byte budget. The result is a near-lossless 2-3x compression: perplexity, GSM8K accuracy, and RULER needle-in-a-haystack retrieval all stay at or within statistical noise of the uncompressed baseline on both a grouped-query-attention model (Mistral-7B-v0.3) and a multi-head-attention model (LLaMA-2-13B). At 2x, JoLT reconstructs the cache to relative Frobenius error 0.009 (K) and 0.006 (V) on both architectures, roughly an order of magnitude below cross-layer SVD and 4-bit quantization. A randomized-SVD variant, FlashJoLT, delivers a 5-13x compression-time speedup at matched quality.
[NLP-23] Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models
【速读】： 该论文旨在解决编码代理（coding agent）在推理过程中难以有效整合外部工具返回结果的问题，这一能力在传统基于代码的自回归预训练中仅具备单向（从左到右）建模能力，无法充分捕捉工具调用与响应之间的双向依赖关系。其核心解决方案是提出一种函数感知的填空中间（function-aware fill-in-the-middle, FIM）中段训练方法，通过程序依赖图（program dependency graph）分析筛选函数，并结合复杂度与可推断性双重标准进行掩码，构建自监督目标。该方法在26亿标记符、来自968个GitHub仓库的去污语料上对Qwen2.5-Coder-Instruct（7B/14B）和Qwen3-8B模型进行中段训练，随后接入现有代理后训练流程。实验表明，该方法显著提升SWE-Bench-Verified（+2.8/+3.0 at 7B/14B，+3.2 at Qwen3-8B）和SWE-Bench-Lite（+3.7/+4.0/+5.4）性能，且在不同后训练管道（R2E-Gym、SWE-Smith）及非Qwen2.5基座模型（Qwen3-8B with SWE-Lego）上均保持增益。更关键的是，尽管训练数据仅包含Python代码，但该方法所引入的函数调用归纳偏置（function-call inductive bias）在代理后训练后仍能保留，并有效缓解代理训练带来的非代理编码（如LiveCodeBench）与非编码工具使用基准（tau-bench、BFCL）能力退化问题，展现出强大的泛化能力。
链接: https://arxiv.org/abs/2607.12463
作者: Yubo Wang,Jiarong Liang,Yuxuan Zhang,Xuye Liu,Cong Wei,Yuyu Zhang,Ping Nie,Wenhu Chen
机构: University of Waterloo ( Waterloo); University of British Columbia (不列颠哥伦比亚大学); NVIDIA (英伟达); Verdent AI (Verdent AI); Vector Institute (向量研究所)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Coding agents must integrate external tool returns into ongoing reasoning - a capability that standard left-to-right pretraining on code exposes only in its forward direction. We observe that the action-observation-continuation loop of a coding agent is structurally isomorphic to a function call site, where a caller binds arguments, a callee returns a value computed elsewhere, and downstream code consumes that value. This conditioning structure exists at internet scale in ordinary code. We exploit it through function-aware fill-in-the-middle (FIM) mid-training: a self-supervised objective that masks functions selected via program dependency graph analysis and a complexity-inferability double criterion. We mid-train Qwen2.5-Coder-Instruct (7B/14B) and Qwen3-8B on a 2.6B-token decontaminated corpus drawn from 968 GitHub repositories, then apply existing agentic post-training pipelines. Mid-training improves SWE-Bench-Verified by +2.8/+3.0 at 7B/14B and by +3.2 on Qwen3-8B; SWE-Bench-Lite gains are +3.7/+4.0/+5.4 on the same models. The improvement holds across two post-training pipelines (R2E-Gym, SWE-Smith) and on a non-Qwen2.5 base (Qwen3-8B with SWE-Lego). Beyond in-domain gains, mid-training also mitigates the capability erosion that agentic post-training otherwise inflicts on non-agent coding (e.g., LiveCodeBench) and non-coding tool-use benchmarks (tau-bench, BFCL): although the mid-training corpus contains Python code only, the function-call inductive bias survives post-training and yields consistent gains.
[NLP-24] Language Identification with Succinct Machine-Independent Traces
【速读】： 该论文旨在解决经典语言识别在极限（Gold-Angluin）模型中因缺乏有效学习信号而导致的负结果问题，特别是针对现有研究中依赖于大型词元（token）词汇表及显式自动机理论机器模型生成计算轨迹（computational trace）所引发的可扩展性与通用性局限。其核心解决方案在于：提出一种无需依赖底层生成机器模型、且仅使用线性规模词元（相对于目标语言字母表大小）的计算轨迹构造方法，从而实现对任意语言集合的极限识别（identification in the limit）。该方法的关键创新在于直接从语言本身定义轨迹，确保了轨迹的通用性与简洁性，同时显著降低了对词元空间的依赖，为生成式学习范式在形式语言识别中的应用提供了新的理论基础。
链接: https://arxiv.org/abs/2607.12443
作者: Moses Charikar,Jon Kleinberg,Chirag Pabbaraju
机构: 未知
类目: Computation and Language (cs.CL); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
备注:
Abstract:Motivated by the power of large language models, there has been renewed interest in the Gold-Angluin model of language identification in the limit, with an eye toward variants of the model that might overcome the negative results for its original formulation. Recent papers on this question have proposed looking at computational traces and annotations of training strings as a source of additional power for a learner, reflecting empirical regularities such as the way that commented source code is easier to learn from than arbitrary source code, and text annotated with algorithmically generated chain-of-thought tokens can be easier to learn from than the raw text itself. This recent work has shown positive results for language identification in the presence of such computational traces, but the traces in these positive results come from explicit automata-theoretic machine models that generate the language, where the underlying vocabulary of tokens for the traces is very large. In this paper, we address two fundamental issues left open by this line of work: can we achieve positive results with traces that use only a small alphabet, and can we define traces directly from the language itself, without requiring an underlying machine model that generates it? We establish positive results for both of these questions: for an arbitrary collection of languages, we show how to define computational traces that enable identification in the limit, using an alphabet of tokens that is linear in the size of the alphabet that the languages are defined over, and independent of any other properties of the languages.
[NLP-25] WikiSTAR: A System for Shedding Light on the Hidden History of Scientific Wikipedia Articles
【速读】： 该论文旨在解决维基百科中科学知识演化过程因大量常规编辑而被掩盖的问题，即如何从海量修订记录中有效识别并可视化具有科学意义的编辑行为。其核心解决方案是提出WikiSTAR（Scientific Tracking of Article Revisions），一个基于大语言模型（LLM）分类器与专家设计的多标签分类体系相结合的交互式系统。该系统首先对编辑类型进行精准标注，如技术术语添加、新研究成果引入及科学叙事变更等；随后通过多粒度交互视图，支持用户从宏观趋势到单个编辑的全尺度追溯，从而揭示科学知识演进的动态过程。研究证明，该方法可有效挖掘以往难以发现的知识演化模式，并激发新的研究问题，显著提升科学史分析的可操作性与深度。
链接: https://arxiv.org/abs/2607.12441
作者: Omer Ehrlich,Nitzan Barzilay,Rona Aviram,Tom Hope
机构: The Hebrew University of Jerusalem; Ben-Gurion University of the Negev; Allen Institute for AI (Ai2)
类目: Computation and Language (cs.CL)
备注:
Abstract:Wikipedia plays a key role in shaping public understanding of science, and its openly accessible revision history is a unique record of how scientific knowledge evolves over time. Yet scientifically meaningful revisions are obscured by the sheer volume of routine edits, leaving each article’s scientific history hidden. We present WikiSTAR (Scientific Tracking of Article Revisions), an interactive system for exploring scientifically meaningful changes across an article’s revision history. Using an LLM classifier with an expert-designed multi-label taxonomy, WikiSTAR first tags edit types such as the addition of technical terms, new research findings, and changes in scientific narrative. Then, through interactive views, an article’s full revision history can be traced at any granularity - from aggregate trends that reveal when and in which sections scientific content was added or refined, down to individual edits - showing how scientific knowledge develops at a scale previously impossible. In a user study, experts from three domains found that WikiSTAR surfaced new patterns and research questions and enabled previously impractical analyses. We release our system, code and a human-annotated benchmark.
[NLP-26] Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
【速读】： 该论文旨在解决在无人类标注数据的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）场景下，如何通过大规模模型实现高质量链式思维（Chain-of-Thought, CoT）推理的问题。现有研究受限于计算资源，普遍局限于小规模模型，导致大模型下的训练动态与涌现能力尚不清晰。为突破这一瓶颈，论文提出了一套稳定高效的训练流水线，其关键在于结合算法优化与系统级改进：包括裁剪重要性采样（clipped importance sampling）、训练-推理比例校正以及混合精度控制，以应对因模型规模扩大而引发的可读性差、令牌冗余及推理深度缺乏自适应等问题。实验表明，将模型扩展至1万亿参数（1T）显著提升了样本效率与性能上限，并揭示了训练过程经历“发现期”与“精炼期”的阶段性演化规律；更重要的是，模型自发涌现出类人化表达、结构化格式、自我验证、并行推理及上下文焦虑等高级认知行为，使人工设计的启发式规则变得冗余。此外，论文构建了一个涵盖可理解性、可复现性与效率三个维度的结构化评估框架，用于衡量CoT质量，结果表明所提出的Ring-2.5-1T-Zero模型在生成结构化、简洁的推理轨迹方面具有显著优势。这些发现验证了“规模即力量”（bitter lesson）的普适性，为理解1万亿参数量级下的模型涌现行为提供了重要洞见。
链接: https://arxiv.org/abs/2607.12395
作者: Xinyu Tang,Gangqiang Cao,Yurou Liu,Yuliang Zhan,Xiaochong Lan,Yifan Li,Yuchen Yan,Han Peng,Zican Dong,Zhenduo Zhang,Tianshu Wang,Xinyu Kong,Zujie Wen,Wayne Xin Zhao,Zhiqiang Zhang,Jun Zhou
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoning. However, due to computational constraints, existing studies are largely restricted to small models, leaving the training dynamics and emergent capabilities at a large scale unexplored. To meaningfully explore this frontier, we aim to elicit high-quality reasoning behaviors from the model. However, we find that naive scaling often suffers from poor readability, token redundancy, and a lack of adaptive reasoning depth. To address these challenges, we present a stable and efficient training pipeline, incorporating algorithmic and system optimizations such as clipped importance sampling, training-inference ratio correction, and mixed-precision control. Our experiments offer three key findings that validate the “bitter lesson” of scaling: (1) scaling to 1T parameters significantly enhances sample efficiency and performance ceilings; (2) the training process progresses sequentially through an initial discovery phase followed by a sharpening phase; and (3) the model spontaneously develops advanced cognitive behaviors, including anthropomorphism, structured formatting, self-verification, parallel reasoning, and context anxiety, rendering hand-crafted heuristics redundant. Evaluated on seven mathematical benchmarks, Ring-2.5-1T-Zero achieves competitive performance. Additionally, to assess CoT quality beyond final-answer correctness, we propose a structured evaluation framework across three dimensions: comprehensibility, reproducibility, and efficiency, where our model demonstrates clear advantages in producing structured and concise reasoning traces. By sharing our observed emergent phenomena, we hope to provide the community with deeper insights into scaling behaviors, particularly at the 1-trillion scale.
[NLP-27] Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit
【速读】： 该论文旨在解决社交媒体上癌症相关谣言传播问题，尤其关注其在乳腺癌、肺癌、结直肠癌和前列腺癌等议题讨论中对公众预防、筛查及治疗决策可能产生的负面影响。现有研究受限于定义狭窄、数据集规模小以及二元标签框架，难以全面刻画复杂的信息误传现象。为此，作者提出了一种多维度的癌症谣言分类体系（multi-dimensional taxonomy），涵盖谣言存在性、信息类型、风险等级、立场态度与主题焦点等七个维度，并基于专家标注数据评估多种大语言模型（Large Language Models, LLMs）在可扩展谣言标注中的表现。研究发现，癌症相关内容中约6%为谣言，且不同社区与话题间存在显著差异；少样本提示（few-shot prompting）显著提升了模型在细粒度维度上的分类性能。此外，研究识别出若干反复出现的谣言叙事模式，包括缺乏证据支持的治疗方法、对主流医学的不信任以及关于诊断与筛查的误导性陈述。该研究构建的分类体系、标注数据集及实证发现为在线癌症谣言的多维度建模提供了坚实基础。
链接: https://arxiv.org/abs/2607.12383
作者: Aria Pessianzadeh,Pooriya Jamie,Naima Sultana,Georgia Himmelstein,Yuliya Zektser,Patricia Ganz,Homa Hosseinmardi,Amir Ghasemian,Rezvaneh Rezapour
机构: Drexel University (德雷塞尔大学); UCLA (加州大学洛杉矶分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Cancer-related discussions on social media provide an important space for information exchange and peer support, but also facilitate the spread of misinformation that may influence prevention, screening, and treatment decisions. Existing research on cancer misinformation often relies on narrow definitions, small-scale datasets, or binary labeling frameworks. We introduce a multi-dimensional taxonomy for characterizing cancer misinformation in Reddit discussions of breast, lung, colon, and prostate cancer. The taxonomy captures seven dimensions, including misinformation presence, information type, risk level, stance, and topical focus. Using expert-annotated data, we evaluate multiple large language models (LLMs) for scalable misinformation annotation and analyze cancer misinformation across Reddit communities. Our results show that cancer-related misinformation constitutes approximately 6% of Reddit cancer discussions, with substantial variation across communities and misinformation topics. Few-shot prompting substantially improves classification performance, particularly for nuanced taxonomy dimensions. We additionally identify recurring misinformation narratives centered on unsupported treatments, distrust of conventional medicine, and misleading claims about diagnosis and screening. Our taxonomy, dataset, and findings provide a foundation for multi-dimensional modeling of online cancer misinformation.
[NLP-28] Policy-Conditioned Constrained Decoding for Column-Level Access Control in Text-to-SQL
【速读】： 该论文旨在解决文本到SQL（Text-to-SQL）系统在跨信任边界部署时面临的三大挑战：政策合规性、答案覆盖率与计算成本的平衡问题。现有方法通常基于查询提及的列进行拒绝决策，并采用随机策略执行，但这种方法无法准确反映列的实际语义使用情况，导致难以确定性地防止违规行为。其核心问题是：查询是否合规不仅取决于涉及哪些列，更取决于这些列在语义上的具体用途（如输出、过滤条件或聚合参数）。为此，论文提出一种基于列使用策略（column-use policy）的形式化框架，将列的语义角色（output, filter condition, aggregation argument）与解码器追踪的语法生成规则对齐。由此构建的PCC-SQL系统通过引入逐标记的逻辑掩码，在单次解码过程中即可确定性地消除支持的SQL片段中的单查询列使用违规。实验表明，PCC-SQL在三个基准测试和三种开源模型上实现了0%泄漏率（Leakage Rate），在Spider-CU数据集上覆盖率高达88.7%，同时仅增加不超过10%的生成令牌数，且在执行准确性方面表现出良好的语义对齐能力。
链接: https://arxiv.org/abs/2607.12341
作者: Ryoto Miyamoto,Xin Fan,Hayato Yamana
机构: Waseda University (早稻田大学)
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Databases (cs.DB)
备注:
Abstract:Text-to-SQL is increasingly deployed across trust boundaries between data providers and users. Such deployment must balance three competing requirements: policy compliance, answer coverage, and bounded cost. Existing approaches typically decide refusal based on which columns a query mentions and enforce it stochastically. Whether a query is compliant, however, depends not only on which columns appear but on how they are used, and stochastic enforcement cannot deterministically rule out violations. We formalize this requirement as a column-use policy over semantic use: output, filter condition, and aggregation argument. We integrate the policy by aligning each role with grammar productions tracked by the decoder. The resulting system, PCC-SQL, applies a per-token logits mask that deterministically eliminates single-query column-use violations on the supported SQL fragment in a single decoding pass. Across three benchmarks and three open-source models, PCC-SQL achieves 0% Leakage Rate and Coverage up to 88.7% on Spider-CU, while staying within +10% tokens of direct prompting. We additionally assess semantic alignment with execution accuracy.
[NLP-29] QUBO-Optimized Evidence Selection for Retrieval-Augmented Question Answering with Unconventional Solvers
【速读】： 该论文旨在解决检索增强型问答（Retrieval-Augmented Question Answering, RAG）系统中多跳问题（multi-hop questions）的证据选择难题，即如何从候选文本段落中筛选出能够协同支持答案生成的互补性证据集合。传统方法依赖于top-k排序，仅依据单个段落的相关性得分进行选择，难以满足多跳问题所需的多重信息需求。尽管基于大语言模型（LLM）的集合选择方法已尝试解决此问题，但其在中间阶段调用LLM导致计算成本高且难以扩展。本文提出将证据选择建模为无约束二次二值优化（Quadratic Unconstrained Binary Optimization, QUBO）问题，通过构建一个能量函数，综合考虑相关性、信息需求覆盖度、支持强度、冗余性、互补性及紧凑性等多重目标。低能量解对应于覆盖必要信息需求且避免冗余的紧凑证据子集。该方法将组合式证据选择与语义答案生成解耦，使大语言模型仅用于最终的答案生成，而证据选择由可兼容Ising/QUBO求解器的离散优化框架完成。实验在HotpotQA数据集上验证了该方法的有效性，其在精确匹配率和token-F1指标上达到与基于LLM的集合选择器相当的性能，同时具备良好的可扩展性和结构化证据选择能力。结果表明，多跳证据选择可被形式化为离散优化问题，为未来RAG系统中将大语言模型聚焦于语义处理、而上下文选择交由专用优化求解器实现提供了可行路径。
链接: https://arxiv.org/abs/2607.12334
作者: Rahul Singh,Madhav Vadlamani
机构: University of California Santa Barbara (加州大学圣塔芭芭拉分校); Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL); Emerging Technologies (cs.ET)
备注:
Abstract:Retrieval-augmented question answering depends on selecting evidence passages that jointly support answer generation. However, many RAG pipelines rely on top-(k) ranking, where passages are selected mainly by individual relevance scores, even though multi-hop questions often require complementary evidence satisfying multiple information requirements. Recent LLM-based selectors address this by treating retrieval as set selection, but using an LLM for this intermediate stage can be costly and difficult to scale. In this work, we formulate evidence selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Given a question, candidate passages, and decomposed information requirements, our method constructs an energy function that balances relevance, requirement coverage, support strength, redundancy, complementarity, and compactness. Low-energy solutions correspond to compact evidence subsets that cover the needed requirements while avoiding unnecessary or repetitive context. The selected passages are then passed to a downstream language model for answer generation, separating combinatorial evidence selection from semantic answer generation. We evaluate the proposed QUBO selector on HotpotQA and compare it with LLM-based set selectors and non-LLM baselines including BM25, relevance top-(k), maximal marginal relevance, hybrid lexical–semantic ranking, greedy coverage, and random selection. The QUBO selector achieves competitive exact-match and token-F1 performance relative to LLM-based selectors while providing a solver-compatible formulation for structured evidence selection. These results suggest that multi-hop evidence selection can be cast as discrete optimization, opening a path toward RAG pipelines where LLMs are reserved for semantic processing and answer generation, while context selection is handled by Ising/QUBO-compatible solvers.
[NLP-30] LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes
【速读】： 该论文旨在解决现有问答（QA）系统在真实世界异构数据湖（data lakes）中表现不佳的问题，尤其针对企业与科学领域中表格、文本段落及关联元数据等弱结构化、多模态数据的端到端检索与综合推理能力不足。其核心挑战在于：当前主流基准测试忽略真实场景中的噪声发现过程，无法有效评估系统从复杂、非标准化数据源中定位并整合证据的能力。为此，研究提出LakeQuest这一由人工验证的基准数据集，包含9,846个高质量问答对，覆盖人工智能/机器学习元数据、零售银行及多模态生物医学药物信息三个领域，并为每个问题提供精确且模态感知的证据指针。该设计的关键在于将“源发现”与“跨模态合成”分离，从而暴露现代QA系统在处理元数据图中的关系链推理、银行账本中的政策依附性判断以及生物医学领域联合表格问答时的系统性缺陷。实验表明，即使具备高质量检索能力的检索增强生成（RAG）和代理工具调用方法仍难以实现正确推理，凸显未来智能问答系统亟需强化数据发现机制与跨文件忠实组合能力。
链接: https://arxiv.org/abs/2607.12310
作者: Michael Solodko,Steven Gong,Guangwei Yu,Satya Krishna Gorti,Jesse C. Cresswell,Victor Zhong
机构: University of Waterloo ( Waterloo); Layer 6 AI (Layer 6 AI)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 24 pages, 4 figures, 18 tables. Accepted at the Conference on Language Modeling (COLM) 2026
Abstract:While modern question answering (QA) systems excel on clean, schema-aligned corpora, real-world knowledge is rarely so neatly packaged. Answering questions over enterprise and scientific data lakes requires systems to navigate heterogeneous, weakly structured collections of tables, passages, and linked metadata. Current benchmarks abstract away this noisy discovery process, failing to evaluate end-to-end performance. To bridge this gap, we introduce LakeQuest, a human-validated benchmark of 9,846 QA pairs designed to evaluate the end-to-end retrieve-and-synthesize pipeline over realistic data lakes. LakeQuest spans three diverse domains (AI/ML metadata, retail banking, and multimodal biomedical drug information) and pairs every question with exact, modality-aware evidence pointers. By isolating source discovery from cross-modal synthesis, LakeQuest exposes critical failure modes in modern QA systems. Our baseline evaluations, including standard Retrieval-Augmented Generation (RAG) and agentic tool-use methods, reveal that high-quality retrieval does not guarantee correct reasoning. Systems consistently struggle with relation chaining in metadata graphs, policy grounding in bank ledgers, and joint tabular QA in biomedical contexts, highlighting the need for robust discovery and faithful cross-file composition mechanisms in future agentic QA systems.
[NLP-31] A Shared Subcircuit Lets LLM s Count Down Across Tasks
【速读】： 该论文旨在解决语言模型在执行需精确追踪剩余标记数（token count）的任务时的机制问题，例如生成恰好十二个词的句子、在正确密码子处终止DNA序列或格式化ASCII表格。其解决方案的关键在于发现并验证了Llama-3.1-70B-Instruct模型中一种通用的“倒计时子电路”（countdown subcircuit）机制：该子电路通过比较当前位置与目标长度，估算到达目标所需的时间（即剩余token数）。研究首先在受控环境下隔离出该子电路，随后分析其表征几何结构，发现其使用与前沿大模型在另一任务中识别到的相同模式，表明该机制具有跨模型共享性。进一步通过无监督探测在自然语言数据集上揭示了该子电路被用于多种任务，包括目标长度需从上下文推断的情况。研究表明，逆向工程子电路有助于理解模型行为如何从单一示例泛化至多样化任务甚至不同模型。
链接: https://arxiv.org/abs/2607.12279
作者: Jacob Dunefsky,Wes Gurnee,Emmanuel Ameisen
机构: Yale University (耶鲁大学); Anthropic (Anthropic)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 12 pages, 11 figures
Abstract:Writing a sentence of exactly twelve words; ending a DNA sequence at the right codon; formatting an ASCII table. These are all tasks that language models can do that requires tracking how many tokens remain before a target. In this work, we identify in Llama-3.1-70B-Instruct a general mechanism for performing these tasks: a “countdown subcircuit” that compares the current position to a goal length and estimates the time remaining until then. We first isolate a countdown subcircuit in a controlled setting, in which the model is tasked with writing a fixed-length sentence ending in a specified word. We then investigate the geometry of the representations used by the subcircuit, and find that the subcircuit uses an identical motif previously identified in a frontier LLM on a separate task, thus suggesting that this motif is shared across models. Finally, we use unsupervised probing on a natural language dataset to find a variety of other tasks where this subcircuit is used, including tasks where the goal length is inferred from context rather than explicitly stated. Our work suggests that reverse-engineering subcircuits allows us to understand how behaviors generalize from a single example to many different tasks and even models.
[NLP-32] Code-MUE: Measuring Code LLM s Uncertainty through Execution-based Semantic Interaction Graphs ISSTA
【速读】： 该论文旨在解决生成式代码大模型（Code Large Language Models, Code LLMs）在实际软件工程应用中因固有的随机性所带来的可靠性问题，尤其关注如何有效识别模型预测中的不确定性，以避免由微小错误引发的功能、安全或安全性灾难。现有不确定性估计方法存在明显局限：白盒与灰盒方法难以应用于闭源模型，而传统的黑盒文本度量指标无法准确反映代码的语义脆弱性——即语法上的微小变化未必导致语义差异。为弥合语法与语义之间的鸿沟，论文提出一种完全基于黑盒的框架Code-MUE，其核心创新在于通过执行层面的语义交互图（Semantic Interaction Graph）来衡量不确定性，将不确定性建模为可观测运行时行为的函数，并利用冯·诺依曼熵（Von Neumann entropy）量化解空间的全局语义多样性。大规模实证研究显示，Code-MUE与功能正确率之间表现出极强的负相关性（斯皮尔曼相关系数高达-0.98），显著优于基于词法和嵌入的基线方法，且在实际工作流中具备可靠的错误风险检测与选择性预测能力。因此，该方案的关键在于以运行时语义行为为基础构建不确定性度量，从而实现对代码生成质量的精准评估。
链接: https://arxiv.org/abs/2607.12273
作者: Xiaoning Ren,Yinxing Xue,Lei Ma,Yuheng Huang
机构: Xi’an Jiaotong University (西安交通大学); Institute of AI for Industries, Chinese Academy of Sciences (中国科学院人工智能产业研究所); The University of Tokyo (东京大学); University of Alberta (阿尔伯塔大学)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: To appear at The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2026
Abstract:As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, where even minor errors can lead to severe functional, security, or safety consequences. Reliable automation, therefore, demands the ability to distinguish between confident, well-supported predictions and stochastic guessing. However, existing uncertainty estimation methods face a critical gap: white and grey-box techniques are often inapplicable to closed-source models, while standard “black-box” text metrics fail to capture the unique fragility of code, where syntactic variation does not always imply semantic divergence. To bridge this syntax-semantics gap, we introduce Code-MUE, a purely black-box framework that measures uncertainty through execution-based Semantic Interaction Graphs. Unlike prior approaches that rely on superficial textual similarity, Code-MUE grounds uncertainty in observable runtime behavior, calculating the Von Neumann entropy of the solution space to quantify global semantic diversity. A large-scale empirical study across eight state-of-the-art LLMs demonstrates that Code-MUE achieves a strong negative correlation with functional correctness (Spearman’s correlation up to -0.98), significantly outperforming lexical and embedding-based baselines while enabling robust risk detection and selective prediction in practical workflows.
[NLP-33] FinResearchBench II: A Deep Research Benchmark with Consensus-Derived Gold Rubrics for Distinguishing Financial Report Quality
【速读】： 该论文旨在解决生成式金融深度研究报告在大规模评估中因依赖人工专家制定与执行高质量评价标准（rubric）而产生的瓶颈问题。其核心解决方案的关键在于构建一个无需人类专家参与最终环节的可扩展自动化评估流水线：首先基于104个真实用户查询生成14,450个针对特定查询的候选评价标准，随后通过三模型（LLM）评判小组对部分样本进行对比验证，证明大语言模型（Large Language Model, LLM）在评价一致性上已达到与人类专家高度吻合的程度（联合一致项标签级一致性达98.67%），从而支持以LLM替代人工执行评价；在此基础上，采用严格一致性过滤器（仅保留三模型完全一致的评价标准）和可区分性过滤器（确保评价标准能在不同系统间产生至少一个多数“是”与一个多数“否”的判断），筛选出最终3,687个通过一致性的评价标准中剩余2,600个具备区分能力的共识黄金评价标准（consensus-derived gold rubrics）。利用该评价体系，实现了对10个深度研究系统在项目层面清晰的差异化排名（通过率范围22.23%–58.58%），并显著提升了评估流程的可扩展性，为未来基准测试、自动系统比较及基于评估驱动的模型优化提供了可持续的技术路径。
链接: https://arxiv.org/abs/2607.12252
作者: Beidi Luan,Rui Sun,Sinuo Wang,Yan Gu,Chao Li,Zhenliang Xiong,Jing Li,Zuo Bai
机构: StepFun(步骤科技); FinStep(金融步骤); University of Adelaide(阿德莱德大学); Shanghai Jiao Tong University(上海交通大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Deep research agents are increasingly used to produce long-form financial reports, yet large-scale evaluation remains bottlenecked by the need for human experts to define and execute high-quality rubrics. We address this problem by proposing a scalable pipeline for generating high-quality rubrics without human experts in the final loop. We build a financial deep research benchmark from 104 real-world user queries and automatically synthesize 14,450 query-specific candidate rubrics from model-generated reports. To justify removing human experts from rubric execution, we compare rubric judgments from three human experts with those from a three-LLM judge panel on a sampled subset, and show that LLM-based evaluation is sufficiently consistent with human evaluation to replace it for large-scale rubric screening, including 98.67% label-level agreement on jointly unanimous items. We then derive consensus-derived gold rubrics through two filters: a strict consistency filter, which keeps a rubric only if the three LLM judges unanimously agree on every report under the same query, and a distinguishability filter, which keeps a rubric only if it assigns at least one majority-yes and at least one majority-no label across the evaluated systems. This process retains 3,687 consistency-passed rubrics, of which 2,600 remain distinguishable and form the final set of consensus-derived gold rubrics. Using this final rubric set, we obtain clearly differentiated rankings across 10 deep research systems, with item-level pass rates ranging from 58.58% to 22.23%. More broadly, because the pipeline removes human-expert execution from rubric generation and evaluation, it is naturally scalable for benchmark evaluation, automatic system comparison, and future studies of evaluation-driven system improvement.
[NLP-34] Speculate with Memory: Lossless Acceleration for LLM Agents
【速读】： 该论文旨在解决生成式 AI（Generative AI）在大型语言模型（LLM）智能体中进行推测执行（speculative execution）时，现有推测器因无状态特性导致无法积累经验、预测质量难以随任务迭代提升的问题。其核心解决方案是为推测器引入三种在线记忆系统：对比转移表（contrastive transition table）用于建模动作序列的统计规律，情景记忆（episodic memory）用于检索上下文相似的历史片段，以及混淆追踪器（confusion tracker）用于抑制重复出现的错误。这些记忆机制使推测器能够从过往智能体轨迹中持续学习，从而显著提升预测准确性。实验表明，在六项基准测试上，该方法在动作预测任务中实现19–39%的相对准确率提升，观察预测任务中最高达2.5倍的性能增益，尤其在具有重复动作空间的任务中表现突出；且性能随记忆积累持续增强，对不同成本的推测器模型均具备良好的泛化能力。由于所有推测操作均在环境空闲时段执行，不增加实际运行时间（wall-clock cost），因此整个过程为无损执行，智能体的轨迹与非推测执行完全一致。
链接: https://arxiv.org/abs/2607.12236
作者: Yu Li,Qinyuan Ye,Prafulla Kumar Choubey,Jiaxin Zhang,Chien-Sheng Wu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Speculative execution accelerates LLM agents by using a smaller, cheaper model to predict and pre-launch the next step while the environment is idle. However, existing speculators are stateless and discard all information between tasks, preventing prediction quality from improving with experience. We equip the speculator with three online memory systems that learn from past agent trajectories: a contrastive transition table tracking action-sequence statistics, an episodic memory retrieving contextually similar segments, and a confusion tracker suppressing recurring errors. We evaluate this approach on six benchmarks spanning three speculation types: action prediction, observation prediction, and chained prediction. Memory-augmented speculation yields a 19–39% relative accuracy improvement on action prediction and up to a 2.5\times increase on observation prediction tasks with repetitive action spaces. These gains grow continuously as memory accumulates and generalize across speculator models of varying cost. All speculation is lossless because it runs during idle time at zero added wall-clock cost, and the actor’s trajectory is identical to non-speculative execution.
[NLP-35] Fin-Analyst at FinMMEval 2026 Task 3: A Live Hybrid Trading Agent with LLM Specialists and Rule-Based Signals
【速读】： 该论文旨在解决生成式 AI 在金融交易中的应用局限性问题，特别是大语言模型（Large Language Model, LLM）交易代理在实际市场部署中仍集中于美国股票市场且缺乏真实环境验证的瓶颈。其核心解决方案是提出一个混合型智能体 Fin-Analyst，采用多专家协同架构：针对特斯拉（TSLA）构建由新闻、SEC文件、基本面数据、分析师预测、技术指标与社交情绪等八类信号组成的 LLM 流水线，通过元智能体（Meta-Agent）进行整合决策；针对比特币（BTC）则设计轻量级基于规则的三信号投票机制。实验结果显示，在 2026 年 FinMMEval 2026 任务 3 的官方排行榜上，Fin-Analyst 在 TSLA 上实现 +13.51% 的超额收益，较买入并持有策略高出 28.33 个百分点，夏普比率达 4.10，胜率高达 88%。而 BTC 策略虽表现持平但显著优于大幅下跌的基线。消融实验表明，事件驱动的 8-K 公告是影响特斯拉股价最关键的信号。错误分析揭示了无记忆机制的代理会持续重复错误判断，且固定阈值规则在震荡市中因误判噪声而亏损，而基于 LLM 的流水线在相同条件下仍能获利，由此推动开发兼具记忆感知能力与生成式推理优势的下一代统一智能体，以应对复杂动态市场的挑战。
链接: https://arxiv.org/abs/2607.12233
作者: Mohotarema Rashid,Lingzi Hong,Junhua Ding,K.S.M.Tozammel Hossain
机构: University of North Texas (北德克萨斯大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 14 pages, 7 tables, 1 figure. CLEF 2026 FinMMEval Task 3 Working Notes
Abstract:Large language model (LLM) trading agents show promising performance in equity markets, yet remain narrowly focused on US equities with little evidence from live deployment. We present Fin-Analyst, a hybrid agent for FinMMEval 2026 Task 3: an eight-specialist LLM pipeline over news, SEC filings, fundamentals, analyst forecasts, technical indicators, and social sentiment, aggregated by a Meta-Agent for Tesla (TSLA), and a lightweight rule based three-signal vote for Bitcoin (BTC). On the final official leaderboard (accessed 2026-07-05), Fin-Analyst ranks first of all agents on TSLA with a +13.51% return, +28.33 points over Buy-and-Hold (Sharpe 4.10, 88% win rate), while the BTC vote ends flat yet well above a sharply falling baseline. Relative to the interim performance, the asset ranking reversed, indicating that short live windows yield volatility-sensitive rankings. Ablation identifies event-driven 8-K disclosures as the most influential TSLA signal. Error analysis shows that the memoryless agents repeat wrong calls for days at a time, and that the fixed-threshold BTC rules lost money by trading on noise in a sideways market while the LLM pipeline gained under similar conditions, motivating a memory-aware, LLM-based successor for both assets.
[NLP-36] Comparing Semantic Navigation in Humans and Large Language Models using Natural Language Processing
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在语义记忆检索过程中与人类认知机制之间差异的本质问题，特别是探讨当前大型语言模型（Large Language Models, LLMs）在概念空间中的搜索行为是否能够真实模拟人类的语义搜索动态。其核心解决方案的关键在于引入基于轨迹的自然语言处理（NLP）度量方法，从三个互补维度量化语义搜索过程：熵（entropy，反映步长可预测性）、到下一项目距离（distance to next，表征连续语义跳跃的幅度）以及到中心点距离（distance to centroid，衡量全局分散程度）。研究通过对比82名人类被试与三种主流大模型（GPT-4o、Gemini-2.5-Pro、Claude-Sonnet-4.5）在八种温度设置下的生成序列，发现人类表现出更高的熵、更大的语义跳跃步长和更广的全局分布，表明其搜索更具变异性与探索性。尽管温度调节可在特定参数下使部分指标与人类趋同，但无任何配置能同时匹配所有维度的人类特征。这一结果揭示了人类语义搜索中局部利用与全局探索之间的独特平衡机制，而现有模型架构尚无法复现这种复杂的行为模式。
链接: https://arxiv.org/abs/2607.12195
作者: Gabriel Paris-Colombo,Rodrigo M. Cabral-Carvalho,Felipe D. Toro-Hernández
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Cogsci paper 2026
Abstract:Semantic memory retrieval can be conceptualized as navigation through conceptual space. We compared semantic search dynamics between humans and three large language models (GPT-4o, Gemini-2.5-Pro, Claude-Sonnet-4.5) using verbal fluency data. By applying trajectory-based NLP metrics to the items generated by 82 human participants and LLM output across eight temperature settings, we quantified three complementary dimensions: entropy (step size predictability), distance to next (successive semantic steps), and distance to centroid (global dispersion). Humans exhibited higher entropy, larger semantic steps and broader dispersion than all LLMs, indicating more variable and exploratory search. Temperature tuning produced only partial alignments, as individual metrics matched between humans and LLMs at specific settings, but no configuration reproduced the complete human profile (in all dimensions). These findings suggest that human semantic search implements a distinctive balance between local exploitation and global exploration that current model architectures fail to reproduce.
[NLP-37] Entropy in Semantic Memory Navigation in Blind and Sighted Individuals: The Effect of Visual Experience
【速读】： 该论文旨在解决“概念知识在语义记忆中的组织与动态导航是否依赖于视觉经验”这一核心问题。传统具身认知理论认为，语义记忆的形成与存储依赖于感知运动系统，而先天盲人群体为检验视觉经验是否为概念表征必要条件提供了关键实证场景。研究采用属性列举任务，比较盲人与视力正常者对具体概念和抽象概念的语义记忆导航差异，并引入基于嵌入的自然语言处理指标——语义熵（semantic entropy），以量化概念检索的可预测性。结果显示：视力正常被试对抽象概念的语义熵显著高于具体概念，表明其语义网络具有更复杂的动态结构；而盲人被试则未表现出此差异，反而对视觉显著性高的具体概念（如企鹅）表现出更高的语义熵，暗示其语义组织更依赖非视觉感官特征或语义关联强度。因此，本研究的关键发现在于：视觉经验在语义记忆的结构化组织及其动态导航中起关键作用，缺乏视觉输入导致语义网络的重构，其导航模式不再遵循典型“抽象-具体”梯度，而是由概念的多模态可及性决定。
链接: https://arxiv.org/abs/2607.12185
作者: Felipe D. Toro-Hernández,Rodrigo Lagos,Sergio E. Chaigneau
机构: 未知
类目: Computation and Language (cs.CL)
备注: Cogsci paper 2026
Abstract:Embodied accounts of semantic memory highlight the role of sensorimotor systems in acquiring and storing knowledge. Congenitally blind populations offer a critical test bed for these assumptions, providing an opportunity to assess whether conceptual grounding requires visual experience. In this study, we assessed semantic memory navigation differences between blind and sighted individuals using a property listing task with concrete and abstract concepts. We computed semantic entropy, an embedding-based natural language processing metric that captures the predictability of retrieval. Generalized linear mixed models revealed distinct navigation patterns across groups: while sighted individuals showed higher entropy for abstract than concrete concepts, blind participants did not. Instead, blind individuals exhibited higher entropy for visually salient concrete concepts (e.g., penguin). These results underscore the role of visual experience in the organization and dynamic navigation of semantic memory.
[NLP-38] We Hebben Een Serieus Translatie: Modeling Intercomprehension as Probabilistic Inference
【速读】： 该论文旨在解决零样本跨语言理解（intercomprehension）的机制问题，即母语者如何在不掌握目标语言的情况下，通过与自身母语相关的语言实现对陌生语言（L2）的部分理解。其核心挑战在于建模在缺乏直接语言知识条件下，人类如何基于语言间的形态相似性或语义关联进行灵活推断。解决方案的关键在于提出一个基于贝叶斯框架的算法模型：该模型仅使用源语言（L1）的语言模型（LM）对潜在翻译假设进行评分，并结合通用噪声模型，依据形式相似性或符号规则推断L2与L1词汇之间的映射关系。通过在英语、西班牙语和俄语母语者对荷兰语、意大利语和乌克兰语语句的跨语言理解任务中开展行为实验，验证了该模型在拟合人类实际理解表现方面优于多个简化版本，且在零样本提示（zero-shot prompting）下显著优于更大规模模型。研究结果表明，该模型能够以认知上合理的方式模拟人类在真实跨语言场景中的不确定性下的灵活推理，为理解语言共性与认知适应机制提供了可计算的理论支持。
链接: https://arxiv.org/abs/2607.12169
作者: Thomas Hikaru Clark,Edward Gibson,Roger Levy
机构: MIT (麻省理工学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Intercomprehension refers to partial intelligibility of an unfamiliar language (L2) by a speaker of a related language (L1). How is this zero-shot cross-language comprehension possible? In this work, we extend past work on algorithmic models of noisy-channel inference to model intercomprehension in a Bayesian framework. The model uses an LM in L1 only for scoring latent hypotheses about the translations of observed L2 utterances, and a general-purpose noise model to infer a mapping between L2 and L1 words based on either form-based similarity or symbolic rules. We then conduct a human behavioral experiment, eliciting inferences for utterances in Dutch, Italian, and Ukrainian from speakers of English, Spanish, and Russian, respectively. Our full model shows a closer alignment to the distribution of human intercomprehension performance than ablations, and also compares favorably to zero-shot prompting of much larger models. These results provide a cognitively plausible computational model of intercomprehension, and highlight the flexible inferences made by comprehenders under wide uncertainty in real-world cross-language scenarios. We share our code publicly.
[NLP-39] oken Reduction Is Not Cost Reduction
【速读】： 该论文旨在解决生成式 AI（Generative AI）在编程代理（coding agent）应用中，现有上下文压缩技术（如命令-输出压缩器、检索排序器和负载优化代理）普遍以“减少文本量”作为评估标准，但这一指标与实际计费成本降低之间缺乏可靠关联的问题。核心问题在于：在不损害任务成功率或延长执行轨迹的前提下，何时真正实现计费成本的降低？ 其解决方案的关键在于提出一种新的评估范式——基于任务成功率调整后的实际计费成本（success-adjusted billed cost），而非单纯依赖于令牌（token）数量的削减。研究通过大规模、受控的实证实验（2,908次经提供商计费的Claude Code运行，分析2,848次），发现提示缓存流量占总成本约80%，而工具输出缩减并未显著降低计费成本，甚至可能因移除关键操作证据导致任务失败率上升。因此，该研究强调应以实际成本效益与任务完成质量为核心，建立分层的证据标准，推动更精准、有效的上下文压缩策略设计。
链接: https://arxiv.org/abs/2607.12161
作者: Sarel Weinberger,Amir Hozez
机构: PointFive(点五)
类目: Computation and Language (cs.CL)
备注:
Abstract:Context-reduction layers for API-based coding agents, including command-output compressors, retrieval rankers, and payload-optimizing proxies, are usually evaluated by how much text they remove. We ask instead: when does reducing retrieved context or tool output lower the actual billed cost of a coding agent without reducing task success or lengthening its trajectory? Our primary evidence is a pre-specified, hash-frozen, paired campaign of 2,908 provider-billed Claude Code runs, of which 2,848 were analyzed, covering 103 tasks, seven repositories, and three models. The campaign compared a baseline with two generations of hook-based compression and an API-boundary proxy, within a broader measured program of roughly 5,500 billed executions. Three findings emerge. First, prompt-cache traffic dominated cost composition. Cache creation and reads accounted for about 87% of reconstructed four-component cost, or about 80% of the actual bill, with an 8.7% dollar-weighted residual that retained telemetry could not attribute. On Haiku 4.5, this residual scaled with thinking effort. Second, tool-output reduction did not reliably predict billed-cost reduction. An arm that removed 38% of estimated raw tool-output tokens had 6.8% higher paired cost (95% CI: +2.8% to +11.3%), while per-task reduction was only weakly associated with cost change (Pearson r = 0.15, CI crossing zero). Third, compression can harm task completion by removing action-critical evidence. In a small single-shot study on SWE-bench-derived Go tasks, compression reduced patch application from 27/40 to 15/40 by corrupting verbatim edit anchors, and the compressed grounded arm produced fewer solves at higher observed cost per solve. We propose a layered evidence standard centered on success-adjusted billed cost rather than token reduction alone. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2607.12161 [cs.CL] (or arXiv:2607.12161v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.12161 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sarel Weinberger [view email] [v1] Mon, 13 Jul 2026 21:10:22 UTC (177 KB) Full-text links: Access Paper: View a PDF of the paper titled Token Reduction Is Not Cost Reduction, by Sarel Weinberger and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CL prev | next new | recent | 2026-07 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[NLP-40] he Capacity of Thought: Benchmarking Llama 3.2 in Semantic fMRI Neural Language Decoding and Improving the Huth Encoding-Model Baseline
【速读】： 该论文旨在解决从功能性磁共振成像（fMRI）信号中解码连续语言这一非侵入式脑机接口（BCI）领域的核心挑战。其解决方案的关键在于：一方面，通过扩展体素选择范围（10K–15K）、将生成式语言模型由GPT-1替换为更高容量的GPT-2 Medium以优化束搜索提议机制，并采用GPU加速的自举训练策略，显著提升了基于岭回归编码模型的解码性能，实现对未见叙事文本的平均METEOR得分0.149与BLEU-1得分0.200，较基准模型提升11%相对性能；另一方面，提出fMRIFlamingo框架，将血氧水平依赖（BOLD）信号映射至冻结的Llama-3.2-1B语言模型，结合可训练的门控交叉注意力层及学习型脑部分词器与Perceiver Resampler，试图建立神经活动与语言表征间的映射。然而，关键发现表明，尽管该模型在100选1的分类任务中达到42.86%的Top-1准确率，远超随机水平，但当输入的fMRI信号被置零时，性能几乎不变，揭示其“解码成功”主要依赖于冻结语言模型的先验知识而非真实神经信号。这表明高容量语言模型在fMRI解码中并不必然提升性能，且可能因缺乏严格的盲控验证而掩盖模型失败的本质。
链接: https://arxiv.org/abs/2607.12079
作者: Milos Suvakovic,Dom Marhoefer,Glenn Grant-Richards,Aidan Pinero
机构: UC Santa Cruz (加州大学圣克鲁斯分校)
类目: Computation and Language (cs.CL)
备注: 7 pages, 2 tables, 2 figures. Preprint. NLP 244: Advanced Machine Learning for Natural Language Processing report, UC Santa Cruz
Abstract:Decoding continuous language from fMRI signals remains a core challenge in non-invasive brain-computer interface research. We present two complementary investigations. First, we improve the Huth et al. ridge regression encoding pipeline through expanded voxel selection (10K-15K), substitution of GPT-2 medium for GPT-1 as the beam-search proposal model, and GPU-accelerated bootstrap training, achieving mean METEOR = 0.149 and BLEU-1 = 0.200 across three held-out narratives for subject UTS03 – an 11% relative METEOR gain over our replication baseline. Second, we introduce fMRIFlamingo, which maps BOLD activity to a frozen Llama-3.2-1B with trainable gated cross-attention layers via a learned brain tokenizer and a Perceiver Resampler. Despite achieving 42.86% Top-1 accuracy on a 1-in-100 ranking task, well above chance, a blind control ablation with zeroed fMRI inputs yields near-identical scores, revealing that apparent decoding success is driven primarily by the frozen language prior rather than by neural input. These results demonstrate that high-capacity language models do not inherently improve fMRI decoding and can actively obscure failures without rigorous blind-control evaluation.
[NLP-41] Beyond Parallel Tracking: Interactive Multi-Feature Fusion Drives Semantic Reconstruction from Non-invasive Brain Recordings
【速读】： 该论文旨在解决非侵入性神经信号中语义重建面临的表征不匹配问题，即语义特征空间与神经编码模式之间的不一致，这一问题严重阻碍了高噪声神经信号与目标语义特征之间的跨模态对齐。现有语义解码方法多依赖静态词法表征或动态上下文表征的单一维度处理，导致信息损失严重，未能充分模拟人脑整合稳定词属性与动态上下文的能力。为此，本文提出一种多特征融合框架，系统比较线性朴素拼接（Naive Concatenation）与非线性多头交叉注意力（Multi-Head Cross-Attention）两种融合策略，通过交互式门控机制将静态词法表征（W2V）与动态上下文表征（GPT）进行协同整合，以实现语言信息的联合建模。实验结果表明，该框架在语义重建与文本生成任务中展现出显著性能优势，呈现出“交叉注意力 > 拼接 > GPT > W2V”的性能层级。关键创新在于采用非线性交叉注意力机制，有效模拟了上下文信息与核心词义属性之间的协同调制关系，突破了传统孤立使用单一特征的局限，实现了当前最先进的非侵入性脑-文本解码性能，为生成式脑机接口提供了可行的技术路径。
链接: https://arxiv.org/abs/2607.12071
作者: Boda Xiao,Xiran Xu,Songyi Li,Yujie Yan,Xihong Wu,Heping Cheng,Jing Chen
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Continuous semantic reconstruction from non-invasive neural recordings remains limited by the representational mismatch between semantic feature spaces and neural coding patterns, which severely impedes cross-modal alignment between high-noise neural signals and target semantic features. Prior semantic decoders have predominantly relied on static lexical representations or dynamic contextualized representations in isolation. This single-dimension approach inevitably leads to severe information loss, as it fails to account for the human brain’s capacity to integrate stable word attributes and dynamic contexts this http URL bridge this gap, this study introduces a multi-feature fusion framework for non-invasive semantic reconstruction, systematically benchmarking two integration approaches: linear Naive Concatenation and non-linear Multi-Head Cross-Attention. Within this framework, our approach complements static lexical representations (W2V) with dynamic contextual representations (GPT) via an interactive gating mechanism to facilitate cooperative processing during language this http URL through extensive semantic reconstruction and text generation experiments, our framework reveals a robust performance hierarchy: Cross-Att Concat GPT W2V. Crucially, the non-linear cross-attention fusion method achieves state-of-the-art performance, demonstrating that neural language decoding benefits from simulating the collaborative modulation between contextual information and core lexical attributes rather than depending on isolated individual features, while also offering a viable non-invasive brain-to-text decoding method.
[NLP-42] Agent ic systems for breast cancer treatment recommendations
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在复杂乳腺癌治疗方案制定中可靠性不足的问题，尤其关注其在临床决策支持场景下的实际应用效能。其核心挑战在于：现有大语言模型（LLM）在缺乏真实临床决策信息的情况下，难以生成准确、可信赖且符合医学规范的治疗建议。为应对这一问题，研究提出并评估了基于不对称信息评分生成（Asymmetric Information Rubric Generation, AIRG）的评估框架，通过构建仅对评分者开放真实临床决策的评价标准，客观衡量模型表现。关键解决方案是设计并比较七种不同架构的智能体系统（agentic LLM systems），涵盖单模型基线、工具增强型系统以及具备事实核查与自主子代理生成能力的多智能体架构。实验结果表明，采用Claude Opus 4.8与DC+SA（Dynamic Coordination + Subagent Autonomy）管道的配置表现最优，全局得分为0.594 ± 0.025，但工具使用与智能体自主性的提升在不同情境下效果不一，存在性能波动。此外，系统在不同临床领域和疾病分期中的表现差异显著，且由肿瘤科医生主导的错误分析揭示了诸多临床上关键的缺陷，包括推荐遗漏或错误、论证逻辑瑕疵、引用错误、过时信息及过度自信等问题。这表明尽管智能体式生成式AI能够生成具有临床相关性的乳腺癌治疗建议，但仍不足以支持无监督的临床部署。
链接: https://arxiv.org/abs/2607.12051
作者: Vinicius Anjos de Almeida,Nícolas Henrique Borges,Leonardo Vicenzi,Helena Kociolek,Sarah Miriã de Castro Rocha,Frederico Nassif Gomes,Júlia Cristina Ferreira Ribeiro,Lucas Emanuel Silva e Oliveira
机构: Spesia; Faculdade de Medicina, Universidade de São Paulo (圣保罗大学医学院); Laboratory of Artificial Intelligence Applied to Bioinformatics, SEPT, Universidade Federal do Paraná (UFPR) (巴拉那联邦大学生物信息学人工智能实验室); Pontifícia Universidade Católica do Paraná (PUCPR) (天主教巴拉那大学); Universidade Federal do Paraná (UFPR) (巴拉那联邦大学)
类目: Computation and Language (cs.CL)
备注: Under peer review. Source code available at: this https URL
Abstract:Large language models (LLMs) are increasingly being explored for clinical decision support, but their reliability in complex oncology treatment planning remains unclear. We evaluated agentic LLM systems for breast cancer treatment recommendation generation using 72 real clinical cases across stages I to IV and 1,147 case-specific rubrics generated through Asymmetric Information Rubric Generation (AIRG), in which the rubric generator had access to real clinical decisions unavailable to the evaluated models. Seven pipelines were compared, including single-LLM baselines, tool-augmented systems, and multi-agent architectures with fact checking and autonomous subagent spawning. The best-performing configuration, Claude Opus 4.8 with the DC+SA pipeline, achieved a global score of 0.594 \pm 0.025. Tool use and increased agent autonomy had mixed effects, improving performance in some settings but degrading it in others. Performance varied by clinical domain and disease stage, and oncologist-led error analysis revealed persistent clinically relevant failures, including incorrect or missing recommendations, flawed justifications, citation errors, outdated claims, and overconfidence. These findings suggest that agentic LLM systems can generate clinically relevant breast cancer recommendations, but remain insufficient for unsupervised clinical use.
[NLP-43] An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering
【速读】： 该论文旨在解决在真实临床场景中部署医学视觉问答（MedVQA）系统时，模型如何在持续学习新任务的同时避免遗忘已有知识的关键挑战。其核心问题是：现有持续学习（Continual Learning, CL）方法在面对具有异构目标（如分类、多标签分类、检测、细胞计数和报告生成）与不同监督格式的医学视觉-语言任务时，难以有效平衡模型的稳定性与可塑性。解决方案的关键在于系统评估多种CL方法在多样化临床任务序列中的表现，重点分析三方面问题：（1）缓解灾难性遗忘的能力；（2）对任务顺序的敏感性，揭示不同任务排列对性能保持与遗忘的影响；（3）低秩适配参数随任务演进的动态变化，揭示不同CL策略下的权重漂移模式。研究发现，当前主流CL方法在处理目标与监督形式差异较大的任务组合时，难以维持理想的稳定-可塑性权衡，凸显了面向异构医学任务的新型持续学习机制的必要性。
链接: https://arxiv.org/abs/2607.12048
作者: Mai A. Shaaban,Tausifa Jan Saleem,Alaa Mohamed,Dilnaz Utemissova,Ufaq Khan,Mohammad Yaqub
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired knowledge. Continual learning (CL) provides a practical framework for this setting. Despite rapid progress in medical vision-language models, the behavior of CL methods when training these models across heterogeneous MedVQA tasks remains underexplored. This work presents a systematic evaluation of CL for MedVQA across diverse clinical objectives, including classification, multi-label classification, detection, cell counting, and report generation. Specifically, we explore (1) the ability of existing CL methods to mitigate catastrophic forgetting; (2) their sensitivity to task ordering, analyzing how different task sequences influence performance retention and forgetting; and (3) the evolution of low-rank adaptation parameters as new tasks are learned, revealing patterns of weight drift under different CL methods. Our findings suggest that existing CL methods struggle to maintain stability-plasticity balance when tasks with different objectives and supervision formats are interleaved. Code and full experimental setup will be publicly available.
[NLP-44] Sparse Inter-Layer Dependencies of Transformer FFN Neurons
【速读】： 该论文旨在解决Transformer架构中前馈网络（Feedforward Network, FFN）模块内部结构难以解释的问题，其核心挑战源于残差流（residual stream）引起的激活叠加效应，导致神经元间依赖关系复杂且不透明。为应对这一问题，研究提出了一种无需训练的归因方法，用于估计上游神经元激活和注意力输出对目标神经元激活的相对影响。该方法的关键在于通过系统性地掩码（masking）非关键输入并保留平均值，量化少量前置激活与注意力输出对目标神经元激活的贡献度。实证结果表明，在不同模型与层间，仅需极小数量的前置激活与注意力输出即可高保真地重构目标神经元的激活状态；当进一步考虑上游层固有的激活稀疏性时，有效稀疏性显著提升。更重要的是，若在所有层中同步应用针对特定神经元的稀疏掩码，使扰动沿网络传播，模型困惑度在中等稀疏水平下仍保持基本不变。这些发现表明，尽管FFN具有密集参数化特性，但在神经元层级上仍存在稀疏且结构化的跨层依赖关系。所提出的归因方法为电路级可解释性分析提供了高效、可扩展的工具，并识别出具备潜在高效推理意义的稀疏路径。
链接: https://arxiv.org/abs/2607.11990
作者: Johannes Knittel,Hanspeter Pfister
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Feedforward network (FFN) blocks account for a large fraction of the parameters and computation in Transformer architectures, yet their internal structure remains difficult to interpret due to the additive superposition induced by the residual stream. We examine whether the activation of an FFN neuron can be explained by a sparse set of preceding neuron activations and attention outputs. We introduce a training-free attribution method that estimates the relative influence of upstream neurons and attention outputs on a target neuron’s activation. Empirically, across models and layers, we find that small subsets of preceding activations and attention outputs suffice to preserve neuron activations with high fidelity when all remaining inputs are masked with their average values. Effective sparsity is even greater when accounting for the inherent activation sparsity of upstream layers. Moreover, applying the neuron-specific masks in all layers simultaneously, such that the induced deviations propagate through the network, leaves model perplexity largely unchanged at moderate sparsity levels. These results demonstrate that, despite dense parameterization, FFNs exhibit sparse and structured inter-layer dependencies at the neuron level. Our method provides a practical, scalable tool for circuit-level interpretability and identifies candidate sparse pathways with potential implications for efficient inference.
[NLP-45] Evaluating Nonuniform Dependability Across Response Conditions: A Conditional Generalizability Framework Illustrated in Automated Essay Scoring
【速读】： 该论文旨在解决传统概化理论（Generalizability Theory, G-theory）在评估测量设计可靠性时，因聚合性估计掩盖了不同响应条件下测量负担异质性的问题，导致单一的G-或D-研究可能无法准确反映设计在特定分层中的适用性。其核心解决方案在于提出一种条件概化框架（conditional generalizability framework），关键在于三个方面：首先，将自动化评分配置（如编码器架构与评分头族）视为固定流程内可接受的测量条件集合，而非偶然的建模选择；其次，通过比较分析性D-研究预测与对有限评分配置池的实证扫描结果，获得两个设计效度估计量，其一致性或差异性可用于诊断实际配置空间的特性；第三，基于熵定义的响应分层进行证据条件化，将熵作为操作性分层变量而非写作质量的构念声明。该框架有效应对了生成式AI在评分侧带来的配置复杂性问题，突破了以往仅关注生成题项变异的局限。以限时第二语言写作的自动作文评分为例，整体可靠性较高（Phi ≈ 0.76），但在按熵分层重估后，各层次可靠性虽保持高位但呈稳健下降趋势（Phi = 0.88, 0.87, 0.84），揭示出不同熵层级存在差异化的决策研究需求，尤其高熵层级需更多交叉条件。该框架提供了一种可迁移的流程，用于评估非均匀依赖性。
链接: https://arxiv.org/abs/2607.11981
作者: Yi Gui
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Aggregate reliability estimates can obscure heterogeneity in measurement-design burden across response conditions, so a single G- or D-study may mischaracterize a design’s adequacy for particular strata. This study introduces a conditional generalizability framework with three components. First, automated scoring configurations – the encoder architectures and scoring-head families admissible within a fixed pipeline – are treated as a universe of admissible measurement conditions rather than incidental modeling choices. Second, analytical D-study projections are compared with empirical configuration sweeps over a finite scoring pool, yielding two estimands of design adequacy whose agreement or divergence diagnoses the realized configuration universe. Third, evidence is conditioned on entropy-defined response strata, treating entropy as an operational stratification variable, not a construct claim about writing quality. Whereas recent generalizability-theory extensions address AI-generated item variants on the response side, this framework addresses the analogous scoring-side problem: AI-mediated scoring configurations. Demonstrated with automated essay scoring of timed L2 writing, the realized design was dependable in aggregate (Phi approx 0.76). Re-estimated within entropy strata, dependability stayed high but declined modestly and robustly (Phi = 0.88, 0.87, 0.84) – a gradient implying different decision-study requirements, the highest-entropy stratum requiring the most crossed conditions. The framework offers a portable workflow for evaluating nonuniform dependability.
[NLP-46] Optimization Is Not All You Need
【速读】： 该论文试图解决的问题是：当前以生成式AI（Generative AI）为代表的语言模型在追求文本流畅性与可预测性过程中，其背后的优化文化已悄然取代传统人类语义评判体系，导致语言生成的合法性标准被量化指标所主导，而这些指标无法区分文本的“错误”与“创造性”之间的本质差异。解决方案之关键在于揭示这一现象的深层根源——即从预训练、解码、偏好调优到基准测试与接口设计等环节所体现的“优化文化”（optimization culture），其核心信念是：沿着预设维度实现可测量的改进即意味着价值的完整实现。然而，这种信念忽视了语言生成中“不可测”的创造性维度，使本应具备判断力的系统（如损失函数、奖励模型、基准测试与提示工程）沦为无判断能力的执行工具，从而将语言合法性的裁决权交予缺乏伦理与语境理解能力的算法机制。
链接: https://arxiv.org/abs/2607.11977
作者: Minh Hua,Rita Raley
机构: Scale AI(规模人工智能); University of California, Santa Barbara(加州大学圣芭芭拉分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: This essay will be forthcoming in MFS Modern Fiction Studies, published by JHUP (Spring-Summer 2027)
Abstract:In 2019, OpenAI released two million GPT-2 outputs-ungrammatical, half broken-to aid the detection of machine-generated text. The alignment that produced their more fluent successors is usually regarded as an engineering achievement; we read it instead as the newest expression of optimization culture: the conviction, older than the technology, that measurable improvement along predefined axes exhausts the question of value. Tracing that conviction through the stack-pretraining, decoding, preference tuning, benchmarking, interface-and back through its genealogy in the audit society, we arrive at the limit: an optimization procedure can measure how improbable a piece of generated text is; it cannot tell whether that unlikelihood is error or invention. A procedure that cannot make that distinction has nonetheless, within half a decade, assumed the authority to set the protocols of legitimate language. Held for centuries by academies and schoolrooms, grammars and examiners, this authority has been given over to loss functions, reward models, benchmarks, and system prompts: an apparatus that executes the office of judgment with no capacity for judging.
[NLP-47] GRID: Grammar-Railed Decoding for Enterprise SQL Generation KDD2027
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLM）在企业级部署中生成SQL时面临的多重挑战：生成结果不仅需语法正确，还需满足基于角色的访问控制（Role-Based Access Control, RBAC）与模式级别的策略约束，具备可证明的可靠性（非依赖经验的近似保证），在生成长度增长时仍保持低延迟，并能提供符合合规要求的完整决策审计日志。其核心解决方案是提出一种名为GRID（Grammar-Railed Decoding）的语法约束解码引擎，其关键在于将下一词元的掩码（mask）精确地由解析器配置（词法扫描状态 × LALR(1) 栈）决定，而非传统的词元序列，同时利用增量推进的LALR(1)解析器作为有效前缀的可信预言机。通过字节级前缀树遍历（trie walk）实现LLM词元到语法终结符的映射，并引入上下文无关/上下文相关双分支设计，确保缓存键的语义保真性（soundness）天然成立。角色权限通过语法生产规则投影和模式词法库对标识符终结符的限制进行编译式实现，使得非法动词与标识符在掩码层面即不可达。该方法严格保证四项核心性质：语义保真性、完备性、终止性及近似恒定的每词元开销，并通过测试或基准验证。实验表明，使用Rust内核实现的每词元掩码计算中位时间仅为3.6–6.7微秒，在两种分词器上均优于llguidance，且零误拒；每词元防护开销在序列长度达16,000时仍保持位置不变。在Spider数据集上，约束解码使0.5B模型的执行准确率提升13个百分点，而一次检查器引导的修复流程即可将7B模型的可执行率提升至94.5%。此外，采用哈希链结构的每词元审计追踪支持比特级重放与100%篡改检测。论文明确指出掩码机制的局限性：无法保证分布忠实性、不适用于列级RBAC及非LALR(1)语言，并公开了实际测量的性能开销。
链接: https://arxiv.org/abs/2607.11951
作者: Mohsen Arjmandi
机构: evolutionID GmbH(进化ID公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Formal Languages and Automata Theory (cs.FL); Programming Languages (cs.PL)
备注: 18 pages, 3 figures. Extended version; a condensed version is under review at KDD 2027
Abstract:Large language models can write SQL, but enterprise deployment demands more than plausible text: outputs must be syntactically valid, must respect per-role and per-schema policy, must carry provable (not best-effort) guarantees, must not slow down as generations grow, and must leave a compliance-grade record of every decision. We present GRID (Grammar-Railed Decoding), a grammar-constrained decoding engine that keys exact next-token masks on parser configurations (lexer scan state x LALR(1) stack) rather than on token sequences, and uses the incrementally advanced LALR(1) parser itself as a viable-prefix oracle. LLM tokens are bridged to grammar terminals by a byte-level trie walk with a context-independent/context-dependent split that makes cache-key soundness hold by construction. Role-based access control is compiled into the language: role projections subset the grammar’s productions and schema lexicons restrict identifier terminals, so forbidden verbs and identifiers are unreachable at mask level. Four guarantees (soundness, completeness, termination, and near-constant per-token cost) are stated with explicit preconditions and each paired with a test or benchmark. Rust kernels bring the per-token mask to a 3.6-6.7 us median, ahead of llguidance at p50 and p90 on two tokenizers with zero false rejects; per-token guard cost is position-flat at n=16,000. On Spider, constrained decoding is worth +13 execution-accuracy points at 0.5B, and one checker-guided repair pass over the provably mask-unenforceable residue (column-level policy) lifts a 7B model to 94.5% executable. A hash-chained per-token audit trail replays bit-identically with 100% tamper detection. We state plainly what the mask cannot do (distribution faithfulness, column-level RBAC, non-LALR(1) languages) and where measured cost remains.
[NLP-48] Hybrid Continual Learning for Low-Resource Australian Aboriginal Language Identification INTERSPEECH2026
【速读】： 该论文旨在解决濒危澳大利亚原住民语言（AALs）在语音技术中应用时因数据极度稀缺导致的识别模型性能低下问题。由于高资源语言迁移学习常伴随灾难性遗忘，难以有效适配新语言，而传统持续学习（Continual Learning, CL）在极低数据量下仍面临挑战。为此，论文提出两种混合持续学习方法：增强型回放弹性权重巩固（Replay Augmented Elastic Weight Consolidation）与约束引导知识蒸馏（Constraint Guided Knowledge Distillation），以在微调预训练语音模型进行AAL识别的同时，有效保留先前已学知识。实验结果表明，所提方法在Warlpiri、Dalabon和Dharawal三种语言上均显著优于微调及现有CL基线，不仅提升了对多个AAL的适应能力，且保持了对先前高资源语言的性能表现。
链接: https://arxiv.org/abs/2607.11946
作者: Pravina Mylvaganam,Ting Dang,Eliathamby Ambikairajah,Vidhyasaharan Sethu,Jingyao Wu
机构: University of New South Wales, Australia(新南威尔士大学, 澳大利亚); University of Melbourne, Australia(墨尔本大学, 澳大利亚); Massachusetts Institute of Technology, USA(麻省理工学院, 美国)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Accepted by Interspeech 2026
Abstract:Language identification is an important step toward integrating endangered Australian Aboriginal languages (AALs) into speech technologies supporting language revitalisation and digital inclusion. However, extreme data scarcity limits model performance. Transfer learning from high-resource languages shows promise but often suffers from catastrophic forgetting when adapting to new languages. Continual learning (CL) can mitigate this issue, though it remains challenging with very limited data. To address this, we propose two hybrid continual learning methods: Replay Augmented Elastic Weight Consolidation and Constraint Guided Knowledge Distillation to adapt pretrained speech models for AAL identification while preserving previously learned knowledge. Experiments on Warlpiri, Dalabon and Dharawal show that the proposed methods outperform fine-tuning and existing CL baselines, improving adaptation to multiple AALs while maintaining performance on previously learnt high-resource languages.
[NLP-49] Belief-reality separation lives in routing over a shared value slot in language models
【速读】： 该论文旨在解决大语言模型在处理信念与现实分离任务时的内在机制问题，即模型如何在不混淆“角色信念”与“客观事实”的前提下准确回答相关问题。其核心解决方案的关键在于识别并验证两个可分离的计算机制：一是位于值槽（value slot）的通用赋值结构，用于绑定被赋予的信念内容；二是位于查询位置的路由机制（router），负责根据上下文选择从“角色信念”还是“现实”框架中读取信息。该机制通过两种路径填充值槽：一种是直接由文本显式提供的“陈述性信念”，另一种是需通过可见性门控回溯推断的“推导性信念”。研究发现，尽管两个路径共享同一值槽，但它们分别由独立训练的子空间控制，且仅推导性路径依赖于描述中的可见性信息。值得注意的是，值槽本身并不携带信念或现实的标签，干预该槽会同等影响信念和现实的输出，说明信念-现实的分离并非源于值槽，而是由一对解耦的路由子空间实现——这些子空间可在不注入源值的情况下，将查询在不同语义框架间切换。该发现已在三种不同架构上得到验证，并在去除心智理论基准测试中常见捷径后依然成立；该能力在模型规模从3B到7B之间逐渐显现，覆盖五个模型家族。本研究深入剖析了信念-现实轴的单一维度，而其配套论文进一步表明，相同的“值槽-路由”结构也适用于其他非现实语境（如反事实、虚构、时间情境）。
链接: https://arxiv.org/abs/2607.11945
作者: Oliver Steele,Jiangtao Wen,Yuxing Han
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 6 figures, 6 tables
Abstract:Capable language models hold what a character believes apart from what is true: told “Anna believes the cup is blue; in reality it is red,” they answer blue about Anna and red about the world. Where in the computation does that separation live? We show it rests on two separable mechanisms at two positions. A generic value slot binds the attributed value. A router at the query position selects which frame, the character’s belief or reality, a query reads out. Two routes fill the slot: an asserted belief, whose value the text supplies, binds in directly; a derived belief, whose value must be inferred from what the character could see, arrives by a visibility-gated lookback. A subspace trained on either route steers the other, and only the derived route depends on described visibility. The slot itself carries no belief-reality tag: intervening on it moves a reality readout as strongly as a belief one. The separation lives instead in a dissociated pair of routing subspaces, which flip a query between frames without injecting the donor’s value. These results hold across three architectures, on stimuli de-confounded against theory-of-mind-benchmark shortcuts; the behavior itself emerges between 3B and 7B across five model families. This paper develops the single belief-reality axis in depth; a companion paper shows the same slot-and-router format is shared across the other non-actual contexts a sentence can open (counterfactual, fictional, temporal).
[NLP-50] MAGE: Understanding Stability-Performance Trade-offs in Multi-component Prompt Optimization
【速读】： 该论文旨在探究迭代式提示优化（Iterative Prompt Optimization）中不同组件之间的相互作用机制，以及在组合使用时所产生的协同效应。其核心问题在于：当多个随机优化信号在封闭的反思循环中共同作用时，它们如何交互？解决方案的关键在于提出MAGE（Memory-Augmented Goal-directed Prompt Evolution）框架——一个用于系统性分析组件间交互的受控实验平台。MAGE本身并非追求绝对性能最优，而是通过集成情景记忆（episodic memory）、多目标帕累托选择（multi-objective Pareto selection）与自适应评估（adaptive evaluation），构建了一个可进行消融实验的标准化研究环境。研究发现了一种此前未被报道的现象——提示优化耦合效应（Prompt Optimization Coupling Effect, POCE）：即多个随机优化信号在闭环反思中不仅协同提升性能，还显著放大了结果方差，这种行为无法通过孤立分析各组件预测。关键发现包括：失败驱动的反思不可或缺，仅依赖评分（OPRO）或抽象批判（Self-Refine）的方法无法有效优化提示；在GSM8K-Hard上，MAGE达到46.4%准确率，显著优于GEPA的34.0%（Δ+12.4%，P(MAGE>GEPA)=0.998，基于gpt-4o-mini，5次种子实验），且方差相当（7.3% vs 7.0%）；扩大候选提示池规模（从n=3增至n=5）使平均准确率提升21.6%，方差增加3.7倍，凸显POCE最明显；在Llama 3.1 8B上的验证表明，POCE具有头空间依赖性——当基线模型已具备高准确率时，方差放大现象消失；在低数据场景（Ntrain=30）下，精心设计的固定提示优于所有反思式优化器，说明提示结构的选择在特定条件下超越优化算法的影响。综上，研究揭示提示优化系统本质上是耦合的随机过程，其评估应兼顾性能与稳定性，而非仅关注峰值准确率。
链接: https://arxiv.org/abs/2607.11944
作者: Prateek Singh
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 10 pages, 1 figure
Abstract:How do different components of iterative prompt optimization interact, and what happens when they are combined? We investigate this through MAGE (Memory-Augmented Goal-directed Prompt Evolution), a controlled analysis framework for studying component interaction in prompt optimization. MAGE is not proposed as a superior optimizer in absolute terms; it integrates episodic memory, multi-objective Pareto selection, and adaptive evaluation as a platform for controlled ablation. Our experiments uncover a previously unreported phenomenon, the Prompt Optimization Coupling Effect (POCE): when multiple stochastic optimization signals operate within a closed reflective loop, they interact in ways that simultaneously improve performance and amplify variance, behavior that cannot be predicted by analyzing components in isolation. Three main findings emerge. First, failure-grounded reflection is essential: methods relying only on scores (OPRO) or abstract critique (Self-Refine) fail to improve prompts. Second, MAGE achieves 46.4% versus GEPA’s 34.0% on GSM8K-Hard (+12.4%, P(MAGEGEPA)=0.998, 5 seeds on gpt-4o-mini), with comparable variance (7.3% vs. 7.0%). Third, increasing candidate diversity reveals the clearest POCE signal: expanding the candidate pool from n=3 to n=5 improves mean accuracy by +21.6% while increasing variance by 3.7x. We further validate on Llama 3.1 8B and show POCE is headroom-dependent: when the base model already achieves high accuracy, variance amplification disappears. Finally, in low-data regimes (Ntrain=30), well-designed fixed prompts outperform all reflective optimizers, indicating that scaffold choice dominates optimizer choice. Our results suggest prompt optimization systems behave as coupled stochastic processes and should be evaluated in terms of both performance and stability, not just peak accuracy.
[NLP-51] QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design GECCO2026
【速读】： 该论文旨在解决现有大语言模型（Large Language Models, LLMs）与进化计算结合用于组合优化问题中自动启发式设计时所面临的模式崩溃（mode collapse）问题，即算法种群趋于同质化，缺乏语义多样性，无法充分探索算法空间。其解决方案的关键在于提出一种名为质量-多样性进化（Quality-Diversity Evolution, QDEvo）的多目标框架，通过将质量-多样性优化（Quality-Diversity Optimization）与基于LLM的启发式搜索相结合，利用预训练代码嵌入（pre-trained code embeddings）构建无限容量的语义多样算法档案库，并引入分层自我反思机制（hierarchical self-reflection）以指导进化过程，从而在保持算法高性能的同时实现语义多样性与计算效率的协同提升。实验结果表明，QDEvo在超体积（Hypervolume）和逆生成距离（Inverted Generational Distance）等关键指标上显著优于现有先进方法，能够为复杂优化问题提供兼具高表现性、高效性与多样性的启发式解集。
链接: https://arxiv.org/abs/2607.11916
作者: Nam Do Khanh,Nhat Nguyen Tran Minh,Dat Pham Vu Tuan,Long Doan,Binh Huynh Thi Thanh
机构: Hanoi University of Science and Technology (河内科技大学); George Mason University (乔治梅森大学)
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 4 pages; Accepted as poster at GECCO 2026
Abstract:The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in combinatorial optimization. However, existing approaches suffer from mode collapse, converging to homogeneous populations that lack semantic diversity and fail to explore the full algorithmic space. We propose Quality-Diversity Evolution (QDEvo), a multi-objective framework that integrates Quality-Diversity optimization with LLM-driven heuristic search, maintaining an unbounded archive of semantically diverse algorithms using pre-trained code embeddings and incorporating hierarchical self-reflection to guide the evolutionary process. Extensive experiments across standard benchmarks and real-world industrial applications demonstrate that QDEvo significantly outperforms state-of-the-art methods in both Hypervolume and Inverted Generational Distance metrics. Our framework enables the discovery of heuristics that are simultaneously high-performing, computationally efficient, and semantically diverse, providing practitioners with a rich portfolio of solutions for complex optimization problems.
[NLP-52] AKE: Trajectory-Aware Knowledge Estimation for Text Dataset Distillation ECML-PKDD2026
【速读】： 该论文旨在解决大规模文本语料库在现代自然语言处理（Natural Language Processing, NLP）中带来的存储与训练成本高昂的问题，尤其体现在模型训练、微调及持续学习过程中的累积开销。其核心解决方案是提出一种文本数据集蒸馏框架，能够在保持下游任务性能的前提下，将原始语料库压缩至仅0.1%的规模。该方法的关键在于引入轨迹感知的知识估计（Trajectory-Aware Knowledge Estimation, TAKE），通过影响函数（Influence Functions）量化每个样本对下游目标的贡献，并沿训练轨迹整合知识信息，生成单个样本级别的知识得分，从而识别出最具信息量的代表性样本。这些得分作为离散最优传输（Discrete Optimal Transport）问题中的样本权重，指导从合成候选池中选取原型样本。实验表明，在极端压缩比（如每类仅20个样本或0.1%原始规模）下，TAKE在文本分类与自然语言推理等任务上仍能保持较高的下游准确性，验证了其在数据效率与任务保真度之间的平衡能力。该方法具有坚实的理论基础，对核心集构建（Coreset Construction）和以数据为中心的人工智能（Data-Centric AI）研究具有广泛启示意义。
链接: https://arxiv.org/abs/2607.11898
作者: Tri-Nhan Vo,Dang Nguyen,Sunil Gupta
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: To be published in ECML-PKDD 2026
Abstract:Large-scale text corpora have become a quiet bottleneck in modern NLP, not just in storage, but in the accumulated cost of training, fine-tuning, and continual learning. We propose a text dataset distillation framework that reduces corpora to as little as 0.1% of their original size while preserving downstream task fidelity. We approach distillation through the lens of influence functions, which quantify each sample’s contribution to the downstream objective, a natural and principled basis for selection. We introduce Trajectory-Aware Knowledge Estimation (TAKE), which convolves the knowledge-based influence along the training trajectory into a single per-sample knowledge score, capturing informative samples. These scores serve as sample weights within a discrete Optimal Transport objective, guiding prototype selection from a synthetically generated candidate pool. We evaluate TAKE on downstream accuracy across text classification and natural language inference tasks at extreme compression (0.1% or 20 samples/class), showing that data efficiency is achievable without sacrificing task fidelity. The approach is theoretically grounded, with broader implications for coreset construction and data-centric AI. We release our source code at this https URL.
[NLP-53] Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels KR
【速读】： 该论文旨在解决社交媒体中虚假信息叙事（disinformation narratives）检测的难题，主要挑战包括内容传播规模庞大、演化速度快以及语言表达形式多样。其解决方案的关键在于提出一种基于图结构的分析框架，通过结合弱监督（weak supervision）与传播图分析，将语义相关的声明聚类为叙事层级的群体，并建模其在相互连接的频道间的扩散路径。该方法能够有效识别出难以通过单一帖子层面分析捕捉的协同性叙事放大行为，从而实现对大规模即时通讯环境中虚假信息传播模式的可扩展检测与深入洞察。
链接: https://arxiv.org/abs/2607.11894
作者: Yuliia Vistak,Viktoriia Makovska,Vera Schmitt,Veronika Solopova
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: UNLP 2026 The Fifth Ukrainian Natural Language Processing Conference
Abstract:Detecting disinformation narratives on social media is challenging due to the scale of amplification, rapid evolution, and linguistic variability of online content. We propose a graph-based framework for identifying and analyzing disinformation narratives in Telegram ecosystems by combining weak supervision with propagation graph analysis. The approach aggregates semantically related claims into narrative-level clusters and models their diffusion across interconnected channels. This enables the detection of coordinated narrative amplification that is difficult to capture through post-level analysis alone. Our results demonstrate that integrating textual signals with network structure provides a scalable method for detecting disinformation narratives and offers insights into how they propagate within large-scale messaging environments.
[NLP-54] Im Sorry but I Cant Help with Braille: Revealing Accessibility Failures in State-of-the-Art LLM s ACL2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在结构约束性强且无障碍性至关重要的模态——如韩文盲文（Korean-Braille）翻译任务中表现不佳的问题。尽管多语言、指令微调的LLMs理论上可通过文本表示泛化至盲文，但研究发现其输出存在持续性差、不稳定，并与人类判断显著不符。问题的关键在于当前LLMs缺乏对盲文特性的感知能力，即盲文感知的分词机制缺失，以及韩文与盲文模式之间对齐薄弱。相比之下，基于相同数据对小型模型（T5-small）进行监督微调，可在多种标准指标（如SacreBLEU、ChrF++、CER、BLEU、ROUGE-L、METEOR、CIDEr）上实现显著且稳定的性能提升，远超零样本和提示工程的LLM基线。这揭示了当前主流LLMs在特定任务中的系统性局限，并证明适度的任务定制化监督具有显著有效性。
链接: https://arxiv.org/abs/2607.11893
作者: Abdullah Abdullah
机构: orinu Inc. (orinu Inc.)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at the LTEDI Workshop at ACL 2026
Abstract:Large Language Models (LLMs) perform strongly on many language tasks, but their capability in structurally constrained, accessibility-critical modalities such as Braille remains unclear. We evaluate state-of-the-art LLMs on bidirectional Korean-Braille translation using a human-annotated dataset. Despite expectations that multilingual, instruction-tuned models can generalize to Braille via text representations, we find consistently poor, unstable outputs and substantial disagreement with human judgments. These results point to missing Braille-aware tokenization and weak alignment between Korean and Braille patterns. In contrast, supervised fine-tuning of a small model (T5-small) on the same data yields large and stable gains over zero-shot and prompted LLM baselines across standard metrics (SacreBLEU, ChrF++, CER, BLEU, ROUGE-L, METEOR, CIDEr). Our findings reveal a systematic limitation of current LLMs and demonstrate the effectiveness of modest task-specific supervision.
[NLP-55] G-SHARE: A Guideline-Based Structured Reasoning Framework for Human-Factor Event Diagnosis
【速读】： 该论文旨在解决核电厂运行事件中人因事件诊断质量依赖专家主观解读叙事报告与基于指南的非结构化分析所导致的局限性问题。现有数据驱动或单次提示（one-shot prompting）的大语言模型方法普遍存在推理过程缺乏结构化、与正式诊断指南对齐不足以及输出逻辑不一致等缺陷。为此，本文提出G-SHARE——一种基于指南的结构化推理框架，将中国核电集团（CNNP）九步人因事件诊断指南转化为多阶段诊断流程，其关键在于实现证据抽取、逐步诊断推理和事后一致性修复三个核心环节，从而显式利用报告中的证据、生成中间推理链，并对诊断结果进行逻辑验证。实验基于中国核工业来源的真实人因事件报告构建数据集，并采用领域专家标注的黄金标准子集进行评估。结果表明，G-SHARE显著优于单次提示与传统机器学习基线模型，最优版本在整体准确率与宏平均F1值上均达到最佳表现；消融实验进一步证明，结构化推理与一致性约束对于提升诊断鲁棒性至关重要，尤其在弱提示条件下。研究证实，将专家诊断指南转化为可审计的推理工作流，为安全关键行业中的智能化人因分析提供了可行且有效的路径。
链接: https://arxiv.org/abs/2607.11892
作者: Xingyu Xiao,Mao Du,Jiejuan Tong,Jingang Liang,Haitao Wang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Human-factor event diagnosis is essential for learning from operational events in nuclear power plants, yet its quality depends strongly on expert interpretation of narrative reports and guideline-based this http URL data-driven or one-shot large language model approaches often lack structured reasoning, have limited alignment with formal diagnostic guidelines, and may generate logically inconsistent conclusions. To address this issue, this study proposes G-SHARE, a guideline-based structured reasoning framework that operationalizes the CNNP nine-step human-factor event diagnosis guideline into a multi-stage diagnostic this http URL framework consists of evidence extraction, stepwise diagnostic reasoning, and post-hoc consistency repair, enabling explicit use of report evidence, intermediate rationale generation, and logical validation of diagnostic outputs. A dataset of real human-factor event reports was constructed from Chinese nuclear industry sources, and a gold-standard subset annotated by domain experts was used for evaluation. Results show that G-SHARE substantially outperforms one-shot prompting and traditional machine learning baselines, with the strongest version achieving the best overall accuracy and macro-F1. Ablation results further indicate that structured reasoning and consistency enforcement are critical to robust diagnosis, especially under weak prompting conditions. The findings demonstrate the value of transforming expert diagnostic guidelines into auditable reasoning workflows, providing a practical pathway for intelligent human-factor analysis in safety-critical industries.
[NLP-56] CANDI: Contextual Alignment for Niche Domains Question Answering
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在医疗诊断、金融咨询等专业领域应用时，传统问答评估基准无法有效衡量其上下文敏感性、用户意图对齐能力及领域理解深度的问题。现有评估体系普遍侧重通用知识，难以反映实际应用场景中对精准、情境化与可操作性回答的需求。为此，论文提出CANDI-QA（Contextual Alignment for Niche Domains Question Answering），一个由专家构建的新型数据集，专门用于评估模型在专业化场景下的表现。其核心创新在于将问题划分为两类：(1) 信息辅助类问题（Information Assistance Questions），要求模型精确提取事实性信息；(2) 应用推理类问题（Applied Inference Questions），需进行多跳推理以生成具有行动指导意义的洞察。解决方案的关键在于引入MTSS-Net——一种轻量级神经符号框架，融合神经检索与规则推理机制，作为强基准模型。实验表明，当前主流LLMs在缺乏显式上下文建模或符号逻辑集成的情况下，普遍存在上下文对齐不足的缺陷。因此，提升模型在高风险专业领域的可靠性，关键在于增强其对任务上下文的感知能力与结构化推理能力。CANDI-QA不仅为评估模型的上下文感知性能提供了标准化基准，也推动了面向可信、稳健的专业化生成式AI的发展。
链接: https://arxiv.org/abs/2607.11891
作者: Megha Chakraborty,Darssan L. Eswaramoorthi,Het Riteshkumar Shah,Madhur Thareja,Michelle A Ihetu,Harshul Raj Surana,Kaushik Roy,Amit Sheth
机构: Purdue University (普渡大学); IAIRO(人工智能与智能优化研究中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The deployment of large language models (LLMs) in specialized domains like medical diagnostics and financial advisory necessitates evaluating capabilities beyond general knowledge. Traditional question-answering benchmarks often fail to capture the nuanced contextual grounding, user awareness, and domain understanding these fields require. To address this, we introduce CANDI-QA (Contextual Alignment for Niche Domains Question Answering), a novel dataset evaluating LLMs on delivering accurate, context-sensitive, and user-aligned answers in specialized settings. CANDI-QA features expert-curated question-answer pairs structured into two categories: (1) Information Assistance Questions, which are direct, factual queries requiring precise extraction, and (2) Applied Inference Questions, which are multi-hop reasoning tasks needing situational inference to generate actionable insights. We evaluate over ten diverse language models, from compact open-source to state-of-the-art proprietary systems. As a robust baseline, we present MTSS-Net, a lightweight neuro-symbolic framework combining neural retrieval with rule-based reasoning. Our findings highlight the profound challenges of achieving contextual alignment in niche domains, revealing the limitations of current LLMs without enhanced contextual or symbolic integration. Ultimately, CANDI-QA serves as a critical benchmark for advancing research in context-aware language models, stimulating the development of robust, trustworthy AI for high-stakes domains.
[NLP-57] Scaling Point-in-Time Language Models
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）在训练过程中因使用包含未来信息的互联网语料而引入的“前瞻偏差”（lookahead bias）问题，该偏差会严重削弱金融与社会科学领域中回溯测试（backtests）和因果推断的有效性。其核心解决方案是构建“时点语言模型”（point-in-time language models），即仅使用截至每个日历日期前可获得的文本进行训练，从而从结构上杜绝信息泄露。关键创新在于通过模型规模的提升显著缩小了时点模型与非受限模型之间的性能差距：研究者在经过时间过滤的1万亿条数据（FineWeb）上训练了参数量达40亿的解码器型Transformer模型，并生成了覆盖2013–2024年每月的模型检查点。实验表明，这些时点模型在常识推理与语言理解等多项基准测试中已接近同等规模开放权重模型（如Gemma-3-4B和LLaMA-7B）的性能水平，尽管部分任务仍存在差距。此外，采用LoRA进行指令微调进一步提升了模型的下游应用能力。研究团队开源了完整的训练流程、数据构建方法及评估代码，为实现可复现的时点语言建模提供了坚实支持，推动需要严格时间有效性保障的研究应用发展。
链接: https://arxiv.org/abs/2607.11889
作者: Bryan Kelly,Semyon Malamud,Johannes Schwab,Teng Andrea Xu
机构: Yale School of Management (耶鲁管理学院); AQR Capital Management (AQR资本管理公司); NBER (国家经济研究局); Swiss Finance Institute (瑞士金融研究所); EPFL (洛桑联邦理工学院); CEPR (欧洲经济政策研究中心); Swiss National Science Foundation (瑞士国家科学基金会); Swiss National Supercomputing Centre (CSCS) (瑞士国家超级计算中心); Anthropic (Anthropic); Claude (Claude)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences. Point-in-time language models–trained exclusively on text available up to each calendar date–eliminate this leakage by construction, but existing efforts typically produce models that lag substantially behind their unconstrained counterparts. We show that this performance gap can be substantially narrowed through scale. Training decoder-only transformers with up to 4 billion parameters on 1 trillion chronologically filtered tokens from FineWeb, we construct a sequence of monthly model checkpoints spanning 2013-2024. Across a range of common-sense reasoning and language understanding benchmarks, our models approach the performance of leading open-weight models of comparable size (e.g., Gemma-3-4B and LLaMA-7B) trained on temporally unrestricted data, although a performance gap remains on several tasks. Instruction fine-tuning via LoRA further improves downstream usability. We release the complete pipeline–including dataset construction, training infrastructure, and evaluation code–to enable reproducible point-in-time language modeling and to support research applications that require strict temporal validity.
[NLP-58] he Sound of Absence: Audio-Language Embedding Models Struggle with Negation
【速读】： 该论文旨在解决当前音频-语言嵌入模型（如CLAP）在评估中普遍存在的“仅正向验证”问题，即模型在处理肯定性描述时表现良好，却难以识别和区分被否定的声音事件。其核心问题是：现有评估范式忽略了对否定语义的考察，导致模型在面对“不存在”或“未发生”的声音概念时，无法生成与肯定表达相区别的语义表示，从而表现出严重的“肯定偏差”（affirmation bias）。解决方案的关键在于提出一个名为NegEval-Audio的框架，将现有数据集转化为两种具备否定感知能力的任务——检索类否定任务（Retrieval-Neg）和多选否定任务（MCQ-Neg），以系统性地检验模型是否能够有效区分存在与缺失的声音事件。实验结果表明，在AudioCaps和Clotho数据集上，模型在否定情境下的性能显著下降，尤其在MCQ-Neg任务中准确率远低于随机水平，且这一缺陷在基于大型多模态语言模型（multimodal LLM-based）的最新嵌入模型中依然存在。尽管采用无需训练的调制方法可略微提升MCQ-Neg表现，但对检索类任务改善有限，揭示了肯定偏差根植于模型的表征几何结构之中，因此必须引入显式的否定感知训练目标才能从根本上解决该问题。
链接: https://arxiv.org/abs/2607.12290
作者: Chun-Yi Kuan,Hung-yi Lee
机构: National Taiwan University (国立台湾大学)
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
备注: Manuscript in progress
Abstract:Audio-language embedding models such as CLAP are widely evaluated on matching present sound events, but rarely on negation. We show this affirmation-only evaluation hides a key limitation: these models fail to encode negated sound concepts, mapping affirmative and negated captions to nearly identical representations. To expose this blind spot, we introduce NegEval-Audio, a framework that converts existing datasets into two negation-aware tasks, Retrieval-Neg and Multiple-Choice Negation (MCQ-Neg), to probe whether models distinguish present from absent events. On AudioCaps and Clotho, performance degrades sharply under negation, with negation-type MCQ accuracy falling far below chance, and the failure persists even for a recent multimodal LLM-based embedding model. While a training-free steering method improves MCQ-Neg, it yields marginal gains for Retrieval-Neg. This indicates that affirmation bias is a fundamental flaw in the representation geometry, necessitating explicit negation-aware training objectives.
信息检索
[IR-0] ViHoRec: A Quality-Controlled Vietnamese Hotel Recommendation Dataset and Cold-Start Benchmark
链接: https://arxiv.org/abs/2607.12946
作者: Minh Hoang Nguyen
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Recommender-system research for Vietnamese remains limited by the absence of a public, well-documented hotel interaction resource. Building such a resource is challenging for three reasons: cross-platform hotel names must be reconciled before interactions are comparable; quality must be audited with reproducible metrics rather than ad hoc cleaning; and public release must preserve privacy while remaining benchmarkable under realistic cold-start conditions. We introduce ViHoRec, a quality-controlled Vietnamese hotel recommendation dataset of 18,267 interactions between 6,832 users and 560 hotels, crawled from this http URL, Traveloka, and Ivivu. Our contributions are: (i) a reproducible construction pipeline with cross-platform entity resolution and quantitative quality control; (ii) a privacy-preserving release with HMAC pseudonyms; and (iii) a public cold-start benchmark with temporal leave-last-one-out split, data-centric ablations, and dependency-free baselines. On the public split, learned models degrade sharply for users with short histories (BPR-MF Recall@10: 0.065 vs. 0.120), while UserKNN remains strongest overall, establishing ViHoRec as a sparse, cold-start-dominated testbed for low-resource recommendation. All data are publicly available at this https URL.
[IR-1] RecRec: Latent Interests Recursive Reasoning for Sequential Recommendation RECSYS2026
链接: https://arxiv.org/abs/2607.12945
作者: Wenhao Deng,Junchen Fu,Hanwen Du,Alexandros Karatzoglou,Ioannis Arapakis,Hangjun Guo,Kaiwen Zheng,Yongxin Ni,Joemon M. Jose
类目: Information Retrieval (cs.IR)
备注: Accepted at RecSys 2026, 10 pages
Abstract:Sequential recommender systems rely on a single forward pass to encode user interaction histories and predict the next item. Increasing inference-time computation through latent reasoning, with the model proceeding step by step before the final prediction, has been recently explored in sequential recommendation with promising results. However, how to structure the reasoning process for sequential recommendation remains an open question. Existing approaches couple reasoning and prediction in a single d -dimensional state, limiting reasoning depth and often relying on multi-stage pipelines with reinforcement learning (RL). We propose RecRec (Recursive Reasoning for Recommendation), an RL-free framework that decouples reasoning from prediction, overcoming the fixed d -dimensional state bottleneck of prior methods. RecRec consists of a Context Compressor and a Recursive Reasoner, trained in two simple supervised stages. The Context Compressor distills the backbone’s hidden states into a small set of latent interests, with an Interest Diversity Regularizer encouraging each interest to capture a distinct aspect of user behavior. The Recursive Reasoner then refines these interests by reasoning in a separate intermediate latent space. Deep supervision lets the reasoning depth be freely adjusted at inference without retraining. On four real-world datasets, RecRec outperforms state-of-the-art reasoning-enhanced methods, and on three of four datasets, gains extend past the training-time depth. Our findings point to a decoupled, multi-vector recipe that unleashes latent reasoning from the single-state bottleneck of prior methods, suggesting reasoning-state structure as a design axis to explore further in sequential recommendation.
[IR-2] What Would You Click? Personalized Video Thumbnail Generation with Preference-aware Highlight Retrieval
链接: https://arxiv.org/abs/2607.12882
作者: Zhiyu He,Zecheng Zhao,Tong Chen,Zi Huang,Yiqun Liu,Min Zhang
类目: Multimedia (cs.MM); Information Retrieval (cs.IR)
备注:
Abstract:Video thumbnails are a key factor for attracting user clicks on video platforms, and are increasingly supported by automation. However, existing thumbnail generation methods typically produce generic results shared across users, overlooking the diversity of individual preferences. We therefore introduce personalized video thumbnail generation, a novel task that aims to create thumbnails tailored to user-specific preferences. It is challenging in two aspects: (i) identifying visual anchors (i.e., key frames) from each video to guide the generation, which requires a balance between personalization and informativeness that existing highlight detection methods fail to achieve; and (ii) generating personalized thumbnails that are both visually coherent and faithful to the original video. As a response, we propose a two-stage framework that tightly couples preference-aware retrieval with controllable generation. In the first stage, a personalized highlight retriever captures fine-grained user-video interactions and incorporates video semantics through summarization, enabling the selection of diverse visual anchors aligned with both user preferences and video contexts. In the second stage, a VLM-guided diffusion pipeline transforms these anchors into thumbnails by extracting and injecting semantically grounded visual cues, improving personalization while preserving visual coherence and fidelity. Experiments on two public datasets show our method delivers state-of-the-art performance compared with both retrieval-based and generative baselines. A user study further demonstrates improved click preference, highlighting its effectiveness in enhancing user engagement. The code is available at this https URL.
[IR-3] Learning to Forget: Satiation-Aware Long-Sequence Transducers for Mitigating Post-Purchase Redundancy
链接: https://arxiv.org/abs/2607.12714
作者: Yipin Dai,Ruocong Tang,Xing Fang,Yang Huang,Jing Wang,Zhentao Song,He Guo
类目: Information Retrieval (cs.IR)
备注:
Abstract:Sequential recommendation models predominantly interpret user interactions as positive signals for preference accumulation. However, in e-commerce scenarios, a purchase action often signifies the termination of a specific intent (“Interest Exit”) rather than its continuation. Existing models overlook this distinction, suffering from Action-Intent Asymmetry, which leads to severe post-purchase redundancy. In this paper, we propose the Satiation-Aware Mechanism (SAM), an end-to-end framework designed to explicitly model the lifecycle of user interests. SAM incorporates three key components: (1) A Dual-path Cross-Attention architecture that retroactively suppresses historical clicks associated with a fulfilled intent while simultaneously retrieving personalized replenishment rhythms from long-term purchase history; (2) An Adaptive Satiation Gating Unit (ASGU) that generates a time-sensitive soft mask to inhibit satisfied interests immediately after purchase and gradually “re-awaken” them as the predicted repurchase cycle approaches; and (3) A self-supervised Time-to-Next-Purchase (TTNP) auxiliary task to learn latent product lifecycles without manual annotation. Extensive offline experiments on industrial datasets and online A/B testing demonstrate that SAM significantly reduces the Post-Purchase Repeat Rate (PPRR) by over 60%.
[IR-4] owards Vision-Free CIR: Attribute-Augmented Scoring and LLM -Based Reranking for Zero-Shot Composed Image Retrieval
链接: https://arxiv.org/abs/2607.12621
作者: Ryotaro Shimada,Yu-Chieh Lin,Yuji Nozawa,Youyang Ng,Osamu Torii,Yusuke Matsui
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注:
Abstract:Recent work has shown that "Vision-Free’’ approaches (representing images as text) can be effective for standard image retrieval tasks. However, it remains unclear whether this paradigm can effectively handle a more complex, multimodal task, Composed Image Retrieval (CIR), due to the inherent information loss in textual descriptions. In this paper, we introduce a Vision-Free CIR framework that addresses this challenge through two key techniques: (1) Attribute-Augmented Hybrid Scoring, which compensates for lost visual details via explicit attribute matching, and (2) LLM-Based Reranking, which verifies semantic consistency of top candidates. Experiments on the open-domain CIRR dataset show that our approach outperforms existing Zero-shot CIR methods (44.04% R@1, +8.79%). On FashionIQ, our results highlight the trade-off between semantic reasoning and fine-grained visual matching. Ablation studies reveal that both attribute-augmented scoring and LLM-Based Reranking consistently improve performance.
[IR-5] Cheaper is Better: A Discount-Aware Network for Conversion Rate Prediction in E-commerce Recommendation System
链接: https://arxiv.org/abs/2607.12578
作者: Ruocong Tang,Yang Huang,Xing Fang,Chenyi Yan,Chuike Sun,Jing Wang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Post-click conversion rate (CVR) is a crucial element in online recommendation systems, which addresses significant challenges such as data sparsity (DS), sample selection bias (SSB), and delayed feedback. However, the impact of item discount rate-a key factor influencing both pricing and user purchasing behavior, has received limited attention. In this paper, we introduce the Discount-Aware Network (DANet) to model the relationship between item discount rates and CVR. DANet comprises three main components: 1) a time-frequency transformation module that utilizes Fourier transform to derive the frequency spectrum and capture the long-term discount rate trends of items; 2) a distribution de-bias module designed to mitigate the biases in user-specific discount rates caused by various purchase combinations and promotional activities, as well as periodic deviations linked to different promotion periods on e-commerce platforms; and 3) a supervised regression auxiliary task that establishes the explicit item discount labels to enhance the model’s performance in terms of value accuracy, facilitating an effective representation of item discount rates. Experimental results on real datasets demonstrate the superiority of DANet, with offline AUC improving by 1.61%, and online A/B test also shows that DANet achieves impressive gains of 3.63% on pCVR and 2.23% on GMV. DANet has been successfully deployed on Alibaba Tmall APP. The code is available at this https URL.
[IR-6] Where Reasoning Matters: Rethinking Latent Reasoning in Semantic ID-based Generative Recommendation
链接: https://arxiv.org/abs/2607.12425
作者: Shangxin Yang,Min Gao,Zongwei Wang,Junliang Yu
类目: Information Retrieval (cs.IR)
备注: 12 pages, 7 figures, 5 tables
Abstract:Semantic ID-based generative recommendation predicts an item by generating a short sequence of semantic ID tokens, where each token is produced autoregressively. Latent reasoning has recently been introduced to improve this process through additional hidden-state computation before each token decision. This raises a practical question: when one item is represented by a sequence of semantic ID tokens, should each token receive the same fixed number of latent refinement steps, or should these steps be allocated more effectively across positions? We study this question through position-wise information-gain (IG), which measures how much each semantic ID position reduces the uncertainty of the target item. We observe that earlier semantic ID positions usually provide higher information-gain, while later positions contribute less additional information. We further analyze that applying more refinement to high-IG positions tends to bring larger expected benefits. Based on this observation, we propose IBA, an Information-Gain Budget Allocation framework for semantic ID-based generative recommendation. IBA treats latent refinement steps as a limited computational resource and learns how to allocate them across semantic ID positions, assigning more refinement to informative positions and less to positions with smaller contribution. Experiments on multiple public datasets show that IBA consistently improves strong generative recommendation baselines and achieves a better accuracy–computation trade-off than fixed or poorly matched step allocations.
[IR-7] MESH: Scaling Up Retrieval with Heterogeneous Content Unification
链接: https://arxiv.org/abs/2607.12392
作者: Jiaxing Qu,Yilin Chen,Junpeng Hou,Jinfeng Rao,Olafur Gudmundsson,Sai Xiao,Huizhong Duan
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Optimizing large-scale retrieval hinges on the ability to efficiently surface candidates across diverse content tiers. However, to capture segments such as fresh and long-tail content, modern systems typically resort to a fragmented “zoo” of specialized retrieval models. This operational complexity is attributed to a fundamental challenge in heterogeneous retrieval systems, the Scaling Bias of Heterogeneity, where model capacity gains do not apply equally across diverse content tiers. To bridge this gap, we propose MESH as a unified retrieval scaling framework that mitigates this bias through a modularized architecture integrated with gated bias correction. By partitioning the feature space into independent domains, MESH enforces a structural inductive bias that reduces interference between sparse-item signals and high-frequency engagement features. This protected gradient path leads to improved scaling behavior for sparse content, empirically validated by a 14 times improvement in the power-law scaling exponent for fresh items. In online evaluations on Pinterest’s Related Pins platform, a billion scale item-to-item recommendation system, these improvements translate into a +5.5% lift in fresh-item repins, alongside with 55% improvement in funnel efficiency and +0.46% improvement in user retention. Finally, our asynchronous serving strategy ensures production viability by delivering a 2.87 times improvement in system throughput. Our findings suggest MESH as a promising paradigm for consolidating fragmented retrieval infrastructures into more scalable and ecosystem-aware backbones.
[IR-8] SlimPer: Make Personalization Model Slim and Smart
链接: https://arxiv.org/abs/2607.12281
作者: Siqi Wang,Xianjie Chen,Shaofeng Deng,Albert Chen,Romil Shah,Jiawei Huang,Zhaoqin Wang,Zhang Zhang,Yiqun Liu,Meilei Jiang,Anish Dubey,Moyan Mei,Tongxin Wang,Nathan Berrebbi,Misael Manjarres,Armand Sauzay,Shardul Kothapalli,Aryaman Vinchhi,Kevin Johnstone,Juheon Lee,Gufan Yin,Ziheng Huang,Justin Lin,Mert Terzihan,Yilin Qi,Cynthia Yang,Colin Peppler,Qi Ding,Ruohan Sun,Ge Song,Litao Deng,Parichay Kapoor,Matt Ma,Huihui Cheng,Jiyuan Zhang,Yanli Zhao,Yiping Han,Fangqiu Han,Ning Yao,Arun Singh,Jordan Edwards,Zhengyu Su,Abhishek Kumar,Guangdeng Liao,Ankit Asthana
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, which justifies maintaining large intermediate tensors that scale with sequence length. In contrast, recommendation systems produce a single set of relevance scores for each user, item pair without token-level supervision. Leveraging this observation, we propose SlimPer, which reformulates personalized ranking as iterative refinement of a compact, unified user, item knowledge base. At each layer, the model selectively queries raw multi-modal user-side tokens, computes explicit relevance matching scores, and refines the knowledge base, all in O(N) per-layer cost with a fixed-size intermediate representation. As a result, model depth is decoupled from user history length, enabling deeper relevance understanding without proportional growth in compute or memory; request-only optimization further trims memory by sharing a single copy of user-side tokens across all candidate items. SlimPer unifies sparse, dense, and sequence features within a single backbone and provides inherent interpretability through its attention mechanism. Deployed on Instagram Reels and Feed, SlimPer yields measurable improvements in user engagement while streamlining the overall system and enabling effective modeling of 10k+ fine-grained user history events.
[IR-9] Not Only NTP: Extending Training Signal Coverag e for Generative Recommendation
链接: https://arxiv.org/abs/2607.12277
作者: Changhao Li,Shuli Wang,Junwei Yin,Senjie Kou,Yinqiu Huang,Chi Wang,Yinhua Zhu,Haitao Wang,Xingxing Wang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Next-Token Prediction (NTP) carries two structural training signal limitations. First, NTP optimizes for single-step prediction only, placing no supervised pressure on learning longer-range behavioral structure – we term this \textbftemporal locality. Second, in multi-domain sequences, each target item embedding receives gradient updates exclusively from the immediately preceding hidden state, with no explicit gradient pathway from cross-domain context – we term this \textbfspatial locality. We propose \textbfNONTP, extending NTP’s signal coverage along both dimensions through two auxiliary objectives. \textbfTCL (Temporal Contrastive Learning) uses a BYOL-style EMA teacher with InfoNCE to align hidden states against a K -step future trajectory in representation space. \textbfTDL (Trans-Domain Learning) mean-pools cross-domain hidden states and predicts through the shared prediction head, opening a second gradient pathway with no additional parameters. Both are discarded at inference: zero overhead. On a four-domain Meituan industrial dataset (full ranking), NONTP achieves HR@10 +34.3% over NTP and +18.3% over MBGR. On the public Amazon Movie-Book-CDs benchmark, HR@10 +2.8% and NDCG@10 +3.7%. Online A/B tests confirm CTR +1.8% and GMV +2.1% (both p 0.01 ). Ablation studies confirm each component contributes independently, with gradient conflict analyzed as a direction for future work. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2607.12277 [cs.IR] (or arXiv:2607.12277v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.12277 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-10] On-Device Deep Research at 4B: Exposure Bounds Faithfulness Retrieval Bounds Coverag e
链接: https://arxiv.org/abs/2607.12257
作者: Vinay Kumar Chaganti
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 13 pages, 2 figures, appendix
Abstract:On-device research agents search a corpus, read sources, and write a cited brief on a personal laptop. Whether their citations are faithful, and at what cost, is unmeasured for a deployable small model. This study fixes one 4B generator on a 24 GB laptop and asks what makes its citations faithful. It separates two quantities usually reported as one number. Cited claim faithfulness asks whether the cited source supports the claim. Trustworthy coverage asks whether the agent also cites the right sources. The study crosses how much of each source the generator sees, 400 against 1500 characters, with the quality of the sources supplied, gold papers against retrieved papers. Two levers fall out, and they act on different outcomes. Exposure sets faithfulness. More of each source lifts faithfulness from 0.45 to 0.58 on retrieved sources and from 0.37 to 0.58 on gold sources, and the two settings converge, so faithfulness is bound by exposure, not by whether the source is correct. The exposure lift is robust to a second, independent judge; the exact convergence is tight under the primary judge and only approximate under the second. Retrieval sets coverage. Trustworthy coverage stays near 0.22 on retrieved sources at any exposure, because recall is held near 0.40, so exposure cannot fix which sources are cited. The extra exposure costs about 235 output tokens. The practical recipe is to raise per source exposure first, cheaply, and then treat retrieval recall as the only remaining lever.
[IR-11] SHEAF: Self-profiled Hardness Estimation from Answer-set Flux for Predicting Query Hardness in Graph-based ANN Search
链接: https://arxiv.org/abs/2607.12229
作者: Dongfang Zhao
类目: Information Retrieval (cs.IR)
备注:
Abstract:Graph-based approximate nearest neighbor (ANN) search is usually governed by a beam-width parameter that trades recall for throughput and is fixed for the whole workload. Yet, queries may not be equally hard: for example, on the widely used data set SIFT1M, the beam that a query needs to reach 95% recall varies by more than 32\times . Therefore, serving each query at its own width would help if the system could tell, cheaply and in advance, how hard it is. The prevailing proxy for this difficulty is called local intrinsic dimensionality (LID); however, LID is static and geometric, which makes it only weakly predict the minimum beam. This paper presents a new measure, namely Self-profiled Hardness Estimation from Answer-set Flux (SHEAF), which represents a query’s hardness as how much its own top- k answer set changes between two shallow probe widths. We design a self-profiling estimator that turns this flux into a deployable per-query beam predictor; furthermore, we develop a fixed-probe evaluation protocol that scores each measure over all queries with an observed minimum sufficient beam. On popular ANN indexes such as CAGRA and HNSW across four diverse data sets, SHEAF predicts the per-query beam better than five baseline measures on both GPU and CPU by up to 1.55\times in held-out correlation, using only two shallow probe searches and no query-time ground truth. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2607.12229 [cs.IR] (or arXiv:2607.12229v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.12229 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-12] Cost-Governed RAG : Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems
链接: https://arxiv.org/abs/2607.12188
作者: Navnit Shukla
类目: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR)
备注:
Abstract:Enterprise Retrieval-Augmented Generation (RAG) deployments face a critical governance gap: while LLM generation cost is metered per token, the retrieval layer - vector memory, similarity compute, and embedding API calls - remains an unattributed shared cost, enabling invisible cross-subsidization among tenants. We present Cost-Governed RAG, an architecture that integrates a codebook-oblivious vector index (TurboVec) with a multi-tenant LLM governance gateway, creating a unified observability stack where embedding, retrieval, and generation costs are jointly attributable per tenant. The architecture exploits TurboVec’s deterministic, closed-form memory formula to enable near-exact per-tenant retrieval cost calculation - a property unavailable in graph-based indexes with non-linear memory overhead. Deployed on Snowpark Container Services within a cloud data platform’s governance boundary, the system achieves 99.96% end-to-end cost attribution accuracy across 100 simulated tenants (10M vectors, log-normal size distribution) with telemetry overhead below 0.04% of query latency. The architecture reduces retrieval infrastructure cost by 3.1-9.0x compared to managed vector database services under the pricing assumptions detailed in Section IV. We formalize a three-layer cost model and demonstrate that codebook-oblivious quantization enables deterministic per-tenant cost attribution while also removing the shared-codebook leakage surface present in trained quantizers - the latter observation being exploratory and subject to the limitations described in Section VII.
[IR-13] Explaining When PRF Fails: Participatory Auditing for Selective Query Expansion SIGIR2026
链接: https://arxiv.org/abs/2607.12098
作者: Zeyan Liang,Graham McDonald,Iadh Ounis
类目: Information Retrieval (cs.IR)
备注: Accepted at WExIR @ SIGIR 2026, the 2nd Workshop on Explainability in Information Retrieval, Melbourne (Naarm), Australia, 24 July 2026. 3 pages, 1 figure. Extended abstract building on the SIGIR 2026 short paper “Auditing Query Drift: Do Users Actually Benefit from Pseudo-Relevance Feedback?” (doi: https://doi.org/10.1145/3805712.3809916 )
Abstract:Pseudo-Relevance Feedback (PRF) improves retrieval effectiveness on average, but harms a substantial fraction of queries through query drift, an asymmetry hidden by aggregate offline metrics. Existing Selective PRF (sPRF) approaches typically rely on Query Performance Prediction (QPP) methods derived from the same ranking statistics, and therefore inherit, rather than resolve, this opacity. We argue that this is a core explainability problem in IR, and propose a two-stage audit-then-automate framework. In Stage 1, a participatory audit with 108 users across 43 TREC Deep Learning 2019 queries shows that only 20.9% of queries benefit from PRF, while 25.6% suffer a degraded user experience, and that avoiding harm is nearly twice as valuable as exploiting successful expansion. In Stage 2, we repurpose LLM-based rerankers as system preference predictors that replicate these user-derived labels automatically, grounded in inspectable document evidence. Together, the two stages explain which queries PRF harms, why an sPRF decision is made, and how the decision can be inspected at scale, turning an opaque retrieval component into an auditable, user-grounded one.
[IR-14] Graph-Constrained Policy Learning for Extreme Clinical Code Prediction
链接: https://arxiv.org/abs/2607.11954
作者: Amritpal Singh,Sebastian Torres,Khawar Shakeel,Syed Ahmad Chan Bukhari
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Clinical code prediction maps unstructured discharge summaries to ICD-10-CM leaf codes in a large, sparse, and deeply hierarchical label space. Most systems treat the task as flat multi-label classification, scoring codes independently and providing limited training signal for rare labels. We propose a graph-constrained traversal policy that formulates ICD prediction as a finite-horizon decision process over a pruned code hierarchy. A single language model descends the graph level by level, selecting valid child nodes until billable leaf codes are reached. This converts extreme multi-label prediction into sparse, hierarchy-aware subset decisions while guaranteeing structurally valid outputs. On MIMIC-IV discharge summaries, our best supervised policy, SFT-1+, achieves 0.709 micro-F1 on a curated 50-code subset and 0.527 micro-F1 on the full 15,761-code space, outperforming flat baselines including CAML, LAAT, and PLM-ICD. In the full setting, SFT-1+ improves over the strongest flat baseline by 0.044 micro-F1 and 0.157 macro-F1, suggesting that graph-constrained decomposition mitigates the rare-code bottleneck. A controlled factorial study evaluates architecture, training algorithm, and data budget. Across both scales, one shared policy matches a three-specialist cascade while avoiding its context-window overflow on 28-32% of full-space test notes. Increasing supervised trajectory data is the only intervention that consistently improves performance, while GRPO reinforcement learning provides no benefit over supervised continuation with matched data. These results show that simple graph-constrained policy learning can outperform more complex flat, cascaded, and reinforcement-learning alternatives for extreme clinical code prediction. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) Cite as: arXiv:2607.11954 [cs.LG] (or arXiv:2607.11954v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.11954 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-15] ransforming LLM s into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking
链接: https://arxiv.org/abs/2607.11933
作者: Shreeya Dasa Lakshminath,Shubhan S
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 6 pages, 4 figures. This work was completed in 2024
Abstract:Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-time deployment. We address this by fine-tuning LLaMA 3 (8B) as a drop-in reranker using a two-stage pipeline: supervised fine-tuning on a custom query-document relevance dataset via the Unsloth framework with LoRA adapters, followed by 4-bit quantization for efficient inference. The resulting model replaces the cross-encoder in a dual-retriever RAG pipeline combining BM25 and dense vector search. Evaluated on a domain-specific question-answering benchmark using the RAGAS framework, our fine-tuned LLaMA 3 reranker achieves gains of 14% in answer relevancy, 16% in context precision, 19% in answer similarity, and 21% in answer correctness over the cross-encoder baseline, while reducing inference overhead through 4-bit quantization. These results demonstrate that instruction-tuned LLMs can be adapted into accurate, efficient rerankers without the quadratic complexity of traditional cross-encoders.
[IR-16] FAIR GraphRAG : A Retrieval-Augmented Generation Approach for Semantic Data Analysis
链接: https://arxiv.org/abs/2607.11464
作者: Marlena Flüh,Soo-Yon Kim,Carolin Victoria Schneider,Sandra Geisler
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)
备注: Accepted at the IEEE International Conference on Knowledge Graph, 2025. Corrects an error in the published abstract: the evaluation dataset is RNA-sequencing data, not single-cell data
Abstract:Retrieval-Augmented Generation (RAG) addresses the limitations of Large Language Models (LLMs) when providing responses to domain-specific questions. Graph-based RAG approaches, such as GraphRAG, enhance retrieval by capturing semantic relationships within knowledge graphs (KGs). While the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) are becoming prevalent for scientific data management, especially in complex domains such as medicine, existing RAG approaches lack a structured FAIRification of the underlying knowledge resources. This lack limits their potential for FAIR information retrieval in these domains. To address this gap, we introduce FAIR GraphRAG, a novel framework that integrates FAIR Digital Objects (FDOs) as the fundamental units of a graph-based retrieval system. Each graph node represents an FDO that incorporates core data, metadata, persistent identifiers, and semantic links. We leverage LLMs to support schema construction and automated extraction of content and metadata from data sources. The framework was co-designed by physicians and computer scientists to ensure technical and clinical relevance. We apply FAIR GraphRAG to a biomedical dataset in gastroenterology, demonstrating its applicability to RNA-sequencing data. Beyond ensuring adherence to the FAIR principles, FAIR GraphRAG significantly improves question answering accuracy, coverage, and explainability, particularly for complex queries involving metadata and ontology links. This work shows the feasibility of combining FAIR data practices with graph-based retrieval techniques. We see potential for applying our approach to other specialized fields such as education and business.
人机交互
[HC-0] Sensing the properties of virtual objects without physical feedback
链接: https://arxiv.org/abs/2607.12978
作者: Rhoslyn Roebuck Williams,Harry J. Stroud,Luis E. Toledo,Mark D. Wonnacott,David R. Glowacki
类目: Human-Computer Interaction (cs.HC); Chemical Physics (physics.chem-ph)
备注:
Abstract:People who have interacted with simulated worlds and simulated objects in extended reality (XR) often have a sense that they can ‘feel’ the objects being simulated despite them not being physical. Our sense of touch is essential for how we ‘feel’ the physical world, however, there is an open question as to what it means to ‘feel’ virtual objects when interacting with them in immersive digital environments. In prior research, we have reported that participants often describe a subjective experience of ‘feeling’ the properties of simulated molecular objects while using interactive molecular dynamics in extended reality (iMD-XR), a field-based interaction paradigm for manipulating real-time simulations of molecular objects without haptic feedback. To better understand these subjective reports of ‘feeling’, we used a psychophysics approach to quantify the threshold at which participants perceive differences in the rigidity of simulated molecular objects (C _60 molecules) in iMD-XR. To evaluate this, we carried out experiments to compare the just-noticeable differences (JNDs) in two conditions: (1) via direct interaction with a real-time C _60 simulation, and (2) via observation-only \unicodex2013 i.e. watching another person interacting with the simulations. Our findings show that direct interaction enabled participants to perceive more subtle rigidity differences of 11.5%, compared to 18.5% for observation-only. Furthermore, participants who undertook interaction first were better able to distinguish rigidity differences in the subsequent observation-only condition, suggesting that interaction trained participants to better perceive differences in molecular properties. These findings demonstrate a novel and flexible approach for sensing the properties of virtual objects in XR, and offer new insights into iMD-XR’s potential in molecular research and education.
[HC-1] CD-MED: Cross-Domain Multimodal Emotion Descriptor for Visual Comparison of Digital Objects
链接: https://arxiv.org/abs/2607.12958
作者: Elnara Kadyrgali,Muragul Muratbekova,Pakizar Shamoi
类目: Human-Computer Interaction (cs.HC)
备注: Submitted to 2026 Joint 14th International Conference on Soft Computing and Intelligent Systems and 27th International Symposium on Advanced Intelligent Systems (SCISISIS 2026)
Abstract:Digital objects express emotions through different modalities. For example, a movie may include visual scenes, audio, dialogue, and facial expressions, while a song may contain melody, rhythm, lyrics, and vocal tone. Because existing emotion recognition models are usually modality-specific, it is difficult to compare such objects directly. This paper proposes CD-MED, a Cross-Domain Multimodal Emotion Descriptor for representing heterogeneous digital objects in a common emotional space. Each modality can be processed by its own emotion recognition model, and the resulting emotional outputs are transformed into a shared descriptor. The descriptor preserves information from individual modalities while also allowing an integrated emotional profile of the object. For interpretation, CD-MED is visualized in the valence-arousal space: position represents affective coordinates, color denotes emotion category, size indicates intensity, and shape shows the modality. This unified representation enables emotion-based comparison, retrieval, recommendation, and visualization across different domains such as movies, songs, images, and books.
[HC-2] GraphPolaris: A System for Query Analysis and Visualization of Graph Databases
链接: https://arxiv.org/abs/2607.12845
作者: Michael Behrisch,Sjoerd Vink,Leonardo Christino,Remco Chang
类目: Human-Computer Interaction (cs.HC)
备注: 9 pages + appendix
Abstract:Graph databases are increasingly adopted as alternatives to tabular, aggregation-focused data models used in business intelligence (BI) systems such as Tableau, Power BI, and Looker. They capture complex relationships between entities, processes, and events, enabling analysis of information propagation in networks. As a result, graph analysis is central to applications such as fraud detection, social influence analysis, and supply chain resilience. Despite these advantages, existing tools do not adequately support interactive analysis of graph databases. Tabular BI systems lack mechanisms for reasoning over nodes and edges, while graph databases require specialized query languages and fragmented workflows that hinder accessibility. We present GraphPolaris, a no-code Visual Analytics system that enables users to explore, analyze, and visualize graph databases without programming skills. At its core, GraphPolaris features the GRAPHPOLARIS QUERY LANGUAGE (GPQL), a formal query grammar that facilitates flexible and composable graph queries, providing a formal foundation for analyzing relationships and graph patterns. GPQL serves as an intermediary between user interactions and the underlying database. Its formal foundation enables no-code query construction, database-agnostic query generation, and guarantees that every interaction produces a valid executable query. Informed by a formative user study, we designed GraphPolaris’ interface and visualizations to lower technical barriers and foster iterative, collaborative exploration of complex networks. We evaluate GraphPolaris through two real-world case studies in telecommunications and supply-chain analysis and a 22-month-long formative mixed-method study, including a MILC-based assessment of its fit to analysts’ graph analytics workflows.
[HC-3] Practical Judgment Virtue and Intuition in the Use of Opaque AI-Enabled Systems
链接: https://arxiv.org/abs/2607.12755
作者: Nathan G. Wood,Andrew P. Rebera
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET); Robotics (cs.RO)
备注:
Abstract:AI-enabled systems are seeing increasing deployment across numerous domains, with many being “black boxes” with respect to core functions and capabilities. I.e., many systems take inputs and give outputs, but without users having any ability to see how the former lead to the latter. AI-enabled systems are also being used to augment autonomy in systems, and autonomy coupled with opacity raises numerous concerns surrounding, e.g., the reliability of systems, their regularity in functioning, human ability to control them, or whether deploying opaque and potentially autonomous systems is in compliance with ethical and legal norms. In this article, we argue that many of these worries can be mitigated by leveraging practical judgment, virtue, and intuition in the deployment and use of opaque AI-enabled systems. We show that focusing on these distinctly human capabilities provides a means for bridging between the practical challenges created by opacity and the ethical, legal, and social norms underpinning particular domains. We argue that a core element in doing this is a recognition that many positive human traits are not quantifiable and we therefore must develop training regimen and guidelines on AI deployment anchored in humanistic but non-quantifiable values. Throughout the article, we focus on the military domain as an exemplar of the importance of practical judgment, virtue, and intuition as drivers for ethical and effective human decision-making surrounding AI deployments, but the underlying arguments apply to all domains where opaque and potentially autonomous systems are being deployed (subject to domain-specific alterations).
[HC-4] Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)
链接: https://arxiv.org/abs/2607.12336
作者: Farnaz Farid,Raihan Alam,Al Al-Areqi,Farhad Ahamed,Muhammad Hassan Khan,Sadia Hossain,Irena Veljanova,Anika Tabassum Binte Hossain
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC)
备注: 39 pages
Abstract:Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation. A prevalent challenge in mitigating this issue arises in non-English contexts and low socioeconomic classes, where limited data hinders the training of AI models for effective detection. Consequently, culturally and linguistically diverse (CALD) communities struggle to access trustworthy health information through AI-driven tools. Current AI tools underperform due to a lack of training data and are largely unable to consider language nuances and traditions in non-English contexts. This research addresses these gaps by proposing a CALD-friendly AI-based health misinformation detector and providing a dashboard for medical professionals to analyse this misinformation, a critical step toward mitigating a growing concern among CALD populations. To this end, we conduct a series of experiments using a Bangla-translated health misinformation dataset to evaluate the performance of various Small Language Models (SLMs). SLMs are particularly relevant in this context given the frequent underperformance of Large Language Models (LLMs), which often stems from insufficient domain-specific knowledge and the prohibitive costs of resource-intensive fine-tuning. The results demonstrate that Phi-4 is the superior model, achieving an ideal balance between precision and recall in claim extraction. Then, to mitigate the limitations of SLMs, we design and test a novel health misinformation detection framework grounded in Responsible Natural Language Processing (NLP), which incorporates cultural sensitivity, potential for harm, and communication quality, thereby providing a holistic lens for evaluating misinformation in low-resource languages.
[HC-5] Real-time Generation of Listener Nodding via Prediction of Kinematic Parameters for Avatar Dialogue Systems
链接: https://arxiv.org/abs/2607.12329
作者: Kazushi Kato,Koji Inoue,Taiga Mori,Divesh Lala,Tatsuya Kawahara
类目: Human-Computer Interaction (cs.HC); Sound (cs.SD)
备注: Accepted by 28th ACM International Conference on Multimodal Interaction (ICMI '26), Long paper
Abstract:In human dialogue, we achieve smooth communication by expressing nonverbal cues such as eye contact, nodding, and facial expressions with precise timing. It is expected for conversational avatars to express these cues appropriately to realize natural and human-like interactions. This study focuses on nodding, which is crucial for demonstrating active listening and encouraging further user utterances. We propose a model that predicts both timing and kinematic parameters representing the motion features of listener nodding in real time. The proposed model consists of a timing prediction module and a kinematic parameter prediction module. Each implements a dyadic attention network over the speaker and listener channels based on the technique of Voice Activity Projection (VAP). Unlike conventional models, this approach enables real-time prediction of kinematic parameters based on the specific context of the dialogue rather than just predicting the timing. Furthermore, we demonstrate the effectiveness of fine-tuning the kinematic parameter prediction module initialized from the trained timing prediction module. The proposed model is lightweight and capable of real-time operation, and it has been integrated into an avatar dialogue system. Subjective evaluation experiments shows that our proposed method significantly outperforms both a baseline with stochastic timing and another with fixed-motion nodding. The code and trained models are available at this https URL.
[HC-6] owards Knitted Textile Electromechanical Systems
链接: https://arxiv.org/abs/2607.12237
作者: John Martins,Abigail Hou,Brandon Tendilla,Noah Tannas,Rishit Garg,Wenchi Liu,Ben Kim,Michael Miller,Alejandro Goldstein,Elizabeth McLaughlin,Nivedita Arora
类目: Human-Computer Interaction (cs.HC)
备注: 4 Pages, 4 Figures
Abstract:E-textiles and wearable sensing technologies enable flexible, customizable interfaces for human-computer interaction, with capacitive sensing offering precise touch and pressure detection. While machine knitting provides scalable, mechanically tunable structures ideal for such sensors, few studies develop or characterize insulated conductive yarns engineered for knitting’s complex structural geometry and high flexure strain. In this work, we present a yarn dip-coating process, driven by an adjusted dip-coating fluid dynamics model, that enables scalable, machine knittable fabrication of capacitive tactile pressure sensing arrays. We establish optimal dip-coating parameters and concentrations of thermoplastic polyurethane (TPU) dissolved in dimethylformamide (DMF) to create knitting-optimized coatings (~630 um thickness). These fabricated yarns are shown to maintain electromechanical characteristics with minimal deviation after knitting and washing, thus allowing the creation of knitted pressure sensors through multi-layered structures. This process demonstrates that machine knitting with insulated yarns is a viable and reliable manufacturing approach to integrate sensing functionality into wearable textiles.
[HC-7] From Chaos to Clarity: A Framework for Program-Level AI Learning Outcomes
链接: https://arxiv.org/abs/2607.12221
作者: Grace Barkhuff,Ian Pruitt,William Gregory Johnson,Rodrigo Borela,Ben Rydal Shapiro,Anu G. Bourgeois
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Industry is leaning into generative artificial intelligence (GenAI), and higher education is under pressure to prepare graduates for a GenAI-augmented workforce. Yet, there is still no clear structure for defining AI readiness across disciplines, programs, courses, and assignments. Current approaches often rely on broad institutional policies or individual course-level decisions, which can also create mixed messages for students, fragmented expectations across programs, and limited visibility for university leaders. In this paper, we argue that higher education needs a more coherent way to connect institutional priorities to curriculum-level action. We propose Program-Level AI Learning Outcomes (PLAI-LOs) as a framework for defining what students graduating from a program should know and be able to do with, without, and about GenAI in a given discipline. The PLAI-LOs framework complements existing program-level learning outcomes and supports alignment across institutional priorities, program-level AI learning outcomes, course-level learning outcomes, and assignment-level objectives. We illustrate the framework with examples from computing and music and show how PLAI-LOs can be implemented through artifact-level GenAI policies, helping programs decide where GenAI should be taught and used, and when students should be expected to work without GenAI. We offer PLAI-LOs as a concrete, measurable, and adaptable path for moving higher education from scattered GenAI rules toward a strategy with clear, learning-centered alignment.
[HC-8] Compos3D: Interactive Part-Based Composition for Creative Control in Generative 3D Models
链接: https://arxiv.org/abs/2607.12193
作者: Faraz Faruqi,Sean J. Liu,George Fitzmaurice,Justin Matejka
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While generative AI has unlocked new opportunities for 3D content creation, current workflows often rely on multiple regenerations, which provides limited control and unpredictable outcomes. We present Compos3D, a system that introduces a compositional workflow for generative 3D modeling through remixing. Instead of repeatedly regenerating models, users generate multiple candidates from text or image prompts, select parts of interest via 2D image regions or 3D mesh segments, and assemble them into a coherent design. The system synthesizes these compositions into a refined 3D model, preserving high-level intent while resolving low-level geometry. To evaluate this approach, we conducted a controlled user study comparing remixing and regeneration workflows across both 2D and 3D modalities. Results show that the remixing workflow provides participants with greater creative control, stronger alignment with their intent, and higher satisfaction. We conclude with design recommendations for future AI-assisted 3D modeling workflows.
[HC-9] Analysis of Mutual and Referential Human and Robot Gazes in a Collaborative Word Association Game
链接: https://arxiv.org/abs/2607.12181
作者: Jens V. Rüppel,Tim Schreiter,Andrey Rudenko,Achim J. Lilienthal
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: This paper has been accepted as a Late-Breaking Report to the 35th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), which will be held in Kitakyushu, Netherlands on August 24-28, 2026. Copyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses
Abstract:Robot gaze is a major component of human-robot dialogue coordination. Most studies of gaze in human-robot dialogue focus on face-to-face social conversations, but little is known about gaze in demanding task-focused interactions. In this paper, we investigate how the gaze of a robot game partner affects human visual attention and if humans tend to direct confirmation-seeking gazes towards the robot. In our study, we let participants play a collaborative word association game with a NAO robot acting as an embodied, LLM-driven conversational partner. Our experiments are conducted under two conditions, which implement mutual and referential gazes of the robot respectively. We record participants’ gaze using eye tracking glasses and analyze the interactions using gaze coordinates, speech segments, key events and areas of interests. We find that robot gaze orientation does not affect the time to first fixation on words the robot proposed. We also find that participants gaze more often at the robot when their dialogue line contains confirmation requests, compared to when it does not. Our results indicate (likely also due to the cognitively demanding nature of the game) that the verbal aspect of this task overshadows the effects of referential robot gaze. These findings offer valuable insights for designing and validating robot gaze and turn-taking behavior in collaborative tasks which require coordination and efficient communication.
[HC-10] RAIL: A Platform for Configurable Human–AI Teaming Experiments
链接: https://arxiv.org/abs/2607.12180
作者: Mohammad Amin Samadi,Pedro Martins De Bastos,Jaeyoon Choi,Spencer JaQuay,Seehee Park,Nia Nixon
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:An AI teammate’s design properties (personality, communication style, when it speaks) can shape a team’s trust, coordination, and decisions. Studying this rigorously demands infrastructure no existing tool provides: reproducible configuration of an AI teammate embedded in instrumented, real-time collaboration sustained over time. We present the Team Research and AI Integration Lab (TRAIL), a web platform that makes the AI teammate a configurable, reproducible design object, pairing a Big Five persona with a selective-participation message pipeline, dual memory, chained longitudinal experiments, and export-ready analytics. In a real six-session classroom deployment (about 51 students), TRAIL sustained longitudinal chaining, held the AI to a stable minority of the conversation, and enabled export-driven AI-human text-similarity analysis. A single blind persona change produced a design-consistent double dissociation: a cognitive-scaffolding agent drew stronger contribution ratings and closer linguistic alignment; a socially-supportive agent, a warmer team climate and lower over-reliance.
[HC-11] Faster AI Uneven Frontier: Rapid Crossings a Jagged Frontier and the Repositioning of Human Judgment
链接: https://arxiv.org/abs/2607.12125
作者: Ancuta Margondai,Julie Rader,Emma Rader,Sara Willox,Mustapha Mouloua
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Between 2023 and 2026, frontier AI systems crossed documented human expert baselines on a growing set of bounded, well-specified, evaluable cognitive tasks, including graduate-level science questions, competition mathematics, software-engineering benchmarks, and structured diagnostic reasoning, while the length of tasks such systems can complete at 50% reliability doubled roughly every seven months. These crossings are rapid and broad, but the frontier is jagged: humans retain decisive advantages in long-horizon reliability, genuinely novel problems, calibrated self-knowledge, sample-efficient learning, and embodied action, and benchmark results overstate deployed capability for reasons that are themselves now documented, namely contamination, construct validity, vendor self-evaluation, and the gap between 50% reliability and the reliability that economic work requires. Concurrently, humans increasingly use these systems as cognitive extensions. The offloading literature predicts costs to unaided skill, and early field evidence is consistent with such costs, though the largest meta-analytic evidence on prior technologies points the other way, and the question of whether generative AI differs is open. Finally, the experimental record on human-AI collaboration shows that naive combination often underperforms the stronger partner, implying that the human contribution must be repositioned toward specification, verification, and oversight, a shift visible in experiments but, so far, barely visible in field labor-market data. This paper states the resulting position, rapid crossings on a jagged frontier with a human role that must be redesigned rather than defended, and draws out its theoretical and practical implications.
[HC-12] hought Experiments for Conceptual Work: A New Application of a (Very) Old Method
链接: https://arxiv.org/abs/2607.12092
作者: Leah Hope Ajmani,Mo Houtti,Eric P.S. Baumer,Stevie Chancellor
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:In this paper, we propose thought experiments (TEs) as a crucial method for Human-Computer Interaction (HCI) researchers to engage in conceptual work. As an interdisciplinary field, HCI often uses concepts as the fundamental building blocks for larger theories. However, the conceptual commitments we make in this process carry normative consequences. TEs are a well-established philosophical method, whereby a hypothetical but tractable scenario logically progresses to a conclusion. We outline TEs as an interrogative method that brings conceptualizations to their normative implications through logical moves. We illustrate the value of thought experiments through two examples: (1) original thought experiments to critique stakeholders in Value-Sensitive Design and (2) Helen Nissenbaum’s use of thought experiments to generate contextual integrity. We discuss how TEs precisely anticipate the potential harms of technologies, allowing HCI to operationalize current calls for increased scrutiny of research ethics and broader implications.
[HC-13] Designing Agent -Ready Websites for AI Web Agents : A Framework for Machine Readability Actionability and Decision Reliability
链接: https://arxiv.org/abs/2607.12056
作者: Said Elnaffar,Farzad Rashidi
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Online shopping is increasingly shifting toward a model in which AI agents independently search for products, compare options, evaluate constraints, and carry out parts of the purchasing process for users. Website design must now support both human and agent-mediated interaction. This paper introduces the agent-ready website, a design framework for enhancing the readability, interpretability, verifiability, and actionability of e-commerce platforms for AI agents. Existing web design, SEO, and generative engine optimization (GEO) metrics do not fully assess a website’s capacity for agent-mediated interaction. The proposed framework is structured around three dimensions agent interpretability, agent executability, and agent decision reliability supported by features such as machine readability, semantic clarity, agent actionability, and contextual decision-reliability signals. The framework is evaluated through a controlled experiment comparing a human-oriented baseline and an agent-ready version of an identical website prototype, with identical catalogs, pricing, stock, and shopping workflows. The evaluation involved five tasks, three browser-agent models (GPT-4.1, Gemini-2.5 Flash, and Grok-4 Fast), and 300 runs, measuring PASS,PARTIAL,FAIL outcomes, strict and functional success rates, error patterns, step counts, and token consumption. The agent-ready website achieved 134 PASS runs out of 150 versus 74 out of 150 for the baseline (strict success rates of 89.3% vs. 49.3%), with the largest gains in product detail extraction, comparison, and multi-constraint selection. It also reduced PARTIAL outcomes from 43 to 3 and lowered the average step count from 9.31 to 6.49. These results provide preliminary evidence that enhanced structural clarity, action cues, evidence signals, and temporal validity indicators can substantially improve the reliability and efficiency of AI browser agents.
[HC-14] Reading the Eyes in VR: Multimodal Modeling of Social Intelligence
链接: https://arxiv.org/abs/2607.11931
作者: Mohammad Fahim Abrar,Shayla Sharmin,Roghayeh Leila Barmaki
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Social intelligence, the ability to interpret others’ emotions, beliefs, and intentions, is often assessed with the Reading the Mind in the Eyes Test (RMET), in which participants infer mental states from images of the eye region. Yet RMET is typically presented on paper or desktop displays, where viewing geometry can vary across participants, and it rarely includes immediate feedback. We investigated whether presentation medium and brief trial-level feedback influence RMET behavior. We implemented RMET in Unity for both desktop and Virtual Reality (VR), using VR to hold stimulus distance and field of view constant without changing the items. We conducted a 2x2 mixed study with 20 participants, with device (VR vs. desktop) manipulated between subjects and feedback (immediate correctness cue vs. none) manipulated within subjects. Eye-tracking and EEG data were recorded and synchronized with behavioral logs. We analyzed fixation-based gaze measures, EEG signals, response time, accuracy, and subjective measures. Immediate feedback was associated with longer fixation durations and higher EEG-based engagement, while no significant differences were observed in task completion time or total correct answers. Presentation medium did not produce reliable differences in the objective measures, but VR received higher usability ratings and was also rated as more effortful. These results provide initial evidence that RMET can be studied as a process-aware assessment task in controlled VR and desktop settings.
[HC-15] From Words to Widgets for Controllable LLM Generation
链接: https://arxiv.org/abs/2604.10925
作者: Chao Zhang,Yiren Liu,Lunyiu Nie,Jeffrey M. Rzeszotarski,Yun Huang,Tal August
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
备注: The first three authors contributed equally to this work
Abstract:Natural language remains the predominant way people interact with large language models (LLMs). However, users often struggle to precisely express and control subjective preferences (e.g., tone, style, and emphasis) through prompting. We propose Malleable Prompting, a new interactive prompting technique for controllable LLM generation. It reifies preference expressions in natural language prompts into GUI widgets (e.g., sliders, dropdowns, and toggles) that users can directly configure to steer generation, while visualizing each control’s influence on the output to support attribution and comparison across iterations. To enable this interaction, we introduce an LLM decoding algorithm that modulates the token probability distribution during generation based on preference expressions and their widget values. Through a user study, we show that Malleable Prompting helps participants achieve target preferences more precisely and is perceived as more controllable and transparent than natural language prompting alone.
[HC-16] When Security Meets Usability: An Empirical Investigation of Post-Quantum Cryptography APIs NDSS
链接: https://arxiv.org/abs/2602.14539
作者: Marthin Toruan,R.D.N. Shakya,Samuel Tseitkin,Raymond K. Zhao,Nalin Arachchilage
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注: Accepted at the NDSS Symposium on Usable Security and Privacy (USEC) 2026
Abstract:Advances in quantum computing increasingly threaten the security and privacy of data protected by current cryptosystems, particularly those relying on public-key cryptography. In response, the international cybersecurity community has prioritized the implementation of Post-Quantum Cryptography (PQC), a new cryptographic standard designed to resist quantum attacks while operating on classical computers. The National Institute of Standards and Technology (NIST) has already standardized several PQC algorithms and plans to deprecate classical asymmetric schemes, such as RSA and ECDSA, by 2035. Despite this urgency, PQC adoption remains slow, often due to limited developer expertise. Application Programming Interfaces (APIs) are intended to bridge this gap, yet prior research on classical security APIs demonstrates that poor usability of cryptographic APIs can lead developers to introduce vulnerabilities during implementation of the applications, a risk amplified by the novelty and complexity of PQC. To date, the usability of PQC APIs has not been systematically studied. This research presents an empirical evaluation of the usability of the PQC APIs, observing how developers interact with APIs and documentation during software development tasks. The study identifies cognitive factors that influence the developer’s performance when working with PQC primitives with minimal onboarding. The findings highlight opportunities across the PQC ecosystem to improve developer-facing guidance, terminology alignment, and workflow examples to better support non-specialists.
计算机视觉
[CV-0] he Seriality Gap in Video Diffusion Models
链接: https://arxiv.org/abs/2607.13031
作者: Jorge Diaz Chao,Konpat Preechakul,Yuxi Liu,Yutong Bai
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Jorge Diaz Chao and Konpat Preechakul contributed equally. 24 pages, 12 figures, and 5 tables. Project page: this https URL
Abstract:When one ball strikes another, then another, video models should predict the consequences of each bounce. In controlled experiments on multi-ball hard-sphere dynamics, we find that the performance of standard bidirectional video diffusion degrades as the causal chain lengthens, even when provided more denoising steps. In a length-matched single-ball control, where ball-ball interactions are absent, the degradation largely disappears, isolating dependent-event structure rather than video length as the cause. Across intervention studies, methods that increase effective serial computation improve performance disproportionately, including autoregressive/blockwise generation and architectural depth. We identify this pattern as the seriality gap: a mismatch between tasks requiring growing serial computation and video diffusion models whose denoising loop does not provide scalable serial compute. We then prove that, for deterministic video prediction, denoising steps do not add serial computation beyond the backbone, indicating a structural obstacle for video diffusion on serial reasoning and simulation tasks.
[CV-1] FlowWAM: Optical Flow as a Unified Action Representation for World Action Models
链接: https://arxiv.org/abs/2607.13017
作者: Yixiang Chen,Peiyan Li,Yuan Xu,Qisen Ma,Jiabing Yang,Kai Wang,Jianhua Yang,Dong An,He Guan,Gaoteng Liu,Jianlou Si,Jun Huang,Jing Liu,Nianfeng Liu,Yan Huang,Liang Wang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:World Action Models (WAMs) are able to leverage pretrained video generators for both world modeling and action prediction. However, directly leveraging such video generators for control raises a new challenge: how to represent actions in a suitable form that aligns with pretrained video generators while carrying enough motion cues for accurate control. Existing numerical actions fail to satisfy the former, and prior visual action representations overlook the temporal motion structure across frames. We address this issue with FlowWAM, a dual-stream diffusion framework that adopts optical flow as a unified, video-native action representation. Flow videos share the same format as RGB videos and encode rich per-pixel displacement. By jointly modeling them within a shared pretrained video generator, FlowWAM can naturally implement two modes of WAMs. In policy mode, FlowWAM generates flow for action prediction, while in world-model mode, it uses target flow sequences to guide future video generation. Moreover, since flow can be easily extracted from raw videos without action labels, FlowWAM can leverage large-scale action-unlabeled video datasets for pretraining. We empirically find that our flow-based action representation delivers gains across both modes. On RoboTwin manipulation, FlowWAM raises the success rate to 92.94% on the Clean setting and 92.14% on Random, outperforming both VLA and WAM baselines. On WorldArena world modeling, it achieves the best overall EWMScore (63.71) with an 18.4% relative improvement in trajectory accuracy. More results can be found on our project website: this https URL .
[CV-2] DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology MICCAI2026
链接: https://arxiv.org/abs/2607.13010
作者: Héctor Carrión,Narges Norouzi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI 2026
Abstract:Dermatological practice routinely involves measuring and tracking lesion size, morphology and texture, as critical components of wound or skin cancer screening, monitoring and diagnosis. To accomplish this task, practitioners often image the skin surface with commonly available off-the-shelf camera sensors. This has led to an overwhelming research focus on 2D methods while these objectives naturally benefit from 3D information. In this paper, we demonstrate that dense monocular 3D reconstructions, metric scale measurements and rich surface normal texture estimates are achievable for both dermoscopic and macroscopic cases without the need for additional hardware or multiple captures. We present DermDepth, the first single-view metric scale 3D model for the dermatological domain and D-Synth, the first synthetic dermoscopic dataset with pixel-perfect 3D information. Our experiments show training DermDepth on D-Synth corrects metric scale error from over 16x to under 1.1x for real dermoscopic data, while preserving geometric quality and increasing texture richness. Fine-tuning on a small amount of real clinical samples generalizes our method across three real-world benchmarks spanning the few mm to hundred cm range, diverse skin-tones, chronic wound cases and produces measurements broadly consistent with disease size reported in medical literature. All code, data and models are available at this https URL.
[CV-3] X-Lens: Real-Time Metric Depth Estimation with Heterogeneous Cameras
链接: https://arxiv.org/abs/2607.12993
作者: Heng Zhou,Shuhong Liu,Yonghao He,Bohao Zhang,Fa Fu,Chenhui Hou,Xianbao Hou,Lijun Han,Wei Sui
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages
Abstract:We present X-lens, a compact feed-forward model for metric depth estimation from a variable number of calibrated fisheye and pinhole views. To support real-time downstream perception, X-lens is built around a geometry-aware heterogeneous camera formulation with two key components. Learnable calibration tokens provide a coarse alignment between fisheye and pinhole projective spaces, while a Jacobian-parameterized distortion bias injected into cross-attention models local projection changes and promotes cross-camera consistency, enabling robust generalization with only 0.04B parameters and up to 41 FPS. The model predicts dense depth together with a global metric scale, avoiding auxiliary reconstruction targets that increase computation and optimization complexity. To learn such cross-camera generalization at scale and depth, X-lens is trained on multiple public datasets and OmniScene, our newly released large-scale synthetic dataset containing approximately 266K synchronized six-view frames, 1.7M individual images, and 103 indoor and outdoor scenes. Extensive experiments on both real-world and synthetic indoor and outdoor datasets demonstrate superior heterogeneous-camera metric depth accuracy, reducing AbsRel by 25.4% on OmniScene-Full over the strongest baseline while using 88.9% fewer parameters, with competitive performance on conventional fisheye-only and pinhole-only settings.
[CV-4] Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification MICCAI2026
链接: https://arxiv.org/abs/2607.12987
作者: Héctor Carrión,Narges Norouzi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI 2026
Abstract:Accurate dermatological diagnosis naturally necessitates equitable performance across diverse populations, yet a systematic lack of expertly annotated images, especially for underrepresented skin tones and rare diseases, impedes progress toward measurably fair methods. We introduce cgDDI (Controllable Generation of Diverse Dermatological Imagery), a hybrid framework that (1) synthesizes realistic healthy skin samples without disturbing other input properties, (2) maps single-sample rare lesions onto novel skin-tones and locations non-parametrically, and (3) allows for efficient parametric generation with as few as 10 training samples. The framework supports both human and automated segmentation masking, enabling scalability to datasets without pre-made lesion masks. We grow a 656-image dataset by more than 400x and validate across two datasets: biopsy-confirmed Diverse Dermatology Images (DDI) and expert-verified Fitzpatrick17k (F17k). On the DDI benchmark, we achieve malignancy classification accuracy of 86.4% under synthetic-only training and 90.9% state-of-the-art performance with real data fine-tuning, alongside leading fairness metrics. Cross-dataset experiments show +13.9% accuracy improvements on unseen F17k data despite minimal disease overlap. We openly release 266k+ synthetic images, code, and generative models to further support fairness research at this https URL.
[CV-5] ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models
链接: https://arxiv.org/abs/2607.12959
作者: Haojie Ren,Songrui Luo,Lingfeng Wang,Yan Xia,Yao Li,Jing Li,Lu Zhang,Jiajun Deng,Yanyong Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:LiDAR-based collaborative 3D perception in Vehicle-to-Everything (V2X) systems typically relies on fusing bird’s-eye-view (BEV) features across agents. However, current BEV representations, typically extracted by LiDAR backbones trained from scratch, are geometry-dominated and lack general semantic priors, inherently limiting the efficacy of feature-level collaboration. Meanwhile, vision foundation models (VFMs) pretrained on large-scale image data have demonstrated strong capability in learning general-purpose and informative visual representations for 2D tasks, and have the potential to enhance agent-wise LiDAR BEV representations for collaboration. Despite this potential, adapting VFMs to LiDAR-based 3D detection remains challenging due to the substantial image-point cloud modality gap. To bridge this gap, we propose ViCo3D, a collaborative 3D object detection framework powered by VFMs. Specifically, ViCo3D adapts VFMs to LiDAR-based collaborative perception from three aspects: First, ViCo3D projects point clouds onto the BEV plane as three-channel images, enabling DINOv2 to extract BEV-space visual features from LiDAR inputs. Besides, to effectively integrate these DINOv2-derived features with LiDAR geometric features, ViCo3D introduces a multi-scale BEV fusion module within the single-agent encoder. In addition, ViCo3D adopts an ego-centric cross-agent fusion strategy to aggregate complementary information from multiple agents. Experiments on DAIR-V2X and V2XSet demonstrate that ViCo3D achieves state-of-the-art 3D detection performance. Remarkably, it delivers up to 1.8x greater collaborative gains than prior methods on DAIR-V2X. The code will be made public available for future investigation.
[CV-6] Point Tracking in Surgery–The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)
链接: https://arxiv.org/abs/2607.12939
作者: Adam Schmidt,Mert Asim Karaoglu,Zijian Wu,Jiaming Zhang,Yuxin Chen,Tim Salcudean,Ho-Gun Ha,Minkang Jang,Kyungmin Jung,Ihsan Ullah,Hyunki Lee,Suresh Guttikonda,Sarah Latus,Alexander Schlaefer,Xinkai Zhao,Yuichiro Hayashi,Masahiro Oda,Takayuki Kitasaka,Kensaku Mori,Peng Liu,Chenyang Li,Stefanie Speidel,Aoife Gardiner,Agostino Stilli,Danail Stoyanov,Francisco Vasconcelos,Anwesa Choudhuri,Meng Zheng,Zhongpai Gao,Benjamin Planche,Van Nguyen Nguyen,Terrence Chen,Ziyan Wu,Alexander Ladikos,Omid Mohareri
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 12 figures. arXiv admin note: substantial text overlap with arXiv:2503.24306
Abstract:Point tracking in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. This paper introduces the 2025 iteration of a point tracking challenge to address this, wherein participants submit their algorithms for quantification. Their algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge named the STIR Challenge 2025 (STIRC2025). The STIR Challenge 2025 comprises two quantitative components: accuracy and efficiency. The accuracy component tests the accuracy of algorithms on in vivo and ex vivo sequences. The efficiency component tests algorithm inference latency. The challenge was conducted as a part of MICCAI EndoVis 2025, and seven teams participated in this challenge. In this paper we summarize the challenge results and participant methods. The challenge dataset is available at: this https URL, and the code for baseline models and metrics calculation is available here: this https URL
[CV-7] Domain-Incremental Remote Sensing Change Detection via Difference-Guided Adaptation and Frequency-Decoupled Distillation
链接: https://arxiv.org/abs/2607.12934
作者: Daifeng Peng,Yaning Li,Haiyan Guan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 33 pages, 14 figures, and 5 tables
Abstract:Remote sensing change detection (RSCD) models are prone to catastrophic forgetting when incrementally adapted to new domains. Existing domain-incremental learning (DIL) methods mainly preserve image-level representations but often overlook bitemporal discrepancy cues, which are critical for robust change detection under domain shifts. To address this limitation, we propose DG-FDD, a domain-incremental change detection framework that integrates Difference-Guided Adaptation and Frequency-Decoupled Distillation. Specifically, the Difference-Guided Dynamic Adapter (DGDA) models bitemporal feature discrepancies to promote change-aware feature adaptation and reduce domain-specific interference. Meanwhile, the Frequency-Decoupled Knowledge Distillation strategy with Cross-domain Synthesis (FDKD-CS) separates structural information from domain style in the frequency domain, enabling stable knowledge transfer without historical data. Extensive experiments on three public high-resolution RSCD datasets under two- and three-domain incremental protocols demonstrate that DG-FDD effectively mitigates catastrophic forgetting. Compared with independently trained single-task models, DG-FDD records mean relative changes in F1 and IoU of only -0.23% and -0.45%, respectively, across six two-domain sequences, and -0.69% and -1.31%, respectively, across the three evaluated three-domain sequences. These results indicate a favorable stability-plasticity balance between historical knowledge retention and new-domain adaptation in continual cross-domain change detection.
[CV-8] Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agent ic Decomposition
链接: https://arxiv.org/abs/2607.12911
作者: Bruce Coburn,Jingbo Yue,Jinge Ma,Siddeshwar Raghavan,Gautham Vinod,Fengqing Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages main paper, 5 pages supplementary
Abstract:Multimodal Large Language Models (MLLMs) are increasingly used for dietary assessment from meal images, where retrieval-augmented grounding was shown to sharpen nutrition estimates. However, we find this premise no longer holds for current MLLMs. A modern MLLM’s direct estimate now matches or surpasses the full retrieval pipeline. This raises a question: if retrieval no longer improves the overall estimate, can it still deliver the two things clinicians value, accurate portions and a traceable, item-by-item record? We pursue this while preserving what matters for clinical adoption: minimal user burden (a single, unannotated meal image), explainability (an auditable record), and privacy (locally hosted inference). We introduce Open-KNEAD, a knowledge-grounded agentic framework for meal nutrition estimation that is training-free and locally deployable. Each decomposed food item is grounded to a Food and Nutrient Database for Dietary Studies (FNDDS) code via selective, nutrient-aware retrieval, composing an auditable per-item record. Across two open MLLM families and three cuisines, Open-KNEAD improves portion estimates over both prior grounding methods and direct estimation in most backbone-dataset settings. An agent-internal recipe-prior step further recovers the invisible cooking-added energy that biases estimates on non-US cuisine. The advantage is largest on the dietitian-verified ACETADA dataset, where the local open agent surpasses the direct portion estimates of two frontier closed models by roughly 30% and 53% , all while keeping every meal image on local hardware. We release the Open-KNEAD framework and its agent-ready FNDDS knowledge base.
[CV-9] Rank-1 Identity Consensus Predicts Gallery Enrollm ent in 1:N Face Matching More Accurately than Score Thresholding
链接: https://arxiv.org/abs/2607.12903
作者: Gabriella Pangelinan,Aman Bhatta,Michael C. King,Kevin W. Bowyer
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 8 figures, 5 tables
Abstract:In operational 1:N face identification, a crucial question arises for each probe: is this person enrolled in the gallery or not? The stakes are high and asymmetric. Rejecting a mate-present (MP) probe loses a valid lead; accepting a mate-absent (MA) probe makes every returned candidate a false identification, at worst a wrongful arrest. Most approaches threshold match scores, but scores shift substantially with image quality and gallery size and composition, making thresholds fixed before deployment brittle under realistic conditions. Our prior work introduced 1-consistency, the only method based on rank consensus across multiple independently trained matchers: a probe is labeled MP if all matchers return the same rank-1 identity. This work stress-tests 1-consistency across 36 (gallery, probe quality) scenarios spanning four quality levels and two structural axes: images per identity and total enrolled identities. We benchmark against two score-thresholding methods that bracket what any deployed threshold could achieve. Fixed Score-Thresholding (FST), calibrated once on baseline conditions, collapses asymmetrically as quality degrades: MP recall falls below 2% while MA recall holds near 100%. Oracle Score-Thresholding (OST), re-tuned per scenario, is the best any threshold could theoretically do, yet for degraded probes 1-consistency matches it with zero tuning. The two differ mainly in error type (OST favors MP recall, 1-consistency favors MA recall), but on one axis 1-consistency does not merely match the oracle: when it labels a probe MP, it returns the correct mate 97-100% of the time versus OST’s 66-84% under severe degradation. In short, 1-consistency delivers oracle-level accuracy without the impossible requirement: it sets no threshold, so it needs no advance knowledge of the conditions a probe will arrive in, which is what makes it usable.
[CV-10] UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
链接: https://arxiv.org/abs/2607.12896
作者: Yunzhou Li,Jiesi Hu,Yanwu Yang,Hanyang Peng,Chenfei Ye,Jianfeng Cao,Yixuan Yuan,Ting Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fragmented by prompt paradigms and spatial dimensions. Visual in-context learning, interactive segmentation, and language-guided segmentation are typically handled by paradigm-specific models, while 2D and 3D images are also modeled separately. Such isolation prevents heterogeneous annotations and data from being jointly absorbed by a single scalable model and limits cross-paradigm knowledge transfer. To address this bottleneck, we propose UniMedSeg, a Transformer-centric universal segmentation framework that maps visual examples, geometric interactions, language instructions, and 2D/3D images into a shared sequence space, enabling heterogeneous medical supervision to be jointly learned through a unified in-context interface without prompt- or dimension-specific branches. To overcome the long-sequence memory bottleneck caused by visual contexts, we introduce Decoupled Split Attention, which reduces attention complexity to linear while preserving hardware-friendly computation and focused context-target interaction. Extensively trained and evaluated on a large corpus curated from 27 public datasets, UniMedSeg achieves state-of-the-art performance across visual in-context, interactive, and language-guided segmentation without task-specific fine-tuning, demonstrating strong generalization on diverse held-out tasks. The code and model weights are publicly available at this https URL
[CV-11] Hy-Embodied-VLM-1.0: Efficient Physical-World Agents
链接: https://arxiv.org/abs/2607.12894
作者: Ziyi Wang,Xumin Yu,Yongming Rao,Yonggen Ling,Yunheng Li,Oran Wang,Mingqi Gao,Yuchen Zhou,Yves Liang,Zuyan Liu,Yani Zhang,Rui Huang,Xiaoran Xu,Bowen Yuan,Yifu Yuan,Xu Tan,He Zhang,Yufei Huang,Shenghao Zhang,Hongsheng Wu,Han Hu,Zhengyou Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Tech Report. Code and models are open-sourced at this https URL
Abstract:Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situations, and interacting with the physical world. In this report, we introduce Hy-Embodied-VLM-1.0, an efficient and powerful embodied foundation model specifically designed for embodied agents operating in the physical world. To cultivate such capabilities from the pre-training stage onward, we define an action-centric capability taxonomy comprising three progressive dimensions: Action-Relevant State Understanding, Action-Transition Reasoning, and Sequential and Adaptive Reasoning. Guided by this taxonomy, we develop a systematic data pipeline and curate data mixtures spanning both pre-training and post-training. To deliver strong physical-world understanding and interaction capabilities while supporting latency-sensitive deployment, we build our model on the Hy3-A3B language backbone and the Hy-ViT2 vision encoder. Its efficient Mixture-of-Experts architecture combines strong model capacity with high inference efficiency. We evaluate Hy-Embodied-VLM-1.0 on a comprehensive suite of 38 benchmarks covering embodied perception, physical-world understanding, and embodied reasoning. The model achieves the best performance among similarly sized models on 19 of the 38 benchmarks and substantially outperforms strong competitors, including Qwen3.6-A3B and Cosmos 3. Compared with the previous-generation Hy-Embodied-0.5 MoT-2B, Hy-Embodied-VLM-1.0 improves average performance by 8.4%. Despite activating only 3B parameters, it achieves performance close to that of the previous-generation model with 32B activated parameters. Beyond static benchmark evaluation, Hy-Embodied-VLM-1.0 also demonstrates strong performance on embodied agentic tasks requiring multi-turn interaction and long-horizon reasoning.
[CV-12] Inhibited Self-Attention: Sharpening Focus in Vision Transformers
链接: https://arxiv.org/abs/2607.12881
作者: Peter R.D. van der Wal,Nicola Strisciuglio,George Azzopardi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision Transformers (ViTs) have demonstrated remarkable performance in computer vision tasks. However, their self-attention mechanism often diffuses focus across background regions, relying on spurious correlations rather than object-relevant cues. Inspired by inhibitory mechanisms observed in biological vision systems, we propose the Inhibited Self-Attention (ISA), a novel self-attention that integrates inhibitory signals to enhance feature selectivity and suppress spurious responses. In contrast to conventional self-attention, which relies solely on positive attention values due to softmax normalization, our approach retains and utilizes negative attention scores to suppress irrelevant features and sharpen focus on objects of interest. Experiments across multiple datasets, including ImageNet-1k and COCO, and several robustness benchmarks demonstrate that ISA enhances object-centric selectivity, reduces shortcut reliance, and improves out-of-distribution generalization. Our analysis of relevance maps confirms that ViTs with ISA exhibit sharper, more localized focus on object-relevant regions while reducing distractions from non-relevant (background) features, enabling more reliable models. We release our code at this https URL
[CV-13] Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agent ic AI
链接: https://arxiv.org/abs/2607.12874
作者: Martina Radoynova,Samuel Pantze,Trina De,Ulrik Günther,Artur Yakimovich
类目: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: 17 pages, 3 figures, 4 pages
Abstract:Deep learning computer vision for scientific applications requires collecting and annotating large datasets in a laborious, expensive and error-prone process. Synthetic data generation through 3D modelling and rendering may simplify this process and increase the accuracy of annotations by generating them programmatically. However, minimising the domain gap between real and synthetic images visually is subjective and lacks systematic quantitative guidance. We present GraNatPy, a Python package with metrics to guide improvement of the rendered scene. We show that quantifiable increase in realism, diversity and size of rendered dataset correlates with improved visual perception of the scene and higher zero-shot performance of an object detection model. Furthermore, we demonstrated using photographs of virological plaque assays that gradient similarity affects performance on small object detection, which can be improved by mixing real and synthetic data. Finally, we turn procedural data rendering into an agentic skill (SynthClaw) to automate the procedural parameter optimisation.
[CV-14] Statistical Non-linear Reconstruction Loss for Image Anomaly Detection
链接: https://arxiv.org/abs/2607.12866
作者: Nguyen Minh Tri,Hoang Khuong Duy,Huynh Cong Viet Ngu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at KES 2026
Abstract:Reconstruction-based methods are a cornerstone of unsupervised image anomaly detection, but they remain vulnerable to \emphoutlier leakage, where standard mean squared error (MSE) loss drives the model to faithfully reconstruct anomalous patterns. We propose a Non-linear Reconstruction Loss that applies a sigmoid-based squashing function to suppress high-magnitude features, preventing outliers from dominating optimization while preserving sensitivity to normal patterns. In addition, we introduce a statistical calibration scheme that selects the scaling factor k from the confidence interval (CI) of the normal feature distribution, enabling data-driven control of the suppression strength. Our approach achieves competitive or superior anomaly detection performance compared to state-of-the-art methods, reaching 99.0% Image-AUROC and 97.3% Pixel-AUROC on MVTec-AD, and 95.3% Image-AUROC and 99.0% Pixel-AUROC on VisA. These results indicate that non-linear gradient suppression is an effective mechanism for mitigating outlier leakage and improving anomaly localization in unified industrial inspection settings. The implementation is available at this https URL.
[CV-15] LARAD: Layout-Aware Road Anomaly Detection via Spatial-Logic Reasoning
链接: https://arxiv.org/abs/2607.12858
作者: Shiyi Mu,Xujie Chen,Shugong Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate open-world obstacle detection is critical for autonomous driving. Current anomaly segmentation methods suffer from a fundamental blind spot: they over-rely on texture novelty to identify out-of-distribution (OoD) objects while ignoring contextual spatial logic. Furthermore, mitigating the resulting false positives often requires cascading massive vision models, introducing unacceptable inference latency. To address these issues, we propose Layout-Aware Road Anomaly Detection (LARAD), shifting the paradigm from appearance matching to spatial-logic reasoning. First, we introduce the Spatial-Logic Violation Synthesis (SLVS) pipeline, which generates training samples that are texture-consistent yet spatially invalid, forcing the model to learn contextual violations. Second, we augment a standard closed-set segmentation network with a lightweight, OoD-guided attention branch. Extensive experiments demonstrate that LARAD significantly enhances robustness against logical anomalies and establishes a new state-of-the-art, all while retaining the high efficiency of a single-model architecture.
[CV-16] AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning
链接: https://arxiv.org/abs/2607.12820
作者: Yanghai Wang,Jiahao Wang,Jiafu Tang,Yuanxing Zhang,Zhe Cao,Hanyan Bian,Zijie Zhang,Weiliang Luo,Zhiyu Pan,Zixuan Dong,Jiaheng Liu,Zhaoxiang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speech, music, and sound effects co-evolve. Existing large multimodal models often fail at this relational step, treating audio and visual streams as loosely coupled observations, relying on automatic speech recognition, and under-specifying non-speech sounds and their links to visual events. We present AVSCap, a framework for audio-visual captioning centered on explicit cross-modal event binding. First, we construct AVSCap-130K, a tri-modal training corpus generated by a decoupled-then-fused pipeline that anchors visual and acoustic evidence before composing grounded omni-modal captions. Second, we train AVSCap-7B, a 7B captioner with a two-stage strategy: supervised fine-tuning establishes baseline capabilities, while sample-efficient reinforcement learning uses hybrid rewards to optimize acoustic completeness and audio-visual synergy. Our scaling analysis shows that reinforcement learning brings larger gains than increasing SFT data. Third, we introduce AVSCapBench, a benchmark that decomposes captions into visual, audio, and synergy events and evaluates them with fine-grained event recall. Experiments on AVSCapBench and external benchmarks show that AVSCap-7B improves non-speech audio coverage and cross-modal binding, delivering the best overall performance among evaluated open-source models.
[CV-17] Breaking Déjà Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning
链接: https://arxiv.org/abs/2607.12818
作者: Sania Waheed,Michael Milford,Sarvapali D. Ramchurn,Shoaib Ehsan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual place recognition (VPR) is a key enabler of accurate localization and long-term autonomous navigation in robotics applications, such as loop closure detection for simultaneous localisation and mapping (SLAM). However, real-world VPR deployment relies on selecting an image matching threshold that balances precision and recall. These thresholds are typically tuned using labeled validation data and fixed during deployment, making them unreliable under environmental changes where ground truth is unavailable. This is particularly problematic in safety-critical robotics, where accepting a false loop closure can corrupt the estimated trajectory and map. In this work, we introduce Visual Place Recognition Auditing, an independent post-retrieval verification framework that leverages Vision-Language Models (VLMs) to assess retrieved matches by reasoning jointly over query and candidate images. Unlike conventional verification methods, our approach performs instance-level verification without requiring architecture-specific confidence measures, dataset-dependent thresholds, or prior knowledge of the deployment environment. We evaluate our method on six benchmark datasets using five state-of-the-art VPR methods and four VLMs. Results show that VLM-based auditing improves recall@1 by 13.6% on average as compared to state-of-the-art methods while reducing false acceptance rates to 12%, maintaining precision above 95% and coverage above 75%.
[CV-18] UniVR: Thinking in Visual Space for Unified Visual Reasoning KR
链接: https://arxiv.org/abs/2607.12800
作者: Zhongwei Ren,Yunchao Wei,Yao Zhao,Weibo Gong,Xiao Liu,Anran Wang,Xiangtai Li,Xiaojie Jin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code and models are released at: this https URL
Abstract:Learning broad world knowledge directly from raw visual data is a fundamental capability of intelligence. We introduce UniVR, the first investigation into simultaneously learning complex reasoning, fine-grained physical dynamics, and long-term planning from pure visual demonstrations. At its core, UniVR features VR-GRPO, a reinforcement learning paradigm with complementary global and step-level rewards. This approach enforces logical coherence and physical consistency throughout the reasoning process without requiring task-specific heuristics or image-text pairs. To train and evaluate UniVR, we construct VR-X, a large-scale benchmark curated from 16 diverse sources spanning long-horizon manipulation, spatial puzzles, and physical reasoning. It is the first comprehensive suite to assess these heterogeneous capabilities under a purely visual protocol. Remarkably, UniVR achieves up to a 25% improvement on VR-X, and its superior visual reasoning also boosts performance on various multimodal understanding benchmarks. These findings underscore the vast potential of reasoning within visual spaces, with all code, data, and models are open-sourced for further research.
[CV-19] AVQ-Attention: Adaptive Vector-Quantized Attention ECCV2026
链接: https://arxiv.org/abs/2607.12789
作者: Winfried van den dool,Patrick Forré,Amir Habibian,Yuki M. Asano,Max Welling
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:The \mathcalO(N^2) complexity of attention over N tokens remains a computational bottleneck in transformer models. Vector-Quantized (VQ) attention reduces this to \mathcalO(MN) by representing keys with M codewords, but applies uniform codebook capacity regardless of where attention mass concentrates: high-attention regions of key space may be coarsely approximated while low-attention regions waste representational capacity. We propose Adaptive Vector-Quantized (AVQ) Attention, which adaptively allocates codebook capacity based on attention importance. Starting from a small set of codewords, our method identifies the most important codes during the forward pass and refines them with pre-learned child codewords, achieving fine-grained quantization where it matters most while maintaining coarse quantization elsewhere. We develop an implementation using custom Triton kernels that enables the full adaptive refinement process, including importance scoring, child codeword insertion, and parent contribution replacement, to be carried out within the tiled computation paradigm of Flash Attention with minimal overhead. Our approach maintains \mathcalO(MN) complexity while achieving improved accuracy-efficiency trade-offs compared to fixed-codebook VQ-attention.
[CV-20] CoRe: A Comprehensive Framework for Cross-Image Comparative Reasoning in Vision-Language Models
链接: https://arxiv.org/abs/2607.12786
作者: Lin Peng,Cong Wan,Zeyu Guo,SongLin Dong,Yihong Gong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACMMM2026
Abstract:Cross-image comparative reasoning remains challenging for vision-language models (VLMs), especially when correct prediction requires fine-grained attribute grounding and globally consistent reasoning. We present CoRe, a unified framework for this problem. CoRe includes: (i) CoRe-20K, a large-scale triplet-based training set automatically constructed from structured visual metadata through a multi-expert collaborative pipeline, covering counting, depth, distance, and spatial relations; (ii) TriSR, a structured reward framework that jointly supervises attribute grounding, judgment alignment, and triplet consistency under GRPO optimization; and (iii) CoRe-Bench, the first benchmark dedicated to fine-grained cross-image comparative reasoning. Experiments show that CoRe substantially outperforms existing VLMs on CoRe-Bench while remaining competitive on standard multimodal benchmarks, achieving a 28.2-point gain in partial accuracy over the strongest baseline.
[CV-21] ExtraG S: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting
链接: https://arxiv.org/abs/2607.12785
作者: Cheng-Tai Hsieh,Jiwei Shan,Han Fang,Jianshu Hu,Tao Ni,Lijun Han,Yutong Ban,Shing Shin Cheng,Hesheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Robot-assisted minimally invasive surgery (MIS) critically depends on reliable endoscopic perception for navigation and safety. However, conventional endoscopes provide only a limited field of view, leaving large portions of surrounding anatomy unobserved. Recent neural rendering approaches, such as Neural Radiance Fields and 3D Gaussian Splatting, enable novel view synthesis from endoscopic videos, but their reliance on sparse observations often leads to severe artifacts when extrapolating beyond the training this http URL this work, we propose ExtraGS, a framework for enhancing endoscopic view extrapolation via diffusion-guided 3D Gaussian Splatting. Starting from an initial reconstruction, we introduce an uncertainty-guided virtual camera sampling strategy to actively explore blind spots and maximize information gain. The rendered views from these sampled locations are refined using a diffusion model to recover plausible anatomical structures, producing pseudo observations that guide further optimization. To prevent the generated content from degrading reliable regions, we adopt a confidence-weighted fine-tuning strategy when incorporating these pseudo this http URL experiments on multiple public endoscopic datasets demonstrate that ExtraGS significantly reduces extrapolation artifacts and achieves state-of-the-art performance in endoscopic novel view synthesis.
[CV-22] MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models
链接: https://arxiv.org/abs/2607.12782
作者: Mingzhen Xu,Haonan Guo,Di Wang,Yinghua Qu,Zhiliang Zhou,Lei Zhang,Huiwen Yao,Rui Zhao,Fengxiang Wang,Gang Wan,Bo Du,Liangpei Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The code will be available at this https URL
Abstract:Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm for adapting to downstream hyperspectral image (HSI) classification tasks with limited labeled samples. However, spectral band configurations vary substantially across sensors, which makes direct model transfer difficult. Existing adaptation strategies often compress, select, or reshape the original spectra to match model-specific input requirements. These operations may discard useful spectral information and weaken local spectral continuity. To address this problem, we propose MBTI, a Multi-Branch efficient fine-tuning framework for Hyperspectral Image classification. MBTI adapts hyperspectral foundation models to downstream classification tasks while preserving full-band spectral information. First, we introduce a spectral-continuity-preserving multi-branch preprocessing strategy. The original HSI is divided into multiple continuous spectral subsets, and a band reuse mechanism is used when the remaining bands cannot form a complete branch. This avoids invalid padding and unnecessary spectral loss. Second, independent Low-Rank Adaptation (LoRA) modules are inserted into each branch. They enable different spectral intervals to learn task-specific discriminative features while keeping most pre-trained parameters frozen. Finally, a multi-branch channel attention fusion module adaptively recalibrates and integrates features from all spectral branches. Experiments on three public hyperspectral datasets show that MBTI achieves competitive and superior performance compared with representative classification methods. Under the final rank-8 configuration, only about 2.33%–2.36% of the parameters are trainable. The code will be available at this https URL.
[CV-23] HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition ECCV2026
链接: https://arxiv.org/abs/2607.12774
作者: Aleksei Bakin,Andrey V. Savchenko
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: to be submitted to ABAW-11 workshop of ECCV 2026
Abstract:This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition. For multi-task learning with simultaneous prediction of valence, arousal, facial expressions, and action units on s-Aff-Wild2 dataset, we use frozen lightweight facial extractors, MT-EmotiDDAMFN and MT-EmotiEffNet-B0, with separate heads and systematic post-processing: temporal Gaussian smoothing, per-class expression bias, AffectNet blending, per-AU threshold tuning, and weighted backbone fusion. On the official validation set, our ensemble significantly exceeds the performance of the ConvNeXt baseline. For ambivalence/hesitancy video recognition on the expanded BAH dataset, we extend the audiovisual pipeline to video-level Macro F1 by late fusion of face, HuBERT audio, and RoBERTa text classifiers, temporal aggregation, and a global-text gate. Frame-level Weighted F1 on validation set rises from 0.74 in ABAW-8 to 0.79, while the best public-test video-level Macro F1 reaches 0.73. In both tasks, competitive performance is achieved without fine-tuning heavy backbones. These results indicate that systematic prediction calibration and lightweight multimodal fusion can rival substantially heavier end-to-end approaches while offering improved efficiency and deployment flexibility.
[CV-24] EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agent ic Retrieval CVPR2026
链接: https://arxiv.org/abs/2607.12764
作者: Jiashi Lin,Changhong Jiang,Xiangru Lin,Ruifei Zhang,Xinyi Zhu,Jiyao Liu,Cheng Tang,Ye Du,Shujian Gao,Junzhi Ning,Lihao Liu,Ziyan Huang,Tianbin Li,Jin Ye,Junjun He
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages main paper, 6 figures. CVPR 2026 accepted paper
Abstract:Retrieval-augmented generation (RAG) has emerged as a critical paradigm for grounding Multimodal Large Language Models (MLLMs) in external knowledge. Recent GraphRAG methods introduce structured entity-relation graphs to improve retrieval and reasoning. However, they remain limited by treating knowledge graphs as static data structures built offline and queried in a single pass. This static paradigm misaligns with the interactive, iterative nature of knowledge-intensive reasoning, creating three bottlenecks: (i) text-centric fragmentation that impedes cross-modal reasoning, (ii) frozen structures unable to incorporate new evidence or correct errors, and (iii) rigid single-pass retrieval without adaptive refinement. To overcome these limitations, we introduce EvoGraph-R1, a self-evolving GraphRAG framework that reconceptualizes knowledge graphs as dynamic environments shaped through agent interactions. We formulate retrieval as a Markov Decision Process (MDP) where the agent observes the graph state and executes actions to query (GraphRetrieve), expand (WebSearch), refine (GraphEdit), or terminate (Answer) the reasoning. These actions reshape the hypergraph structure and generate feedback signals that guide subsequent evolution. Through this closed loop, the hypergraph evolves by integrating new evidence, correcting errors, and refining structure to support multi-hop reasoning. Experiments on multimodal VQA and text QA benchmarks demonstrate substantial improvements over existing RAG baselines in accuracy, coverage, and traceability, establishing self-evolving knowledge graphs as a fundamental paradigm across modalities.
[CV-25] VisCo: Leverag ing Large Language Models as Intrinsic Encoders for Visual Token Compression
链接: https://arxiv.org/abs/2607.12756
作者: Yupeng Zheng,Kai Zou,Bin Liu,Nenghai Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models (VLMs) process large numbers of visual tokens, resulting in substantial inference latency and memory overhead. This has motivated extensive research on visual token compression. While training-free strategies rely on heuristic metrics and suffer significant performance degradation under high compression ratios, many training-based methods introduce external compression modules that force the VLM backbone to adapt, incurring substantial retraining cost and compromising VLMs’ priors. Effective visual token compression hinges on strong information encoding, a capability already present in pretrained VLMs but underutilized by existing approaches. Motivated by this, we propose VisCo, a training-efficient self-compression framework that reuses the pretrained VLM itself as an intrinsic compressor. VisCo is a parameter-sharing autoencoder that compresses visual information using a small set of memory tokens and transfers hierarchical information from encoding to decoding. Experiments show that VisCo surpasses prior methods across all evaluated compression ratios, with larger gains under more aggressive compression, and remains stable even in the extreme single-token setting. Moreover, when combined with the original visual tokens, the learned memory tokens can even improve the base model, suggesting that VisCo captures complementary representations beyond compression.
[CV-26] RFMSR: Residual Flow Matching for Image Super-Resolution
链接: https://arxiv.org/abs/2607.12753
作者: Shuwei Huang,Tianyao Luo,Jicheng Liu,Daizong Liu,Pan Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Image super-resolution (ISR) has witnessed remarkable progress with diffusion models and flow matching. The dominant text-to-image (T2I) based approaches leverage large-scale foundation models as generative priors, achieving impressive perceptual quality but at the cost of massive model sizes and prohibitive training expenses. Recent flow-matching-based vision-only approaches have made significant strides; however, they adopt standard flow formulations that transport from a pure Gaussian prior to the data distribution, discarding the rich structural information already present in the low-quality (LQ) input. Furthermore, existing single-step acceleration techniques often forfeit the model’s multi-step inference capability. In this paper, we propose Residual Flow Matching for Image Super-Resolution (RFMSR), a vision-only framework that centers the source distribution at the LQ latent, reducing transport distance and preserving structural priors throughout the flow trajectory. We further introduce a two-phase training strategy: Phase I pretrains the velocity field via conditional flow matching, while Phase II applies end-to-end supervision to the single-step prediction while retaining the velocity loss across all timesteps, achieving high-quality single-step generation without sacrificing multi-step refinement. Extensive experiments demonstrate that RFMSR achieves comparable or even superior perceptual quality compared to state-of-the-art (SOTA) methods. The source code is available at this https URL.
[CV-27] Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
链接: https://arxiv.org/abs/2607.12752
作者: Hongbo Wang,Huaibo Huang,Jie Cao,Jin Liu,Haoyang Tong,Ran He
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:While recent advances in 3D generation have enabled impressive visual synthesis, existing methods often rely on 2D diffusion supervision without explicit mechanisms for geometric consistency, leading to spatial hallucinations such as duplicated structures and misaligned geometry. These issues become more severe in 4D generation, where maintaining consistency across viewpoints and temporal evolution introduces additional challenges, including jitter, identity flicker, and structural drift. We present \textbfHallo4D, a unified and model-agnostic framework for mitigating spatiotemporal hallucinations in 3D and 4D content generation. Hallo4D introduces a generation-detection-correction paradigm that leverages large multimodal language models (LMMs) to identify and summarize spatial and temporal inconsistencies from multi-view and multi-frame renderings. These insights guide a consensus-driven image-space consistency optimization, where an LMM-based selector evaluates candidate corrections through multi-model voting, without requiring retraining or architectural modifications. To further improve temporal consistency and optimization efficiency, Hallo4D incorporates motion-aware keyframe sampling, LMM-guided initialization, and appearance alignment. We additionally introduce exposure-aware optimization and visibility pruning to enhance robustness under challenging viewpoints. Extensive experiments demonstrate that Hallo4D consistently outperforms strong baselines across diverse 3D and 4D generation settings, providing a scalable and generalizable solution for consistency-aware content generation.
[CV-28] CRC-HGD: A Histopathological Image Dataset for Grading Colorectal Cancer
链接: https://arxiv.org/abs/2607.12750
作者: Elham Amjadi,Amin Bahreini,Sayed Mohammad Hasan Emami,Sayyed Mohammadreza Hakimian,Alireza Fahim,Hojjatollah Rahimi,Hamidreza Bolhasani
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1,926,425 new cases and 904,019 deaths reported in 2022. Accurate histologic grading plays a critical role in prognosis and treatment planning for colorectal adenocarcinoma. In recent years, artificial intelligence and its subcategories, including machine learning and deep learning, have been increasingly employed for automated cancer detection and classification. An appropriate and well-organized dataset is the essential first step to achieve this goal. This paper introduces CRC-HGD, a histopathological microscopy image dataset of 1,914 images obtained from 214 colorectal adenocarcinoma patients (Grade I: 106, Grade II: 75, Grade III: 33). The specimens are HE-stained colorectal tissue sections acquired at the Poursina Hakim Research Center of Isfahan University of Medical Sciences, Iran, diagnosed between 2014 and 2019, and graded according to the World Health Organization (WHO) criteria into three grades: well-differentiated (Grade I), moderately differentiated (Grade II), and poorly differentiated (Grade III). For each specimen, four magnification levels are provided: 4x, 10x, 20x, and 40x. The dataset is accessible via Mendeley Data (this https URL) and at this http URL, where the latest version is also available. The distinctive feature of this dataset is the provision of labeled specimens across all three differentiation grades at multiple magnification levels, enabling comprehensive computational analysis of colorectal cancer grading.
[CV-29] Weakly Supervised Spatio-Temporal Candidate Discovery of Dairy Farm Sites from Seasonal Satellite Imagery
链接: https://arxiv.org/abs/2607.12748
作者: Usman Haider,Fatima Khalid,Karl Mason
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Farm site discovery from satellite imagery is a spatiotemporal candidate ranking problem because farm evidence is distributed across pasture, field boundaries, roads, buildings, and seasonal vegetation patterns. Direct farm labels are often incomplete, which makes fully supervised detection difficult. This paper proposes a weakly supervised pipeline for ranking dairy farm candidate clusters from seasonal Sentinel imagery and open map priors. The method uses aligned spring, summer, and autumn image tiles from County Cork, Ireland, with spectral bands, vegetation indices, built area indices, and a pasture channel. A Barlow Twins encoder learns multi-season tile embeddings without farm labels. In parallel, weak OpenStreetMap farm priors are split into a prior and a held-out set. Prior features support a rule-based tile score that combines farm proximity, seasonal pasture evidence, and summer greenness, while held-out features are reserved only for proxy evaluation. The rule score is smoothed over a spatial representation graph using geographic proximity and embedding similarity, and high-scoring tiles are grouped into ranked candidate clusters. From 26,722 valid tiles, the main run selects 535 high-confidence tiles and forms 71 candidate clusters. The top 5 clusters achieve 0.60 precision within 500 m and 0.80 precision within 1000 m of held-out OpenStreetMap farm features. The top 10 clusters achieve 0.40 precision within 500 m and 0.80 precision within 1000 m. The results show that seasonal representation learning and weak geographic priors can reduce large satellite image collections into compact candidate sets for human review.
[CV-30] Color Pass-Through via Camera-Display Coupling
链接: https://arxiv.org/abs/2607.12746
作者: Ruikang Li,Molin Li,Jiarui Wu,Zhe Wei,Pengpeng Liu,Tianfan Xue
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 35 pages, 20 figures, including supplementary material. Project page: this https URL
Abstract:When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and contrast. This gap persists despite substantial advances in both modern cameras and displays. A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and display stages, and then connect them through low-dimensional color transforms, leading to information bottlenecks and inevitable error accumulation. To address this systemic challenge, we propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images. Our key insight is to treat the camera and display as a coupled system rather than calibrating them in isolation. Coupling the camera and display yields two practical advantages: (1) it brings the entire real-world scenes to the display via end-to-end optimization, and (2) it allows efficient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digital and human observers. Compared with representative baselines, our method achieves an average gain of +2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics, demonstrating improved reproduction of the perceived color of the original scene.
[CV-31] Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification
链接: https://arxiv.org/abs/2607.12704
作者: Alaa Almouradi,Erchan Aptoula
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training. Feature-statistics augmentation such as MixStyle, EFDMix, and correlated style uncertainty improves generalization at low cost but perturbs channel statistics globally, treating each image as a single style; one class can then contaminate the augmentation of another. Domain generalization is understudied for multi-label remote sensing; no prior method or multi-source benchmark targets it. A label-decoupled augmentation framework is therefore proposed, confining style perturbation to label-specific regions. Per-label attention, obtained from a learnable module or from gradient class-activation maps, yields per-label feature statistics; these statistics are mixed with cross-domain samples that share present labels, under independent per-label coefficients, and features are recomposed by attention-weighted normalization. Three operators combined with two attention sources produce six variants, evaluated on a leave-one-domain-out benchmark from multi-label UCM, AID, and DFC15 over six shared labels. Averaged over three splits and five seeds, the best variant attains 71.5% mean average precision, exceeding empirical risk minimization by 5.0 points and the strongest global-statistics baseline by 1.3 points, with the largest gain on the hardest transfer (up to 7.7 points). Ablations indicate that spatial attention and refreshed localization maps are most influential. The framework adds at most 0.35% parameters, leaves inference unchanged, and appears to offer a generic, inexpensive upgrade path for multi-label statistics-based domain generalization. Code is available upon acceptance at this https URL.
[CV-32] Lesion Segmentation in Moderate to Severe Traumatic Brain Injury: An nnU-Net Based Approach with Adaptive Normalization in the AIMS-TBI 2025 Challenge MICCAI2025
链接: https://arxiv.org/abs/2607.12684
作者: Inhwa Son,Gaeun Lee,Sohyeon Sim,Kwang-Hyun Uhm
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 2nd place, AIMS-TBI Challenge at MICCAI 2025
Abstract:The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) from T1-weighted MRI presents a significant clinical challenge due to the profound heterogeneity of lesion characteristics in terms of size, shape, and location. To address this, the AIMS-TBI 2025 Challenge was organized to promote the development of robust and accurate segmentation algorithms. In this paper, we present our deep learning-based solution. Our methodology employs the nnU-Net framework with an adaptive intensity normalization strategy confined to the brain parenchyma, effectively reducing inter-subject variability and mitigating artifacts from non-brain structures. Upon final evaluation on the held-out test set, our method demonstrated highly competitive performance on the official leaderboard, achieving an Overall Dice Coefficient of 0.6305. The model obtained a Dice score of 0.4805 for lesion segmentation and 0.9324 for non-lesion tissue. While the lesion Dice reflects the difficulty of detecting highly heterogeneous lesions, the high non-lesion Dice primarily indicates the model’s strong ability to correctly identify non-lesion voxels, demonstrating good specificity in differentiating lesion from non-lesion regions. These results demonstrate that incorporating anatomically constrained normalization within the nnU-Net pipeline is a powerful and effective strategy for tackling the complexities of msTBI lesion segmentation.
[CV-33] MambaPSA: A Mamba-based Replacement for C2PSA in YOLO26
链接: https://arxiv.org/abs/2607.12681
作者: Sheng-Wei Chan,Chia-Min Lin,Hsin-Jui Pan,Ching-Yu Tsai,Chih-Hsiang Yang,Yung-Che Wang,Jen-Shiun Chiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:State space models (SSMs), notably Mamba, have recently emerged as efficient alternatives to self-attention with linear computational complexity. We investigate the integration of Mamba into YOLO26, the latest non-maximum suppression (NMS)-free object detection framework, by proposing MambaPSA, a lightweight Mamba-based replacement for the C2PSA block at the end of the backbone. To complement this study, we additionally insert a bidirectional Vision Mamba (BiViM) module at the P3, P4, and P5 levels of the neck. Experiments on PASCAL VOC 2007+2012 show that MambaPSA reduces parameters by 2.9%, FLOPs by 12.1%, and improves CPU inference throughput by 17.6% (from 17 to 20 FPS) with negligible accuracy change (-0.1 mAP50:95), while the P4 BiViM placement yields the best accuracy gain (+0.9 mAP50:95). These results suggest that SSMs offer a favorable efficiency-accuracy trade-off when replacing attention-based blocks in NMS-free lightweight detectors.
[CV-34] ReflectVLN: Training Vision-Language Navigation Agents with Reflective Reasoning
链接: https://arxiv.org/abs/2607.12680
作者: Jiahang Wang,Yirong Yang,Yanqing Zhu,Minghua Luo,Shichao Xie,Fei Liu,Mu Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing vision-language navigation methods often couple a VLM with waypoint decoders to produce multi-step action plans, but they typically lack an explicit closed-loop mechanism for tracking semantic progress, diagnosing execution failures, and recovering from error accumulation in long-horizon navigation. To address this gap, we propose ReflectVLN, an agentic VLN framework that organizes decision-making through bidirectionally interactive intention and execution agents. The intention agent performs subtask decomposition and reflection, generating executable subtask descriptions as corrective plans. Conditioned on these descriptions, the execution agent grounds them into short-horizon actions under current observations while monitoring sub-goal progress and detecting off-track behavior. Crucially, ReflectVLN enables closed-loop bidirectional communication: the execution agent emits progress and deviation signals to trigger reflection and subtask updates on demand, and the intention agent returns structured guidance that reconditions subsequent actions for recovery. To encourage temporally coherent decisions with interpretable intermediate rationales, we introduce Action Chain-of-Thought (Action-CoT), a path-conditioned dual-query training scheme for action generation. Experiments on standard VLN benchmarks show that ReflectVLN improves success rates and path efficiency under a constrained data budget, with favorable training cost and fewer high-level intention calls at inference time, while providing interpretable intermediate decisions for analysis and collaboration. Code is available at: this https URL
[CV-35] xt-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings
链接: https://arxiv.org/abs/2607.12678
作者: Yan Gong,Bohao Li,Bowen Du,Junchen Ye
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Computer-Aided Design (CAD) floor plan drawings contain both graphical primitives and textual annotations, which provide complementary geometric and semantic cues for intelligent design understanding. Among CAD analysis tasks, panoptic symbol spotting has become increasingly important with the growing demand for industrial digitalization and deep learning-based automation. However, most existing methods remain primarily primitive-centric and underexploit textual annotations, despite their critical semantic value. Even the few text-aware approaches often treat annotations only superficially, without properly modeling complex syntax and hierarchical semantics of CAD annotations, which leads to semantic loss and suboptimal spotting performance. To address these limitations, we propose TextCAD, a multimodal framework that jointly models graphical primitives and textual annotations for panoptic symbol spotting. Specifically, we design a Type-Attribute Correlation Encoder (TACE) to explicitly encode the compositional semantics within annotations by jointly modeling their types and attributes. We further introduce a Semantic Hierarchy Alignment framework with Multi-level Semantic Filtering (MSF) and primitive downsampling, which adaptively aligns annotation semantics with graphical primitives at different semantic levels and enables accurate cross-modal semantic injection and fusion. Experiments on real-world building-design datasets show that TextCAD effectively improves symbol spotting performance and achieves state-of-the-art results.
[CV-36] MAGE: Color-Invariant and Spatial Knowledge Distillation for Gastric Neoplasm Classification MICCAI2026
链接: https://arxiv.org/abs/2607.12663
作者: Jiho Jun,Jeongwon Woo,Jaemin Song,Thanh Bong Nguyen,Dong-heon Yeon,Donghoon Kang,Jae-Myung Park,Sung-Jea Ko,Kwang-Hyun Uhm
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to MICCAI 2026
Abstract:Accurate differentiation between gastric adenoma and carcinoma during endoscopy is critical for clinical decision-making. Yet, this task is highly challenging due to high inter-class similarity and ambiguous boundaries between the two classes. Existing ROI-based classification methods often suffer from detection/segmentation error propagation and loss of surrounding global context. In contrast, full-image classification lacks the necessary spatial focus. Furthermore, we observe that deep neural networks gravitate towards domain-specific texture biases(e.g. bleeding, lighting artifacts), often causing models to predict based on spurious correlations instead of intrinsic morphological features. To address these limitations, we propose a novel framework, Masked Achromatic Guidance Expert (MAGE). During training, we introduce an auxiliary local expert branch trained on masked achromatic views of the neoplasm. By suppressing background context and color, this branch is forced to learn highly discriminative, purely structural features. We then employ a dual-objective distillation strategy, transferring both classification logits and spatial attention maps to provide implicit spatial supervision to the main branch that receives full WLI as input. This dual-objective distillation forces the model to ground its predictions in morphology rather than relying on shortcuts, while still retaining clinically relevant color cues. At inference time, our deployable model operates on images without annotated masks, ensuring real-time deployability . Extensive experiments on a clinical gastric endoscopy dataset show that our method significantly outperforms existing detection-based methodologies (e.g. YOLO) and classification-based methodologies (e.g. Swin-Transformer), providing not only superior classification performance but also interpretable attention maps for clinical reliability.
[CV-37] Instance-Enriched Semantic Maps for Visual Language Navigation
链接: https://arxiv.org/abs/2607.12630
作者: Jiho Hong,Eunae Kang,Sanghyun Kim,Young-Sik Shin
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual Language Navigation (VLN) aims to enable an embodied agent to navigate complex environments by following natural language instructions. Recent approaches build semantic spatial maps and leverage Large Language Models (LLMs) for reasoning and decision making. Despite these advances, existing systems lack instance-level object detail and robustness to diverse user queries, limiting reliable navigation in complex indoor environments. To address these limitations, we propose Instance-Enriched Semantic Maps, a unified framework with three key contributions: (1) Instance-level two-and-a-half-dimensional (2.5D) rich information mapping that constructs maps from color and depth observations via open-vocabulary panoptic segmentation, preserving vertical distinctions and capturing small objects, while storing diverse semantic attributes and natural language captions enriched with room-level context. (2) Robust query processing via LLM-based target selection, which dynamically routes queries across type-specialized experts and integrates their outputs through score-level fusion, enabling consistent goal selection across diverse query formulations. (3) Storage-efficient semantic representation that achieves approximately 96% reduction compared to three-dimensional (3D) scene-graph approaches while preserving sufficient spatial information for navigation. The proposed 2.5D representation outperforms the 3D baseline by over 27% in prediction-normalized Area Under the Curve (AUC). In navigation experiments, our method achieves over 17% improvement in object retrieval and over 23% in navigation success compared to the baseline across diverse query types. The project page is available at this https URL.
[CV-38] Decouple and Reason : Anatomically Guided Two-Stage Voxel-Level Grounding of Free-Text Findings in 3D Chest CT MICCAI2026
链接: https://arxiv.org/abs/2607.12602
作者: Kwang-Hyun Uhm,Inhwa Son,Sung-Jea Ko
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to MICCAI 2026
Abstract:Automatic voxel-level grounding of free-text findings in 3D chest Computed Tomography (CT) is critical for clinical interpretability. However, this task remains highly challenging due to the intricate spatial complexity of large 3D volumes and the heterogeneity of free-text findings. Existing end-to-end approaches often struggle to simultaneously learn the localized feature representations required for accurate 3D segmentation and the complex semantic understanding needed for text alignment, leading to suboptimal grounding performance. To overcome this fundamental limitation, we propose a novel decoupled framework that disentangles the problem into two specialized stages: (1) class-agnostic lesion segmentation and (2) text-volume reasoning. This structural separation allows the model to first extract candidate sub-volumes by localizing potential abnormalities. Subsequently, intensive cross-modal reasoning is performed to align these localized sub-volumes with free-text medical findings. To resolve the spatial ambiguities inherent in local regions, the reasoning module is augmented with explicit anatomical guidance, utilizing relative spatial coordinates and lung lobe priors. Evaluated on the ReXGroundingCT benchmark, our method achieves state-of-the-art performance in overall grounding quality on the official leaderboard. These results demonstrate that decoupling detection from reasoning is a highly effective paradigm for handling the complexity of 3D medical visual grounding. Our code is publicly available at this https URL.
[CV-39] WanToFight: Real-Time Generative Game Engine for Multi-Player Combat Interaction
链接: https://arxiv.org/abs/2607.12592
作者: Li Hu,Guangyuan Wang,Peng Zhang,Bang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:We present WanToFight, a generative game engine that simulates real-time, two-player The King of Fighters '97 (KOF~'97) gameplay from keyboard input. Prior generative game engines target either single-player first-person settings or non-real-time cooperative scenarios; multi-player control, real-time inference, complex physical interaction, and adversarial gameplay have not been jointly addressed. WanToFight closes this gap with three components built on the Wan-1.3B video diffusion transformer: a streaming autoregressive generator with block-causal attention and a rolling KV cache; a visually grounded Player Association module that binds each player’s keyboard signal to a character identity; and a gated, locally causal keyboard injection module trained with a single-player-to-full-gameplay curriculum. A four-step DMD-distilled student paired with a pruned VAE decoder sustains 30FPS at 512x384 on a single NVIDIA RTX 5090 over the duration of a complete match. To our knowledge, WanToFight is the first generative game engine to combine multi-player control, real-time inference, complex physical interaction, and adversarial gameplay in one system.
[CV-40] raceback Translators Against Forgetting in Continual Fake Speech Detection
链接: https://arxiv.org/abs/2607.12569
作者: Enrico Gottardis,Mattia Tamiazzo,Simone Milani
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM); Sound (cs.SD)
备注: Accepted at EUSIPCO 2026
Abstract:Fake speech detectors are increasingly challenged by the development of new and more accurate generative models. To cope with this problem, continual learning techniques are nowadays widely considered feasible strategies for updating models to new datasets, but they also lead to decreased performance on previously seen samples (catastrophic forgetting). In this work, we propose a forgetting-resilient solution based on the adoption of domain translators within a frozen detector, which remaps the new feature spaces into the original ones by means of a traceback translator network. Experimental results show that this strategy enables the achievement of high detection rates with respect to traditional retraining, while minimizing the computational effort and preserving the detection accuracy on previous data.
[CV-41] Gaussian Mixture Modeling for Event-Aware Visual Allocation in Long Video Understanding
链接: https://arxiv.org/abs/2607.12557
作者: Yifan Lu,Ziqi Zhang,Chunfeng Yuan,Jun Gao,Bing Li,Weiming Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted at PRCV 2026
Abstract:Large Vision-Language Models (LVLMs) face significant challenges in long video understanding due to the excessive computational cost and information loss associated with uniform sampling. Existing keyframe selection methods often treat video frames as atomic entities and allocate visual budgets equally, thereby overlooking high-level semantic structures and introducing substantial redundancy. To address these limitations, we propose GMM-EVA (Gaussian Mixture Modeling for Event-Aware Visual Allocation), which leverages Gaussian Mixture Models to model event-level structure from discrete frame-wise observations. A differentiated allocation strategy is then applied to preserve one primary high-resolution keyframe per event for high-fidelity detail, while utilizing lower-resolution secondary keyframes to maintain temporal context and optimize token budgets. GMM-EVA is a training-free, plug-and-play framework that generalizes robustly across various relevance measures and downstream LVLMs. Extensive experiments on multiple long video benchmarks demonstrate that our method significantly outperforms uniform sampling. Notably, GMM-EVA achieves comparable performance to baseline selection methods while utilizing only approximately half of the visual token budget, highlighting its superior efficiency and effectiveness.
[CV-42] CGRL: Concept-Guided Pruning and Representation Learning for Whole-Slide Image Classification
链接: https://arxiv.org/abs/2607.12556
作者: Thuc Huynh,Tuan Le,Doanh C. Bui
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 2 figures. Accepted at MAPR 2026. Code: this https URL
Abstract:Weakly supervised whole-slide image (WSI) classification is widely used in computational pathology because slide-level labels are easier to obtain than dense region annotations. Existing multiple instance learning (MIL) methods often aggregate large bags of patch embeddings using mainly visual cues, which can retain many non-informative patches and provide weak alignment between instance features and class-level disease semantics. We propose Concept-Guided Pruning and Representation Learning (CGRL), a simple framework that introduces class-level concept prototypes derived from disease prompts into the MIL pipeline. First, concept-relevance pruning ranks patch instances by their similarity to class concepts and retains the top-K concept-relevant patches for downstream MIL aggregation. Second, concept-guided contrastive representation learning constructs class-wise positive and negative patch sets from the same similarity matrix and optimizes target-class, symmetric auxiliary, and cross-class separation objectives, thereby regularizing the projected concept space. We evaluate CGRL on TCGA-BRCA and TCGA-NSCLC using multiple representative MIL methods. Experimental results show that CGRL improves several model-dataset combinations, with gains depending on the downstream MIL model and dataset. It achieves particularly clear improvements in accuracy and macro-F1 while reducing computational cost through concept-relevance pruning. These findings demonstrate that class-level semantic concepts provide an effective and practical prior for patch selection and representation learning in weakly supervised computational pathology.
[CV-43] VanillaBench: The Hidden Accuracy Cost of Adversarial Robustness
链接: https://arxiv.org/abs/2607.12545
作者: Niklas Bunzel
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Adversarial robustness research has produced hundreds of defended models over the past decade, yet the literature almost universally reports robustness results in isolation: standard (clean) accuracy and adversarial accuracy of the robust model are shown, but the gap to the corresponding vanilla model is rarely quantified. We introduce VanillaBench, a systematic benchmark that makes this gap explicit. For every adversarially-trained model catalogued by RobustBench across four threat models, we compute the accuracy difference against multiple vanilla references from Papers with Code, computed over both all entries and no-extra-data entries, the best vanilla model as of the robust model’s publication year, and an architecture-matched baseline. Across all 186 robust models, the mean delta clean relative to the best vanilla model ranges from -7.7 to -29.5 percentage points, and even the single most robust model per track still trails its temporal vanilla counterpart by 4.0-21.0 points. The architecture-matched comparison, which isolates the effect of adversarial training from architectural differences, reveals a mean gap of -3.5 to -17.5 points. Restricting this architecture-matched comparison to models whose vanilla accuracy is known for the exact same architecture, rather than approximated from a related one, narrows the gap to -4.0 to -14.0 points. These results demonstrate that the robustness-accuracy trade-off is substantially larger than what is typically conveyed by individual papers. This information is critical for practitioners and decision-makers. When deploying models in real-world settings, the accuracy cost of robustness directly affects business outcomes, yet current publications do not provide the vanilla baseline needed to assess it. We argue that future robustness evaluations should report vanilla-referenced accuracy gaps as a standard component.
[CV-44] Edge-Aware Thermal Infrared UAV Swarm Tracking
链接: https://arxiv.org/abs/2607.12544
作者: Yu-Hsi Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 7 pages, 4 figures, 3 tables
Abstract:Thermal infrared (TIR) imaging is essential for UAV swarm operations in visually degraded environments. However, tracking tiny UAVs remains challenging due to limited appearance cues, frequent occlusions, and rapid maneuvers. Despite significant progress driven by benchmarks such as the Anti-UAV challenge, existing methods primarily prioritize accuracy while overlooking the computational constraints of real-time edge deployment. The standard Kalman Filter (KF) offers the efficiency required for edge devices, yet its constant-velocity assumption often breaks down under highly dynamic UAV motion and thermal sensor jitter. More sophisticated nonlinear estimators can improve robustness but often introduce additional computational costs. To address this gap, we propose an edge-aware online tracking pipeline centered on the Adaptive Kinematic Kalman Filter (AKKF), which augments the linear KF with state-dependent kinematic modeling while preserving real-time efficiency. Combined with transient false-positive suppression and kinematics-driven predictive coasting, the presented pipeline improves trajectory continuity under challenging TIR conditions. Experiments on the Beyond Strong Baseline (BSB) benchmark provide a starting point for edge-aware UAV tracking by jointly evaluating tracking performance and computational efficiency, offering insights toward future real-time deployment.
[CV-45] DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models ECCV2026
链接: https://arxiv.org/abs/2607.12539
作者: Francesco Taioli,Daniel Coelho,Iaroslav Melekhov,Roberto Alcover-Couso,Jose Miguel Grande Saiz,Virginia Fernandez Arguedas,Artur Bekasov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Project page: this https URL
Abstract:Despite remarkable progress in text-guided image editing, generative models frequently fail to preserve visual object consistency, defined as the preservation of a subject’s key attributes throughout the editing process. We address this limitation through three contributions. First, we introduce ABO-Edit, a dataset specifically designed to study object consistency, comprising over 12,000 triplets of source images, editing prompts, and high-quality target images rendered from artist-designed 3D assets, with multi-view coverage and human-verified quality control. Second, we uncover an overlooked property of image-editing rectified flow models: the conditioning embedding space, not directly supervised during training, encodes a prediction of the final generated image even at high noise levels. Third, exploiting this finding, we propose FlowMirror, a parameter-free auxiliary loss that supervises this conditioning embedding space. Without architectural changes, our method improves generation quality across several metrics over baselines.
[CV-46] DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLM s ACM-MM2026
链接: https://arxiv.org/abs/2607.12503
作者: Rongxin Gao,Yuzhi Huang,Dongxuan Liu,Chu Li,Zhenye Wang,Jie Wu,Shuzhao Xie,Jingyan Jiang,Xinghao Ding,Xiaotong Tu,Yue Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM MM 2026
Abstract:4D spatio-temporal reasoning, jointly modeling 3D spatial structure and temporal evolution, is essential for understanding dynamic worlds and enabling embodied interaction. While current Multimodal Large Language Models (MLLMs) show strong capabilities in static scene understanding and coarse-grained 4D tasks, they still have notable limitations in continuous dynamic scene perception, especially in tracking dynamic object evidence for coherent 4D spatio-temporal reasoning. This shortcoming stems mainly from relying on sparse frame-level observations, fragmenting continuous dynamic cues and leaving models unable to disentangle genuine object dynamics from camera-induced apparent motion. Inspired by humans tracking dynamic cues while compensating for viewpoint changes, we propose DynTrace, a training-free framework for 4D spatio-temporal reasoning with two complementary components. Dynamic Trajectory Visualization (DTV) reprojects world-coordinate trajectories onto the image plane, providing geometry-informed visual priors that disentangle genuine object dynamics from camera-induced apparent motion. Meanwhile, the Dynamic Trace Token (DT-Token), organized into a Dynamic Trace Graph (DTG), tracks object-level dynamic cues, trace evolution, and key moments, maintaining continuous dynamic object evidence for coherent 4D reasoning. Together, these two components equip MLLMs with continuously tracked dynamic object evidence, grounded in geometry-informed visual priors and structured spatio-temporal traces. DynTrace consistently improves open-source MLLMs, achieving state-of-the-art results on Dyn-Bench, VLM4D, and DSI-Bench, validating the importance of tracking dynamic object evidence for robust 4D spatio-temporal reasoning.
[CV-47] Adversarial Attacks on Online Handwriting using Salience-based Temporal Editing ICDAR2026
链接: https://arxiv.org/abs/2607.12500
作者: Yataro Tamura,Brian Kenji Iwana,Jiseok Lee
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICDAR 2026
Abstract:Deep learning models for online handwriting recognition have been shown effective and are increasingly deployed in practical applications. However, their vulnerability to adversarial attacks is still a challenge. Existing adversarial methods are predominantly designed for image-based inputs and typically rely on additive spatial perturbations. When applied to online handwriting, which is inherently represented as a time series of pen trajectories, such perturbations often introduce high-frequency jitter and visibly unnatural stroke artifacts. In this work, we propose a novel adversarial attack framework for online handwriting recognition based on salience-guided temporal editing. Instead of adding noise, the proposed method generates adversarial examples by inserting and deleting points at time steps selected according to temporal salience, preserving the shape and smoothness of the original handwriting. Temporal salience is estimated using gradient-based activation mapping, which guides edits toward time steps that strongly support the original class prediction. We evaluate the proposed approach on the Unipen and CASIA-OLHWDB datasets under both white-box and one-shot black-box attack settings. Experimental results demonstrate that while conventional image-based attacks achieve strong white-box performance, they exhibit poor transferability across models. In contrast, the proposed temporal editing attack achieves stronger one-shot black-box transferability while preserving the visual structure of the handwriting. These results indicate that temporal editing is a relevant threat model for online handwriting recognition, particularly in one-shot black-box transfer settings.
[CV-48] rraLogic: A Benchmark for Hierarchical Geospatial Reasoning in Earth Observation
链接: https://arxiv.org/abs/2607.12497
作者: Yuhang Yan,Linchao Mou,Bokang Yang,Qingyu Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 3 figures, and 7 tables. Dataset and agent code are available at this https URL
Abstract:Beyond perception, reasoning is essential in remote sensing for advanced interpretation, inference, and decision-making. Recent advances in large language models (LLMs) have enabled tool-augmented agents that leverage external tools to perform complex analytical tasks. However, existing studies in remote sensing primarily focus on perception-oriented tasks, leaving cognitive geospatial reasoning largely underexplored. To address this gap, we introduce TerraLogic, a benchmark for geospatial reasoning. TerraLogic comprises 545 scenario-driven, hierarchy-aware tasks, such as hazard vulnerability assessment, urban heat island analysis, and forest fragmentation dynamics, spanning optical, Synthetic Aperture Radar (SAR), and infrared (IR) imagery. It advances evaluation beyond recognition and monitoring toward cognitive-level geospatial analysis. To facilitate evaluation on TerraLogic, we further propose HieraPlan, a tool-augmented agent that organizes toolkits into functional hierarchies and performs fault-tolerant reasoning. HieraPlan enables structured abstraction, robust recovery from tool failures, and stable long-horizon planning. Extensive experiments demonstrate that current approaches struggle with hierarchical geospatial reasoning, while HieraPlan provides a strong baseline with improved reasoning, cross-modal generalization, and error handling. The dataset and agent code are publicly available at this https URL.
[CV-49] RealSkin: Spatio-Spectral Partial Neural Adjoint Maps for Image-to-3D Attribute Transfer
链接: https://arxiv.org/abs/2607.12495
作者: Jing Li,Yawei Luo,Xiangze Meng,Ying Li,Tieru Wu,Rui Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Creating photorealistic 3D assets requires bridging the appearance gap between real-world observations and synthetic models. A promising approach is to transfer visual attributes from real images onto synthetic 3D surfaces. Traditional methods struggle with resolution mismatch and the inherent discreteness of point correspondences. In contrast, resolution-robust functional maps enable smooth attribute propagation but rely on near-isometry assumptions and topological consistency. To address these limitations, we propose RealSkin, a self-supervised framework that performs correspondence optimization in a learned spectral domain, guided by spatial correspondences. We first introduce a spatial-guided registration algorithm to establish coarse correspondences under severe topological discrepancies. To relax strict isometric assumptions and handle partial correspondences, we further design a spectral-aware neural adjoint network that incorporates partial correspondences into a neural function space and models non-isometric residuals for correspondence refinement. Experimental results demonstrate that our method achieves state-of-the-art performance on challenging real-to-synthetic scenarios. The code will be publicly released.
[CV-50] Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence
链接: https://arxiv.org/abs/2607.12477
作者: Zhishan Zou,Guoyan Sun,Zhiwei Wei,Jiancheng Pan,Yujie Li,Mugen Peng,Wenjia Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Website: this https URL ; Code: this https URL
Abstract:Autonomous UAV systems increasingly rely on multimodal large language models (MLLMs) to operate in complex real-world environments. Such embodied scenarios require not only understanding the surrounding space but also maintaining a coherent representation of the agent itself. However, existing UAV-oriented approaches and benchmarks remain largely environment-centric, primarily focusing on spatial understanding tasks, with the agent’s self-awareness remaining implicit. To address this gap, we introduce SIS-Bench, a benchmark for evaluating embodied spatial intelligence in UAV scenarios under a unified self-in-space formulation. SIS-Bench organizes evaluation along two complementary dimensions, space and self, and a three-level hierarchy of perception, memory, and reasoning. It contains 4,856 question–answer pairs across 13 tasks derived from 1,646 real-world UAV videos through a task-conditioned construction pipeline with expert this http URL evaluations reveal that current MLLMs exhibit fundamental limitations in modeling dynamic and agent-centered processes. In particular, we observe a clear imbalance between spatial cognition and self-awareness, as well as a progressive performance degradation across cognitive this http URL by these findings, we further explore a motion-aware representation that incorporates self-related dynamics through optical flow and visual feature fusion. Experimental results show that modeling agent motion consistently improves perception and memory performance, not only in spatial cognition but also in self-awareness, and generalizes to downstream UAV decision-making this http URL results highlight the importance of self-awareness for advancing embodied spatial intelligence, and provide both a new benchmark and empirical evidence for motion-aware self-in-space modeling.
[CV-51] Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification
链接: https://arxiv.org/abs/2607.12464
作者: Jeeyung Kim,Erfan Esmaeili,Qiang Qiu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approaches largely improve synthetic data by increasing realism, diversity, or domain adaptation, while overlooking a more fundamental question: how should sample usefulness for classification be measured and optimized? We address this with Class-Contrastive Influence (C2I), a criterion that quantifies a sample’s usefulness through its gradient-based influence on the classifier. We find that effective samples exhibit a strong C2I gap: their loss gradients align with validation gradients from the same class and oppose those from other classes. Our analysis further suggests that such high-C2I samples are hard, boundary-proximal examples that help refine the decision boundary and improve robustness. Building on this insight, we fine-tune diffusion models with reinforcement learning using a C2I-based reward to steer generation toward class-informative samples. Across several few-shot medical imaging benchmarks, C2I-guided generation improves downstream accuracy and robustness over diffusion-based augmentation baselines, showing that synthetic augmentation is most effective when guided by task usefulness rather than image quality alone.
[CV-52] Let RGB Be the Language of Vision
链接: https://arxiv.org/abs/2607.12450
作者: Timing Yang,Jinrui Yang,Xinlong Li,Yuhan Wang,Haoran Li,Yanqing Liu,Guoyizhe Wei,Jixuan Ying,Chen Wei,Rama Chellappa,Yuyin Zhou,Cihang Xie,Alan Yuille,Feng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This work introduces a unified formulation for vision models, where diverse forms of visual information beyond natural images, such as masks, depth maps, and other structured visual signals, are all represented as RGB images, while general visual tasks can be converted into a common RGB-to-RGB image editing problem. In this paradigm, different types of visual information internally share the same encoding and decoding architecture and parameters as natural images, enabling a single model to transfer across tasks through a unified visual interface, in a way analogous to how language models operate over text. We refer to this formulation as RGB In and RGB Out (RINO). Built upon a generic image editing backbone without task-specific fine-tuning, RINO demonstrates robust and competitive zero-shot performance on both dense understanding tasks such as segmentation and depth estimation (where we unify outputs as RGB), and dense-conditioned generation tasks such as pose-to-image generation (where we unify inputs as RGB). We hope this study provides useful insights toward general unified vision-language systems, where diverse visual tasks can be expressed, interpreted, and solved through a shared visual language. Code is available at this https URL.
[CV-53] ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning
链接: https://arxiv.org/abs/2607.12433
作者: Zijie Wang,Wei Zhang,Weiming Zhang,Xiao Tan,Weikai Chen,Xiaoxu Li,Guanbin Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Under review
Abstract:Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE). However, they implicitly assume that depth can be recovered as a globally smooth field through iterative denoising, which does not explicitly reflect the piecewise and scale-dependent organization of scene geometry. In practice, geometric structure emerges progressively across spatial scales, where coarse layout, surfaces, and boundaries are constructed in a hierarchical manner. Motivated by this observation, we introduce ARDepth, which formulates depth estimation as structured auto-regressive generation. Instead of recovering depth through global refinement, ARDepth progressively constructs depth representations as spatial resolution increases. To support this generative process, we introduce Scale-Progressive Conditioning (SPC) to inject multi-scale visual features at each generation stage, and Semantic-Aware Guidance (SAG) to provide scene-level semantic priors that enhance global structural consistency. Together, these designs enable the model to capture fine-grained local details while maintaining coherent global geometry. Empirical results demonstrate that our approach achieves strong performance and produces structurally consistent depth predictions across scales, validating auto-regressive generation as a promising alternative paradigm for geometric modeling.
[CV-54] More Than Where You Are: Learning Semantics Structure and Geometry from Cross-View Localization
链接: https://arxiv.org/abs/2607.12429
作者: Mao Chen,Xiangkai Zhang,Zhiyong Liu,Chuankai Liu,Xu Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Consistent cross-view understanding under extreme viewpoint changes is essential for spatial intelligence, as it enables models to recognize the same scene across extreme viewpoint gaps. Cross-view localization naturally provides a promising pathway toward this ability, as it requires a model to align ground-view imagery with geo-referenced satellite-view imagery despite drastic appearance changes to estimate camera poses. Recent visual foundation models have made this long-standing localization problem increasingly feasible by providing rich 2D representations for cross-view matching. However, we argue that cross-view localization should not be viewed merely as 2D matching or pose estimation. In this work, we revisit cross-view localization as more than pose estimation and investigate how it can help the model develop consistent cross-view understanding under extreme viewpoint changes, including stable semantics, reliable structure, and transferable geometry. We identify three key limitations of existing methods that prevent them from achieving this. They usually lack explicit 3D grounding, rely on strict point-wise matching that can weaken semantic consistency, and learn from an absolute objective that provides limited guidance for geometric reasoning. To address these limitations, we propose CROSS, a unified cross-view localization framework built upon 3D-grounded alignment, structure-aware matching, and hypothesis ranking. This formulation makes structure learning an intrinsic requirement, encourages semantic representations to remain stable, and enables the model to acquire transferable geometry. Extensive experiments on the KITTI and VIGOR datasets show that CROSS achieves state-of-the-art performance in cross-view localization. More importantly, CROSS effectively learns stable semantics, reliable structure, and transferable geometry across extremely different viewpoints.
[CV-55] DeGuNet: Depth-Guided Ultra-Compact Backbones for Efficient LiDAR-Camera 3D Detection ECCV2026
链接: https://arxiv.org/abs/2607.12419
作者: Haifa Zhang,Yijing Wang,Peixi Peng,Zhiqiang Zuo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:In autonomous driving perception, the fusion of LiDAR and camera modalities has become the dominant paradigm for 3D object detection. However, current multi-modal frameworks heavily rely on massive visual backbones pretrained on 2D semantic tasks. This reliance introduces substantial parameter redundancy and a structural misalignment, as 2D priors are ill-equipped to handle the extreme sparsity of LiDAR projections required for Bird’s-Eye-View geometry. To address this, we present DeGuNet, an ultra-compact and plug-and-play image backbone explicitly designed for depth-guided representation learning. By incorporating sparsity-aware feature extraction mechanisms, DeGuNet effectively aligns multi-view images with unstructured LiDAR depth while strictly preventing invalid-region contamination. Extensive experiments on the nuScenes dataset demonstrate DeGuNet’s broad plug-and-play applicability and superior efficiency. When integrated into established baselines, it fundamentally eliminates architectural redundancy, reducing GPU memory consumption by up to 66.5% and achieving a 1.16x inference speedup. Concurrently, DeGuNet delivers up to a 6.20 absolute mAP gain, establishing a new paradigm for parameter-efficient multi-modal 3D perception.
[CV-56] MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning
链接: https://arxiv.org/abs/2607.12418
作者: Yumiao Zhao,Bo Jiang,Min Lu,Xiao Wang,Jin Tang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification, we observe that VLMs exhibit a notable ability to filter candidate categories and thus achieve high Top-K accuracy. However, they often struggle with fine-grained discrimination among visually similar categories, resulting in unsatisfactory Top-1 performance, as shown in Figure 1. Existing studies on VLM adapters generally focus on global alignment between visual and textual representations in the feature space, but fail to exploit semantically similar categories to refine fine-grained visual representations. Based on these observations, we propose a novel coarse-to-fine VLM fine-tuning approach for few-shot learning that leverages quantum computation, termed the Multi-Modal Quantum Adapter (MQAdapter). Specifically, MQAdapter first retrieves the Top-K category candidates most similar to the input image and uses them as semantic anchors. It then employs a cross-modal quantum learning mechanism to refine visual features under the guidance of these anchors. The core of this mechanism is the encoding of visual and textual features into quantum states. By leveraging quantum entanglement and superposition in a high-dimensional Hilbert space, MQAdapter effectively models higher-order cross-modal interactions, producing more discriminative representations than traditional Euclidean adapters. MQAdapter is parameter-efficient and can be integrated with various existing fine-tuning algorithms to achieve further performance gains. Evaluations on 15 datasets demonstrate the effectiveness of MQAdapter while requiring fewer trainable parameters.
[CV-57] Virtual Chromoendscopy with Tunable Visibility Enhancement WWW
链接: https://arxiv.org/abs/2607.12416
作者: Yuhi Kanno,Yusuke Monno,Sho Suzuki,Tomohiro Tada,Masatoshi Okutomi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 8 figures. Accepted at EMBC 2026. Project page: this http URL
Abstract:Chromoendoscopy (CE) is a common clinical practice that sprays indigo carmine blue dye onto the gastric surface to improve the visibility of gastric lesions, such as an early cancer. While CE is effective in detecting the lesions, preparing and spraying the dye needs additional cost and time, which is undesirable both for patients and medical practitioners. To overcome this issue, virtual chromoendoscopy (V-CE) was recently proposed, which applies a learned image translation model to virtually generate a CE image from a standard endoscopy (SE) image. In this paper, we propose virtual enhanced chromoendoscopy (V-ECE) that combines V-CE with image enhancement techniques to further improve the visibility of gastric lesions. Because a desired enhancement level depends on the inspected lesion and the practitioner’s preference, we introduce a novel image translation model that can generate V-ECE images using an enhancement level tunable by a user. Experimental results demonstrate that our proposed model can plausibly generate V-ECE images with various enhancement levels using a unified model.
[CV-58] Contrastive-Augmented Flow Matching for Style-Content Disentanglement
链接: https://arxiv.org/abs/2607.12404
作者: Yusong Li,Pingchuan Ma,Ming Gui,Vincent Tao Hu,Björn Ommer
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: under review, code available at: this https URL
Abstract:Learning representations that separate content and style is crucial for controllable generation and compositional generalization. However, diffusion and flow-based models trained primarily with generative objectives often produce entangled or misaligned factors. To address this gap, we introduce Contrastive Augmented Flow Matching (CAtFM), a framework that integrates contrastive regularization into an invertible flow matching formulation to promote structured content-style representations. Rather than constraining intermediate latents or velocity fields, we apply contrastive supervision to predicted endpoints during training, enforcing semantic consistency across transported distributions while allowing disentanglement to emerge implicitly, without assuming strictly pure or fully factorized content and style representations. Our main experiments operate in the CLIP embedding space, with additional validation using frozen DINO and ALIGN encoders. Across synthetic data, in-domain styles, and real-world benchmarks (ImageNet, WikiArt, DomainNet, and DTD), CAtFM improves content and style retrieval, enhances embedding cluster separation, and achieves stronger open-set robustness compared to generative and discriminative baselines. Overall, CAtFM provides a simple way to couple discriminative constraints with deterministic transport, improving disentanglement and robustness under distribution shift.
[CV-59] Physically Aware Radiomics Without Interpolation: Disentangling Voxel Geometry and Signal Modification in CT and MRI
链接: https://arxiv.org/abs/2607.12399
作者: David Corral Fontecha,Juan Miranda Bautista,Pablo Menendez Fernández-Miranda,Sergio Rubio-Martín,Lara Lloret Iglesias,Jose A. Vega
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Manuscript under peer review
Abstract:Objective: Radiomic texture features are usually computed in voxel-index neighborhoods, implicitly assuming isotropic spatial relationships. In anisotropic images, this can confound voxel geometry with interpolation-induced signal changes. We developed a voxel-spacing-aware radiomic framework that incorporates physical geometry into texture computation without resampling. Approach: We modified PyRadiomics to account for voxel spacing while preserving the native image signal. Four configurations were compared: native non-resampled extraction (NR), isotropic resampling (RS), voxel-spacing-aware extraction (VS), and fake-isotropic preprocessing (FK), in which spacing metadata were overwritten without altering the image array. Experiments included 685 LIDC-IDRI pulmonary nodules and 209 I-SPY2 breast MRI cases, with 196 radiomic descriptors. Robustness was assessed using ICC, within-subject variability, Friedman testing, feature selection, machine learning, a multilayer perceptron, and external validation. Main results: VS showed near-native agreement with NR: median ICC(A,1) was 0.9976 in CT and 0.9984 in MRI. RS produced lower agreement and larger deviations, while FK showed intermediate behavior, confirming that spacing metadata alone can affect radiomic features. Gradient-derived and neighborhood-sensitive descriptors were most affected by preprocessing. VS preserved predictive performance comparable to NR in external CT validation, whereas MRI showed greater variability across preprocessing strategies and classifiers. Significance: Voxel-spacing-aware extraction separates geometric modeling from interpolation-induced signal modification while preserving the native image signal, offering a coherent alternative to isotropic resampling for radiomic analysis of anisotropic CT and MRI. Comments: Manuscript under peer review Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.12399 [cs.CV] (or arXiv:2607.12399v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.12399 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: David Corral Fontecha [view email] [v1] Tue, 14 Jul 2026 06:19:05 UTC (6,623 KB)
[CV-60] Seeing Globally Refining Locally: Global Visual Guidance and Local Ultrasound Cues for Robust Freehand 3-D Ultrasound Reconstruction
链接: https://arxiv.org/abs/2607.12398
作者: Yameng Zhang,Zhongyu Chen,Dianye Huang,Xiangyu Chu,K. W. Samuel Au,Zhongliang Jiang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Freehand 3-D ultrasound (US) imaging has attracted increasing attention owing to its intuitive volumetric visualization, ease of use, and low cost. However, accurate 3-D reconstruction critically depends on stable probe pose estimation, yet existing trackerless methods remain susceptible to accumulated pose errors, particularly over long scanning trajectories. To address this limitation, we propose a global-to-local pose estimation framework that exploits external camera observations for globally stable localization and B-mode US images for anatomy-aware local refinement. Specifically, the framework comprises a dual-camera branch that performs contextual feature aggregation across camera views and temporal observations to estimate a globally consistent probe trajectory, and a B-mode branch that performs anatomical feature aggregation from sequential US images to capture tissue-dependent local motion cues. A cross-modal fusion module subsequently integrates the contextual camera features and anatomical US features to predict pose residuals and refine the camera-derived estimates in the transformation space. Furthermore, a multi-scale pose loss constrains relative motion over multiple temporal horizons to suppress accumulated drift during extended scans. The proposed framework is validated on phantom and in vivo datasets. On two in-house datasets (FUSION-J and FUSION-L) collected using different machines, the proposed US + Dual-Cam model reduces average trajectory drift to 1.67 mm and 1.29 mm, representing improvement of 16.50% and 27.12%, respectively, over a strong dual-camera baseline, while substantially outperforming US-only pose estimation (13 mm drift). In in vivo forearm arteries reconstruction, it achieves Hausdorff distances of 1.58 mm, demonstrating the effectiveness of the proposed method on real clinical scenarios.
[CV-61] SeamGen: Artist-Aligned UV Seam Generation via Graph Flow Matching
链接: https://arxiv.org/abs/2607.12379
作者: Hao Xu,Yuqing Zhang,Yiqian Wu,Xueqi Ma,Ding Liang,Yan-Pei Cao,Ying-Tian Liu,Xiaogang Jin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:UV seam placement is a critical yet labor-intensive step in 3D content creation, requiring artists to balance chart shape, seam concealment, and alignment with semantic and geometric features. Existing automatic methods are primarily based on per-object optimization, relying on handcrafted objectives to avoid distortion or on proxies from pretrained models to inject semantic information. However, these strategies are not always well aligned with seams used in industrial production pipelines, often resulting in layouts that deviate from artist-preferred seam patterns and practical production requirements. To address these limitations, we propose SeamGen, a generative model for UV seam generation that aligns with artist preferences and production requirements. Instead of depending on manually designed objectives and constraints, SeamGen learns the distribution of per-edge seam labels from a large corpus of existing seam layouts using a flow-matching generative model. A key challenge is that typical Transformer architectures used in flow matching models are designed for sequential representations, such as point clouds, and cannot naturally account for mesh topology. To enable mesh-native learning, we design a Mesh Transformer backbone that interleaves local graph attention over mesh edges with global self-attention across vertices, capturing both fine-grained geometric cues and long-range topological coherence. To further improve inference-time controllability and quality, we exploit the training-free inpainting capability of flow models for both localized seam refinement and constraint-guided seam generation. Extensive experiments show that by learning priors from professional seam layout data, SeamGen produces UV layouts that better align with artist-authored preferences and achieve superior perceptual quality compared with distortion-based and semantic-proxy baselines.
[CV-62] Demonstration of the common dual-channel feature decoupling characteristic of front-door mediation causal inference methods in whole-slice image classification
链接: https://arxiv.org/abs/2607.12376
作者: Zhirui Zhang,Tianhang Nan,Yong Ding,Zhuolun Song,Dayu Hu,Xiaoyu Cui
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: The manuscript is being submitted for publication to a journal
Abstract:Causal inference using front door intervention and multi-instance learning (MIL) has advanced the analysis of Whole Slide Images (WSI) in digital pathology. These methods adjust feature distributions of subtle evidence sub-images to correctly associate them with WSI-level diagnoses. We propose and prove 2 hypotheses for evaluating such methods: 1) Causal inference MIL introduces an independent classification channel that effectively completes WSI classification; 2) Greater difference between features extracted by the new and baseline channels increases effectiveness in eliminating false correlations. This hypothesis describes the core of causal inference MILs: overlaying parallel, independent channels to eliminate false associations between WSI-level diagnostic and non-diagnostic evidence sub-images by increasing deep feature diversity. Based on these hypotheses, we evaluated several causal inference MILs on breast cancer and non-small cell lung cancer datasets. This hypothesis provides a new theoretical perspective for applying causal inference to WSI analysis.
[CV-63] IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment ECCV2026
链接: https://arxiv.org/abs/2607.12375
作者: Jinjian Wu,Jiaqi Tang,Wei Wei,Yingying Yan,Jianmin Chen,Botong Geng,Lei Zhang,Qifeng Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注: Accepted by ECCV 2026
Abstract:Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability. Recent approaches based on multimodal large language models (MLLMs) introduce textual reasoning for quality prediction, yet their judgments rely heavily on semantically biased internal representations, making them insensitive to low-level perceptual degradations. We propose IQA-T1, a tool-based visual evidence reasoning framework that augments MLLM reasoning with explicit perceptual observations. During inference, the model autonomously invokes specialized analysis tools to generate structured visual evidence, such as noise residual maps, gradient statistics, and frequency spectra, which are progressively integrated into the reasoning process. To support this paradigm, we construct Q-Tool, a dataset containing 11k multimodal reasoning chains grounded in tool-generated evidence. Extensive experiments on seven IQA benchmarks show that IQA-T1 achieves the best overall performance across datasets while producing interpretable and evidence-grounded quality assessments. Code and dataset are available at this https URL.
[CV-64] UMSS: Towards Unsupervised Multi-modal Semantic Segmentation
链接: https://arxiv.org/abs/2607.12372
作者: Haitian Zhang,Thai Duy Nguyen,Xiangyuan Wang,Mohan Liu,Lin Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal semantic segmentation (MSS) is essential for robust perception in complex environments, yet its potential remains largely untapped because of the prohibitive cost of human annotations. While unsupervised semantic segmentation (USS) has achieved strong results on a single RGB modality, its naive extension to multimodal data is often hindered by fusion degradation. This occurs because, without explicit supervision, existing frameworks struggle to reconcile the heterogeneous structural patterns captured by different sensors and therefore fail to effectively exploit their complementary information. In this paper, we make the first attempt to address the novel problem of Unsupervised Multimodal Semantic Segmentation (UMSS), aiming to effectively exploit complementary sensor information in a fully label free setting. To this end, we propose UniM2 (Unified Multimodal), a novel framework built on DINOv3 that transforms conventional fusion methods into consistent performance gains. Our key idea is to learn a unified latent space driven by Cross Modal Correspondence Synergy (CMCS) to extract intrinsic shared semantic cues, bypassing the need for label guided adaptive fusion. To mitigate inherent intermodal conflicts, we introduce a Cross Modal Harmonizer (CMH) that designates RGB as a stable reference, effectively suppressing inconsistent relational supervision while guiding the model to exploit complementary structural features. Extensive experimental results on NYU Depth v2 and MFNet show that UniM2 improves mIoU by 6.4% and 9.8%, respectively, demonstrating clear advantages over existing frameworks for UMSS.
[CV-65] Lost in Visual Translation: A VLM-Assisted Perceptual-Semantic Coherence Framework for EEG-to-Image Reconstruction
链接: https://arxiv.org/abs/2607.12364
作者: Sukriti Tiwari,BHVSP Subrahmanyam,Nidhi Goyal,Sai Amrit Patnaik
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 27 pages, 3 figures, 13 tables. Accepted at the 5th International Workshop on Human Brain and Artificial Intelligence (HBAI 2026)
Abstract:EEG-to-image evaluation should distinguish visual fidelity from recoverable meaning. Yet EEG-derived reconstructions are blurry, distorted, and low-detail, causing SSIM, LPIPS, and CLIP to penalize semantically recoverable outputs or reward plausible but incorrect ones. We analyze 6,855 ground-truth/reconstruction pairs from ATM, ENIGMA, BrainVis, and DreamDiffusion using semantic probes, caption harshness and blind-spot rates, and controlled degradations. Pixel metrics show near-zero correlation with semantic consistency, while representation metrics conflate perceptual and semantic errors. We therefore introduce a BCI-aware framework in which four VLMs assess image pairs through structured questions, producing Tolerant Perceptual Alignment Scores (T-PAS) and Tolerant Semantic Alignment Scores (T-SAS). Their consensus is distilled into the BCI-Coherence Score (BCS), a compact evaluator achieving a T-PAS MAE of 0.079 (r = 0.700) and a T-SAS MAE of 0.082 (r = 0.850) on our data. Human validation shows highly reliable joint coherence judgments, with Cohen’s kappa = 0.882 +/- 0.174 and Krippendorff’s alpha = 0.882, supporting perceptual-semantic recoverability over generic visual similarity. Code and resources are available at this https URL.
[CV-66] Implicit 4D Gaussian Splatting for Fast Motion with Large Inter-Frame Displacements ICLR2026
链接: https://arxiv.org/abs/2607.12362
作者: Seung-gyeom Kim,Areum Kim,Yongjae Yoo,Sukmin Yun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICLR 2026. Project page at this https URL
Abstract:Recent 4D Gaussian Splatting (4DGS) methods often fail under fast motion with large inter-frame displacements, where Gaussian attributes are poorly learned during training, and fast-moving objects are often lost from the reconstruction. In this work, we introduce Spatiotemporal Position Implicit Network for 4DGS, coined SPIN-4DGS, which learns Gaussian attributes from explicitly collected spatiotemporal positions rather than modeling temporal displacements, thereby enabling more faithful splatting under fast motions with large inter-frame displacements. To avoid the heavy memory overhead of explicitly optimizing attributes across all spatiotemporal positions, we instead predict them with a lightweight feed-forward network trained under a rasterization-based reconstruction loss. Consequently, SPIN-4DGS learns shared representations across Gaussians, effectively capturing spatiotemporal consistency and enabling stable high-quality Gaussian splatting even under challenging motions. Across extensive experiments, SPIN-4DGS consistently achieves higher fidelity under large displacements, with clear improvements in PSNR and SSIM on challenging sports scenes from the CMU Panoptic dataset. For example, SPIN-4DGS notably outperforms the strongest baseline, D3DGS, by achieving +1.83 higher PSNR on the Basketball scene.
[CV-67] ACID: Adaptive Caching for vIDeo generation
链接: https://arxiv.org/abs/2607.12358
作者: Om Agrawal,Saurabh Agarwal,Aditya Akella
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 12 figures
Abstract:Video diffusion models produce high-quality generations but remain slow at inference due to their sequential denoising procedure. Caching-based acceleration methods address this by reusing intermediate model outputs: leading dynamic approaches such as TeaCache, EasyCache, and DiCache accumulate a drift signal and skip expensive model evaluations when accumulated drift stays below a fixed threshold \tau. This threshold controls an apparent tradeoff - raising it yields faster generation at the cost of visual quality, while lowering it preserves quality but sacrifices speed. We show this tradeoff is not fundamental; it is an artifact of holding \tau constant throughout denoising. We identify the existence of critical steps - timesteps where the drift signal changes rapidly - and show that applying a low threshold selectively at these steps while caching aggressively elsewhere recovers most of the quality of conservative caching at substantially higher inference speeds. Building on this insight, we propose ACID, a lightweight, training-free wrapper that monitors the rate of change of each method’s existing drift signal to dynamically switch between a low and a high threshold. ACID is signal-agnostic and modular: it requires no retraining and plugs directly into existing dynamic caching methods without modifying their core mechanisms. Evaluated across three caching methods (TeaCache, EasyCache, DiCache) and three open-source video diffusion models (HunyuanVideo, Wan 2.1, CogVideoX), ACID consistently expands the Pareto frontier of visual quality versus inference speed beyond what any fixed threshold achieves. In particular, on TeaCache and HunyuanVideo, ACID achieves up to 2.16x speedup over the no-caching baseline, and up to 38% additional speedup over the conservative fixed-threshold baseline with negligible (0.3 dB PSNR, 0.01 SSIM, 0.01 LPIPS) quality degradation.
[CV-68] Filtering-out poor-quality images for data preparation
链接: https://arxiv.org/abs/2607.12352
作者: Roopdeep Kaur,Gour Karmakar,Muhammad Imran
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages
Abstract:Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the literature, including the use of median, Gaussian, and bilateral filters. Convolutional neural networks (CNNs) have gained popularity in image denoising owing to their ability to extract complex patterns and features from data. CNNs are highly adaptable, making them effective tools for various image-denoising tasks. One drawback of CNN-based techniques is that they require an appropriate training dataset and all images to be resized. Another notable drawback of all these filtering techniques is that they work for certain types of environmental and camera noises. To bridge this research gap, in this paper, for the first time, instead of denoising, we propose an approach that filters out poor-quality images for various environmental and camera impacts. In our approach, quality is assessed using an image quality assessment metric and an optimum threshold is used to filter out poor-quality images. We also ensure that a sufficient number of images remain to develop the deep learning (DL) model. The results produced using real and simulated traffic and object recognition data demonstrate the performance supremacy of the proposed approach compared with the state-of-the-art approaches. The average recognition accuracy for our proposed approach is 93.8% for the traffic sign recognition dataset and 84.9% for the object recognition dataset. This indicates our model’s potential for real-life applications such as autonomous vehicles.
[CV-69] ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations
链接: https://arxiv.org/abs/2607.12335
作者: George V. Jose,Thao Liang Chiam,Toby Hughes,Dilan Patel,Alan Brook,Lyle J. Palmer,Nikhil Cherian Kurian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 2 Figures, 2 Tables
Abstract:Prototype-based networks provide inherently interpretable classification by linking predictions to learned exemplars, but their use in 3D point clouds and clinical surface-pair reasoning remains limited. We introduce ProtoPointNet, a prototype-based model for dental occlusion classification from registered upper–lower intraoral arch pairs. Each point is encoded by a 14-dimensional descriptor combining local surface geometry, curvature, and explicit inter-arch displacement and clearance, exposing occlusal relationships to prototype matching. A shared multi-task point-cloud backbone learns axis-specific prototype heads for sagittal-left, sagittal-right, vertical, transverse, and midline classification. To support limited clinical data, we train prototypes from scratch using auxiliary supervision and encoder-freeze hand-off. On Bits2Bites, ProtoPointNet achieves mean test macro-F1 of 0.724 and AUROC of 0.825, with strongest performance on vertical (F1 0.828) and sagittal-left classification (F1 0.807). Projected prototype activations localise to anatomically plausible regions, including posterior molars and premolars for cross-bite evidence and anterior incisors for bite-depth evidence. These results support prototype-based reasoning as a transparent, spatially grounded alternative to black-box 3D classifiers for dental surface-pair analysis.
[CV-70] DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery
链接: https://arxiv.org/abs/2607.12319
作者: Xinyue Xu,Zheng Zhang,Kunyang Ma,Ge Zhu,Lianshuai Cao,Lei Wang,Zixuan Li,Yi Cheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:As vision-language models (VLMs) are increasingly deployed in geospatial question answering and visual scene understanding, improving their spatial cognition capability on street view imagery for complex logical reasoning has emerged as a key research priority. However, existing VLMs frequently suffer from “spatial semantic hallucinations” when perceiving object locations, distances, and directions in real-world street view scenes. Furthermore, such errors are often recalcitrant to tracing and calibration, posing a critical bottleneck for their practical deployment in geospatial tasks. To address this pressing challenge, this study proposes DM-KG (Direction-Metric Knowledge Graph), a structurally grounded spatial representation framework for street view imagery. By explicitly extracting directional and metric relationships between entities from a single 2D image, this framework enhances the spatial reasoning accuracy of VLMs through a structured knowledge graph. Specifically, we integrate panoptic segmentation with metric depth estimation to robustly compute entity-level 3D spatial coordinates. Subsequently, we encode the clock azimuths and Euclidean distances of entity pairs into a JSON-formatted knowledge graph, which is injected into the VLM as an explicit geometric prior to guide spatial reasoning. Experimental results on public spatial question-answering (QA) benchmarks demonstrate that DM-KG reduces the mean absolute error (MAE) in distance estimation by 31.1% and the mean angular error in direction judgment by 65.8%, while simultaneously maintaining a high QA success rate. By establishing a complete, augmented reasoning pipeline, this research significantly improves the spatial cognitive capabilities of VLMs in street view scenarios, thereby providing a flexible, generalized, and interpretable framework for geographic visual question answering (GeoVQA) in open environments.
[CV-71] What Does a Temporal Benchmark Score Measure? Decomposing Channel Use in Video VLM Evaluation
链接: https://arxiv.org/abs/2607.12304
作者: Farrukh Rahman
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 9 pages, 11 pages supplemental
Abstract:A score on a temporal video question answering benchmark is meant to measure that a model has temporal understanding, but it conflates two questions. 1. The task question: is the question even temporal, does it need several frames and their order? and 2. The channel question, when it does, does the model recover the order from the pixels, or read it off the positional encoding (RoPE)? Most of a temporal score answers neither, a single frame and answer priors often carry it. The field’s validity checks, frame-shuffle sensitivity and the accuracy gained from the full video, speak only to the task question. We contribute a label-free screen for the channel question, the reversal-drop: the accuracy lost when the visual sequence is reversed while RoPE remains forward. It can be applied to compatible temporal benchmarks without new annotations. Paired reverse labels, or tasks whose labels transform deterministically under reversal, distinguish models that follow reversed content from those merely disrupted by the conflict. Molmo2 answers the forward event reading order off positions, while Qwen3-VL answers the reversed event it actually sees, reading visual order (comparatively). We call them position-dominant and visual-sequence-dominant. The split holds across two benchmarks and several temporal tasks at two scales, and activation patching shows it is a real internal property, not an artifact of the conflict. The distinction matters, the two channels fail on opposite inputs so two models with similar score are not interchangable, i.e. an aggregate score does not reflect potential failure modes.
[CV-72] MobileSAM2: Lightweight Segment Anything for Spatial Intelligence ECCV2026
链接: https://arxiv.org/abs/2607.12297
作者: Kai Jiang,Jiaxing Huang,Jingyi Zhang,Weiying Xie,Yunsong Li,Yufei Wang,Aoran Xiao,Dacheng Tao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:The recent large video foundation model, SAM2, enables segment anything in both images and videos, serving as a powerful base model for various applications. However, many of such use cases require to operate on resource-constrained devices like mobile phones and laptops. In this work, we aim to make SAM2 more mobile-friendly by distilling the heavyweight SAM2 into a lightweight model, facilitating segment anything in both images and videos on mobile devices. To this end, we propose Hypergraphical Knowledge Distill (HyperKD), which introduces the idea of hypergraph into knowledge distillation, aiming to effectively model and transfer SAM2’s generalizable and comprehensive knowledge. HyperKD consists of Temporal HyperKD and Granularity HyperKD that construct hypergraphs to explicitly model and extract the generalizable temporal knowledge and the comprehensive multi-granularity knowledge from SAM2 respectively, which are then distilled into the lightweight student model by aligning it with the constructed hypergraphs. Besides, we present MobileSAM2, a new family of lightweight SAM2 that balances efficiency and effectiveness via searching the best model architectures with HyperKD during model size reduction. Extensive experiments validate MobileSAM2 across multiple benchmarks and show promising generalization performance on embodied AI tasks.
[CV-73] Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction
链接: https://arxiv.org/abs/2607.12293
作者: Md Mahfuzur Rahman,Pengzhan Zhou,A F M Abdun Noor,Md Imam Ahasan,Kah Ong Michael Goh,S. M. Hasan Mahmud,Md Mustafizur Rahman,Kaixin Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 5 figures, 4 tables. Under review at PeerJ Computer Science
Abstract:Predicting pedestrian crossing intention is a safety-critical task for autonomous driving, yet existing approaches often rely on single-modal inputs or dense multimodal fusion strategies that inadequately capture complementary visual and kinematic information while introducing redundant inter-modal interactions. We propose ADAPT (Adaptive Domain-Aware Pedestrian Crossing Transformer), a multimodal framework that jointly models local and global visual context together with temporal motion dynamics for accurate pedestrian crossing intention prediction. ADAPT processes four spatially aligned visual modalities, including RGB images, local depth maps, global semantic maps, and global depth maps, together with ego-vehicle speed, pedestrian bounding boxes, and skeleton pose information through five specialized modules: a weight-shared Swin Transformer V2 backbone for visual feature extraction, a Cross-Modality Guided Attention module for hierarchical visual fusion, a Mamba-based Motion Feature Encoding module for efficient temporal modeling, a Sparse Cross-Modal Attention module that selectively preserves the most informative inter-modal interactions, and a Vision Transformer-based Temporal Feature Fusion module for sequence-level prediction. Extensive experiments on the JAAD and PIE benchmark datasets demonstrate that ADAPT consistently outperforms existing state-of-the-art methods while maintaining low computational complexity. On JAAD, the proposed method achieves an AUC of 0.73 on JAADbeh and 0.85 on JAADall, while on PIE it achieves an accuracy of 0.92 and an AUC of 0.90. Furthermore, ADAPT performs inference in only 17.23 ms per sample, offering an effective balance between predictive accuracy and real-time deployment efficiency for intelligent transportation and autonomous driving applications.
[CV-74] Semantic-Edge Response Decoding of SAM3 for Zero-Shot Crack Segmentation
链接: https://arxiv.org/abs/2607.12292
作者: Shipeng Liu,Zhanping Song,Liang Zhao,Dengfeng Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Crack segmentation is essential for infrastructure inspection and structural health assessment, but existing high-performance methods typically require task-specific pixel-level annotations and training. Text-promptable vision foundation models enable zero-shot deployment, yet their final mask proposals are poorly suited to thin, fragmented, and low-contrast cracks, whose evidence may be suppressed, truncated, or over-expanded during mask generation. We find that language-conditioned semantic responses within the SAM3 decoder preserve more continuous and complete crack evidence than its final masks. Based on this observation, we propose Semantic-Edge Response Decoding (SERD), which interprets internal responses as a dense crack-likelihood field, calibrates them with a lightweight edge prior, and generates crack masks using a unified global threshold, without annotation or fine-tuning. Experiments on six public datasets show that SERD consistently improves over native SAM3 and outperforms the compared zero-shot and open-vocabulary segmentation methods, achieving an average Crack IoU of 61.14%, 4.63 points higher than SAM3. Further analyses show that most gains arise from directly decoding internal semantic responses, while edge calibration improves structural recovery and false-positive control without increasing end-to-end inference overhead. These results suggest that, for thin and non-compact targets, internal continuous responses can provide a more transferable interface than the final masks of foundation models. Code is available at: this https URL
[CV-75] GeoSEAN: Explainable Country-Level Image Geolocation for ASEAN Regions
链接: https://arxiv.org/abs/2607.12284
作者: Muhamad Syukron,Danish Rafie Ekaputra,Tintrim Dwi Ary Widhianingsih
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Image geolocation aims to infer the geographic origin of an image from visual content alone. However, this task remains challenging in regions where countries share similar urban, roadside, architectural, and environmental characteristics. Many existing geolocation models focus on coordinate level prediction or classification performance while providing limited insight into how visual evidence contributes to location predictions. This study presents an explainable country level image geolocation pipeline for 11 ASEAN countries. First, we collected 4,850 images from GeoGuessr style sources, Google Images, and additional street level imagery. We then evaluated three approaches on this dataset: CLIP zero shot classification, a LightGBM classifier, and an MLP classifier. The MLP achieved the best test performance, attaining an accuracy and F1 score of 85.91%. For explainability, predictions generated by the MLP classifier were analyzed post hoc using CLIP attention rollout, YOLO26 object detection on the original images, and Energy Based Pointing Game (EBPG) overlap metrics. Object level analysis indicates that frequently detected objects are not necessarily associated with the highest attention density, suggesting that object frequency and attention based visual evidence capture different aspects of a scene. These results demonstrate that the proposed model can support accurate regional image geolocation while enabling object level inspection of the visual cues underlying its predictions.
[CV-76] Auditing Data Leakage in Whole-Slide Image Multimodal Benchmarks
链接: https://arxiv.org/abs/2607.12278
作者: Wenhao Zhang,Zhongliang Zhou,John Kang,Sheng Li
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Recent vision-language models (VLMs) for computational pathology report striking zero-shot performance on whole-slide image (WSI) visual question answering (VQA) benchmarks. We audit these claims and find them fundamentally compromised by data leakage at two hierarchical levels: patient-level leakage, where slides from the same case appear in both training and test folds, and institutional-level leakage, where different cases nonetheless share staining-batch and scanner signatures through a common Tissue Source Site (TSS). By tracing canonical slide, case, and TSS identifiers across major public resources, we document case level train test overlaps of 92.3~100% on TCGA-derived benchmarks, together with near-complete TSS overlap. We further demonstrate that both leakage levels are linearly decodable from foundation-model feature space, that they induce a measurable accuracy gap between leaked and audit-clean cases on a published checkpoint, and that across multiple published WSI VLMs, peak reported accuracies concentrate on the most heavily contaminated benchmarks. Therefore, the current WSI VQA evaluation cannot distinguish genuine multimodal reasoning from nearest-neighbor retrieval over memorized institutional and patient-specific artifacts. Finally, we outline concrete recommendations for contamination-free evaluation. By addressing benchmark construction, provenance disclosure, and automated overlap auditing, we aim to guide future research toward verifiable claims of progress.
[CV-77] How to Realize Recursively Self-Improving Agents and Personal Singularity: A Goal- Scope- Tool- and Benchmark-Driven Multi-Agent Architecture
链接: https://arxiv.org/abs/2607.12254
作者: Chengshuai Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 4 figures, 5 tables, and 4 algorithms. Position and systems-design paper presenting a research architecture and evaluation roadmap
Abstract:Large language model (LLM) agents can increasingly plan, use tools, maintain memory, and execute long-horizon tasks. These advances motivate two linked questions: how can an agent improve the mechanisms by which it learns and acts, and how can that improvement increase the durable capabilities of its user rather than only the software itself? This paper proposes a governed multi-agent architecture for recursively self-improving agents and introduces personal singularity as a bounded human-AI co-development objective: helping a user approach an expanding, user-defined capability frontier across selected domains. Each agent is defined by a goal contract, bounded scope, validated tool registry, tool-level tests, end-to-end benchmarks, an owner-controlled autonomy policy, a routing policy, memory, and an improvement policy. Out-of-scope tasks are transferred to another accountable agent or to a newly created niche agent. A user-facing Auto-Index selects interactive, hybrid, autonomous, or scheduled operation without overriding external permissions. The architecture combines a fast planner-executor-verifier loop, a slower evidence-gated improvement loop, an external governance plane, decentralized agent lineages, an owner-directed agent foundry, and a Personal Singularity OS coordinating working, computational-imaging, process-learning, and personal-learning agents. We formalize scope, routing, improvement acceptance, bounded goal evolution, tool-first execution, and human capability transfer, and provide safety invariants, benchmark design, and an implementation roadmap. This is a position and systems-design paper, not evidence that unrestricted recursive self-improvement or personal singularity has already been achieved.
[CV-78] Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection
链接: https://arxiv.org/abs/2607.12245
作者: Jiaqi Kuang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Probability (math.PR)
备注:
Abstract:Few-shot industrial defect detection remains difficult for standard supervised detectors, which achieve poor performance on boundary-dominated industrial defects. This paper proposes rough path signature-guided geometry augmentation (RPS-GA), a geometry-aware approach in which Canny edge contours are treated as ordered planar paths whose truncated second-order signature responses, especially the antisymmetric Lévy-area term, are aggregated into a spatial map that highlights boundary-related structure through two fusion operators, SIG-AUG and SGAA. The approach is evaluated on NEU-DET and PCB-Defect under a few-shot protocol with 5, 10, 20, or 50 labeled images per class, using an unmodified YOLOv8n detector throughout. Compared with the baseline, RPS-GA delivers large gains when supervision is limited, although the margin shrinks as more labels become available. On NEU-DET, SIG-AUG raises 10-shot mAP@0.5 from 0.341 to 0.583, whereas on PCB-Defect, SGAA improves 10-shot mAP@0.5 from 0.086 to 0.299 and yields usable detection at 5-shot where the baseline fails entirely. These trends are confirmed by multi-seed evaluation across independent random partitions. Overall, the results indicate that second-order path-signature geometry offers a practical way to strengthen few-shot industrial defect detection without meta-learning or detector redesign.
[CV-79] he GEST-Engine: From Event Graphs to Synthetic Video. A Full Technical Report
链接: https://arxiv.org/abs/2607.12231
作者: Nicolae Cudlenco,Mihai Masala,Marius Leordeanu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present the GEST-Engine, a complete system that goes from natural-language text to fully-annotated multi-actor video. At its core is an explicit world model: rather than encoding state as a learned latent, the engine maintains a complete, inspectable representation of the world (which actors exist, where they are, what they are doing, which objects they hold, and how events relate in time and space), expressed as a formal Graph of Events in Space and Time (GEST) and realized deterministically inside the open world of a commercial game engine driven through an open-source multiplayer scripting framework. GESTs are produced either procedurally or by an agentic text-to-GEST system in which an LLM Director plans a story through tool calls validated by a programmatic state backend, so every generated specification is executable by construction. A GEST then enters a four-stage execution pipeline: graph parsing and validation, entity and action grounding, temporal orchestration (Allen-style constraints resolved by Floyd-Warshall transitive closure), and execution and capture. In a single simulation pass the engine emits frame-aligned RGB video, dense per-pixel depth, instance segmentation, per-actor skeletal pose, per-frame pairwise spatial-relation graphs, 2D bounding boxes, event-to-frame temporal mappings, and natural-language descriptions, all at zero marginal annotation cost. We further describe an in-game world editor, runtime capability extraction, a text-generation pipeline, and a production system that renders corpora at scale across parallel virtual machines. Because every frame traces back to a semantic specification, the engine guarantees object permanence, multi-actor coordination, and temporal consistency by construction, making its output valuable as training data, evaluation benchmarks, and diagnostic tools for video understanding.
[CV-80] Beyond Perfect Priors: Adaptive Gaussian Graph for 4D Driving Reconstruction in the Wild
链接: https://arxiv.org/abs/2607.12214
作者: Xiaoyun Dong,Qian Xu,Yun Wang,Yang Lu,Jen-Ming Wu,Jianping Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing 4D driving scenes in the wild (e.g., internet and AI-generated videos) is critical for diverse autonomous driving simulation. While recent Gaussian Scene Graph (GSG) methods achieve impressive visual quality, they heavily rely on precise priors, such as accurate camera poses and LiDAR depth, or manual annotations. When initialized with noisy priors estimated from in-the-wild videos, existing GSG methods suffer from optimization ambiguity (e.g., entangling camera and agent poses) and topological failures (e.g., missing objects), causing severe rendering artifacts. To enable robust in-the-wild reconstruction, we introduce Adaptive Gaussian Graph (AGG), a self-correcting 4D framework. Our Semantically-Guided Tick-Tock Strategy leverages 2D foundation features to explicitly decouple static background and camera pose updates from dynamic agent learning. Concurrently, our Adaptive Topology Evolution module actively rectifies graph structures by spawning missing agents, reassigning misclassified Gaussians, and pruning false positives. To rigorously evaluate this in-the-wild setting, we introduce Wild-30, a challenging benchmark of internet and generative videos. Extensive experiments on KITTI and Wild-30 validate that AGG consistently outperforms state-of-the-art approaches in visual fidelity and robustness under noisy priors.
[CV-81] RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration
链接: https://arxiv.org/abs/2607.12206
作者: Jiahao Luo,Hao Zhang,Jianqi Chen,Yijie He,Jiaxu Zou,Michael Vasilkovsky,Sergei Korolev,Sergey Tulyakov,Chaoyang Wang,Peter Wonka,James Davis,Jian Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:We present RegHead, a framework for constructing semantic blendshape sets for animatable non-humanoid head avatars. With a fixed expression vocabulary, semantic blendshapes provide a low-dimensional and interpretable animation interface and support cross-identity retargeting. Building such blendshape sets remains expensive because (i) expression-consistent supervision is scarce, (ii) generated 4D assets typically lack correspondence, and (iii) facial motion is highly localized. We propose (1) a large-scale dataset of non-humanoid identities paired with a shared expression vocabulary, obtained by expanding a small artist-rigged library via fine-tuned image editing; (2) a dense stochastic anchor motion representation tailored to localized facial deformations; and (3) a fast feed-forward registration model that converts unregistered expression meshes into a corresponded blendshape basis by predicting anchor-based deformations from the neutral shape. Experiments show that our approach produces higher-fidelity expression meshes than baselines, while running orders of magnitude faster than optimization. We further demonstrate real-time retargeting from human face tracking signals to non-humanoid characters, capturing both head pose and localized facial motions. Our project page is available at this https URL.
[CV-82] Overview of Cross-Component In-loop Filters in Video Coding Standards
链接: https://arxiv.org/abs/2607.12186
作者: Zhaoyu Li,Xuewei Meng,Jiaqi Zhang,Cheng Huang,Chuanmin Jia,Siwei Ma,Yun Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper was submitted and accepted by ZTE Communications
Abstract:In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capability. In the early stage of video coding, in-loop filters, such as Deblocking Filter, Sample Adaptive Offset, and Adaptive Loop Filter, were performed separately for each component. Recently, cross-component filters were studied to improve the chroma fidelity by exploiting correlations between the luma and chroma channels. This paper summarizes the cross-component filters used in the state-of-the-art video coding standard. Specifically, it includes the Cross-Component Adaptive Loop Filter and Cross-Component Sample Adaptive Offset. Cross-component filters aim to reduce compression artifacts based on the correlation between different components and provide more accurate pixel reconstruction values. In this paper, we introduce the origin, development, and status of cross-component filters in the current video coding standards. Finally, we had some discussions on the further evolutions of cross-component filters.
[CV-83] he Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agent ic Reasoning
链接: https://arxiv.org/abs/2607.12177
作者: Shelley Cazares
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 4 figures. To appear in Lecture Notes in Computer Science (LNCS)
Abstract:The analysis of satellite and aerial imagery has entered a new era with the advent of foundation models. This paper describes the concept of Geospatial Foundation Models (GeoFMs), which are artificial intelligence/machine learning (AI/ML) models pre-trained on massive geospatial datasets through varied methodologies. We first articulate the core paradigm shift that GeoFMs enable: a separation of duties, where large-scale model providers perform the computationally intensive pretraining, allowing domain experts to rapidly fine-tune or prompt these models for specific, mission-critical tasks. This approach democratizes access to state-of-the-art AI/ML while maintaining the security and confidentiality of the downstream task. We then explore the novel capabilities unlocked by different types of GeoFMs, distinguishing between the finetunable vision models produced by self-supervised techniques like masked auto-encoding, and the vision-language models produced by contrastive learning which enable zero-shot tasks like open-vocabulary image analysis. Next, we discuss the practical considerations for operationalizing GeoFMs, from performance-cost analysis to the broader MLOps ecosystem. To that end, we introduce a taxonomy of model adaptation strategies and propose a framework for domain experts to select the most cost-effective adaptation approach for their particular mission set. Finally, we present a forward-looking vision of Agentic Geospatial Reasoning, where Large Language Models act as intelligent orchestrators, leveraging GeoFMs as tools to answer high-level user queries in natural language and automate complex analytical workflows, moving the field from perception to cognition.
[CV-84] A Calibrated Multimodal Ensemble for Ambivalence/Hesitancy Recognition: System Description and Private-Test Submission Strategy ECCV
链接: https://arxiv.org/abs/2607.12176
作者: Josep Cabacas-Maso,Ismael Benito-Altamirano,Carles Ventura
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 1 figure, ECCV workshops
Abstract:Ambivalence and hesitancy (A/H) undermine digital behaviour-change interventions, and recognizing them automatically from video is the goal of the ABAW A/H challenge on the BAH dataset. We describe our system for the 11th edition of the challenge: a calibrated, equal-weight ensemble of three fusion models over frozen face, audio, text, and pose embeddings, which reaches 0.7358 macro-F1 on the public test set. This year’s private test, released on a disjoint set of 30 new participants, is scored on five allowed submissions; we report the configuration and rationale of each of our five submissions, and, where already available, the private-test score obtained. Our first submission, an exact replica of the calibrated ensemble tuned only on public validation, scored 0.7361 macro-F1 on the private test, matching our public-test estimate almost exactly and confirming the pipeline generalizes to unseen participants without leakage.
[CV-85] From Reconstruction to Interpretation: Zero-Setup Multi-Phase Segmentation of X-ray Tomography Data
链接: https://arxiv.org/abs/2607.12175
作者: Pradyumna Elavarthi,Arun J. Bhattacharjee,Harrison Lisabeth,Anca Ralescu,Petrus H. Zwart,Dilworth Parkinson,Elizabeth G. Clark
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric data acquisition and reconstruction. However, rapid interpretation remains limited by image segmentation, which often requires manual thresholding, user prompting, or material-specific model training. We present a zero-setup framework for multi-phase segmentation of synchrotron X-ray tomography data that generates interpretable masks for previously unseen datasets without user input or retraining during deployment. The framework combines a material-agnostic mask preparation strategy with a pretrained semantic segmentation network. It represents commonly occurring structural regions as background, sample, bright, dark-gray, light-gray, and porosity masks. Unlike conventional deep learning pipelines that require dataset-specific annotations and retraining, the proposed framework can be applied directly to new scans and produce diagnostic-level segmentations within minutes of reconstruction. This enables rapid assessment of scan quality, sample morphology, porosity, and attenuation variations during ongoing beamline experiments. The generated masks can later be manually refined or used to fine-tune application-specific models when greater accuracy or material-specific labeling is required. Evaluation on held-out synchrotron micro-CT images and qualitative testing on additional datasets demonstrate consistent and physically meaningful segmentations across varying samples and imaging conditions. The framework also substantially outperforms conventional intensity-based thresholding. By connecting high-speed reconstruction with immediate interpretation, the approach supports near-real-time beamline feedback and scalable AI-assisted scientific imaging workflows.
[CV-86] Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models
链接: https://arxiv.org/abs/2607.12171
作者: Xu Han,Jiajing Hu,Li-Ping Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Published in Transactions on Machine Learning Research
Abstract:In rectified-flow-based generative models, the neural network can be trained to predict two different targets, such as the instantaneous velocity or the data endpoint, to perform denoising. Although prior work shows that these parameterizations lead to different empirical behaviors, the mechanisms underlying their respective advantages remain to be underexplored, and how to combine them effectively is still unclear. In this work, we analyze how learning errors from different parameterizations affect the generation performance. We show that predicting the data endpoint has a clear training signal that stabilizes training, whereas predicting the velocity maintains stable sampling dynamics near the data manifold. Motivated by these insights, we propose Self-Consistent Flow (SC-Flow), a new method that unifies the benefits of both parameterizations. By employing a lightweight consistency loss, SC-Flow jointly trains a single network to predict both the local velocity and the data endpoint, and the consistency between the two predictions improves the model’s performance. The method requires no major architectural changes and adds minimal computational overhead. Extensive experiments on image generation tasks demonstrate that SC-Flow substantially stabilizes optimization and improves the straightness of generation paths, leading to significant gains in generation quality over standard rectified-flow baselines.
[CV-87] Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset
链接: https://arxiv.org/abs/2607.12165
作者: Kuniko Paxton,Amila Akagić,Koorosh Aslansefat,Dhavalkumar Thakker,Yiannis Papadopoulos
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recently, the societal implementation of high-performance image classification models has expanded rapidly. While these models require vast amounts of training data to improve performance, securing sufficient real images is often impractical. As a means to compensate for this shortage, the use of synthetic data is becoming widespread. However, synthetic images are not necessarily equivalent to real images for training purposes. This study systematically analyzes the differences between two types of synthetic images created by different generation methods and real images from three perspectives: high-dimensional feature space, low-level statistics in color space, and the model training process. Furthermore, it experimentally verifies how synthetic data should be utilized by considering realistic data mixing scenarios. This enables the proposal of an evaluation and application strategy for performing preliminary assessments on synthetic images of unknown quality and safely incorporating them into training. This research aims to contribute to enhancing the reliability and safety of image classification models utilizing synthetic images.
[CV-88] GaitSpan: Growing Humanoid Locomotion from Walking to Running
链接: https://arxiv.org/abs/2607.12114
作者: Kwan-Yee Lin,Zilin Wang,Janelle J. Liu,Stella X.Yu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:A humanoid that can walk should not relearn locomotion from scratch to jog or run. Yet current approaches often obtain gait diversity by prescribing gait schedules, imitating motion clips, training experts to switch between or distilling skills into one policy. These strategies can produce impressive behaviors, but offer limited flexibility across continuous speed commands, terrains, and morphologies. We study skill growth with GaitSpan, a framework that expands a pretrained, basic walking policy into faster locomotion. It treats walking as a seed skill: reusable motor structure for balance, support, body coordination, and contact transition that can be regenerated at new rhythms, extended into longer/higher strides, and corrected by residual adaptation. This expansion has three aspects: 1) rhythm generation, which modulates the frozen walking policy with multiple internal clocks and learns command-conditioned combinations of the resulting canonical actions; 2) stride shaping, which rewards dynamic locomotion patterns appropriate for higher commanded speeds using a physically grounded objective inspired by spring-loaded inverted pendulum dynamics; and 3) residual adaptation, which captures motion details not accounted for by rhythm generation or stride shaping. GaitSpan is the first to deliver a single command-conditioned humanoid policy that spans walking, jogging, and running-like regimes covering a continuous speed range, transfers across morphologies, and deploys zero-shot on unseen sim-to-sim, and real-world terrains. Compared with baselines either trained with multi-experts or imitation from humans, it learns faster and achieves stronger gait performance.
[CV-89] Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
链接: https://arxiv.org/abs/2607.12112
作者: Jing Liu,Chenxuanyin Zou,Jiayang Ren,Gaoyun Fang,Chengfang Li,Yan Wang,Zhenchao Ma,Bo Hu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: submitted to IEEE JSTSP
Abstract:Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations. Addressing this challenge is especially critical for autonomous networked AI operating in safety-sensitive domains, such as content moderation, where reliable retention of prior knowledge underpins system integrity. To overcome this, we propose Federated Continual Multimodal Learning (FedCMM), a framework that embeds continual-learning safeguards into the federated optimization loop at three complementary levels. At the parameter level, modality-aware elastic weight consolidation computes separate Fisher information matrices for the vision encoder, language backbone, and cross-modal projector, providing granular, asymmetry-aware protection against modality-specific forgetting. At the data level, each client trains a lightweight local generative replay module to synthesize raw-data-free embedding-level multimodal replay tuples without any raw data sharing. At the aggregation level, Task-similarity-aware gradient aggregation autonomously filters and reweights client updates by gradient cosine similarity, suppressing conflicting directions and stabilizing the global learning trajectory. Extensive experiments on two benchmarks demonstrate that FedCMM consistently outperforms recent baselines on accuracy and backward transfer, confirming that holistic, modality-aware optimization enables robust evolutive adaptation across heterogeneous networked AI deployments.
[CV-90] ACZ-GSeg: Adaptive Concentric Zone-based Two-stage Ground Segmentation for LiDAR Point Clouds
链接: https://arxiv.org/abs/2607.12110
作者: Ge Zhang Chunyang Wang Bin Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Ground segmentation is a fundamental prerequisite for autonomous navigation, environmental perception, and object detection in ground mobile platforms. To address the under-segmentation of ground points caused by sparse long-range point clouds, ground undulations, and interference from non-ground structures in complex road scenarios, this paper proposes a two-stage ground segmentation method based on the Adaptive Concentric Zone Model. First, an Adaptive Concentric Zone Model is constructed to dynamically determine the number of sectors in each ring, thereby forming local zones with more balanced point distributions. Based on this model, a two-stage ground segmentation method is developed. In the coarse segmentation stage, a lowest-height seed constraint and height-decay weighting are introduced to establish a weighted principal component analysis plane fitting model, from which ground candidate points are extracted. In the fine segmentation stage, a reflectance intensity consistency constraint is employed to distinguish high-confidence ground points from uncertain points, and the uncertain points are further refined based on the local height stability of high-confidence neighborhoods. Experimental results show that the proposed method achieves Precision, Recall, and F1-score values of 99.12%, 96.24%, and 97.66% on the SemanticKITTI dataset, and 98.72%, 100.00%, and 99.36%, respectively, on a self-collected point cloud acquired using a RUBY-PLUS. The results demonstrate that the proposed method can effectively adapt to the range-dependent distribution characteristics of LiDAR point clouds, which are dense at near ranges and sparse at far ranges. It reduces the misclassification of non-ground points while maintaining ground point recall, thereby effectively improving the stability of ground segmentation.
[CV-91] Causal Supervision of Attention for Affective Behaviour Analysis
链接: https://arxiv.org/abs/2607.12091
作者: Nemanja Rašajski,Konstantinos Makantasis,Antonios Liapis,Georgios N. Yannakakis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 1 figure, 2 tables
Abstract:Affective Behaviour Analysis aims to enable machines to infer human affective states from behavioural signals, particularly facial expressions, in real-world environments. The \textit11th Affective Behaviour Analysis in-the-wild Competition includes the Multi-Task Learning Challenge based on the s-Aff-Wild2 database, where participants develop a unified framework for Valence-Arousal Estimation, Expression Recognition, and Action Unit Detection. This is challenging because emotion-related cues must be distinguished from spurious factors such as identity, illumination, pose, and demographic variation. Attention mechanisms are well suited as they aggregate information from the most informative facial regions, but may still exploit dataset-specific correlations instead of true affective cues. To improve generalization, we propose an attention pooling framework that promotes subject-invariant attention while increasing feature expressiveness. Our method consists of three components. First, we introduce causal supervision to enforce attention on facial regions with invariant predictive value across subjects. Second, we apply a cross-covariance independence regularization between Key (K) and Value (V) projections to encourage complementary, non-redundant representations. Finally, we replace the linear Value projection with a gated nonlinear SwiGLU transformation to increase feature expressiveness and capture finer-grained affective cues. Our method achieves CCC_VA=0.5123 for VA estimation on the official validation set, together with F1_EX=0.3116 and F1_AU=0.3974 for expression recognition and action unit detection, respectively, resulting in an overall P score (the sum of the individual task metrics) of 1.2214 .
[CV-92] NEEDL-Bench: Dataset for Swiss Needle Cast and Stomata Detection in Microscopy Images
链接: https://arxiv.org/abs/2607.12076
作者: Benjamin Blake,Declan McIntosh,Jürgen Ehlting,Nicolas Feau,Joey B. Tanney,Alexandra Branzan Albu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 Pages, Published at CRV2026
Abstract:We present NEEDL-Bench, a microscopy detection benchmark for Swiss Needle Cast (SNC), a fungal disease of Douglas-fir trees. Douglas-fir is a keystone species of major ecological and economic importance as a softwood timber resource, and SNC affects productivity by forming sexual reproductive structures (pseudothecia) that emerge through the gas exchange pores (stomata) of the needles, thereby blocking gas exchange and compromising needle function. To date, there is no dataset for automatic computer vision detection of these structures, despite computer vision being well poised to standardize and viably scale severity measurements. To address this, we present NEEDL-Bench, a dataset of 3250 annotated images from 1082 Douglas-fir needles, annotated for both keypoints and bounding-box detectors. This dataset exhibits a challenging collection of features, including blur, poor object contrast, small objects of interest, and occlusions. To better capture both the nominal distribution of the data and the full breadth of rare structures, we present two distinct evaluation splits: either random sampling from the collected images or sequential sampling to maximize structural diversity. We evaluate multiple popular keypoint and bounding box methods for detection on this dataset as a baseline and observe a maximum F1 score of 0.8479, suggesting significant potential for gains from future development on this problem. Further, we find that larger models generally do not show commensurate gains in performance on this dataset, indicating that improvements on this problem will not come from scaling laws but rather from domain-specific inductive biases.
[CV-93] Enabling 24-hour Agricultural Robotics: Unsupervised Day-to-Night Cross-Modal Image Translation for Nighttime Visual Navigation IROS2026
链接: https://arxiv.org/abs/2607.12065
作者: Robel Mamo,Rajitha de Silva,Grzegorz Cielniak,Taeyeong Choi
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to IROS2026
Abstract:While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions. In fact, deploying autonomous robots at night offers significant advantages, including 24-hour crop and soil monitoring, fruit harvesting, and nocturnal pest detection. Modern vision-based systems, however, rely heavily on large-scale well-annotated image datasets, which remains challenging to obtain for nighttime operation scenarios. To address this, we propose an unsupervised image translation framework that converts daytime plant-row RGB images into near-infrared (NIR) nighttime counterparts without requiring pixel-to-pixel supervision. This enables the direct reuse of daytime semantic labels for training nighttime perception models. In particular, by incorporating a pre-trained Contrastive Language-Image Pre-training (CLIP) model, the proposed framework is designed to preserve semantic consistency during day-to-night translation. Additionally, a visibility mask is introduced to account for the limited effective range of NIR illumination in nighttime scenes. We conduct comparative evaluations with state-of-the-art image translation baselines and demonstrate higher image qualities, as supported by improved performance in downstream semantic segmentation for nighttime visual navigation. For evaluation, we utilize AgriNight–a novel dataset comprising 428 daytime and 549 nighttime images collected using night-vision-equipped mobile robots in agricultural fields and manually annotated with pixel-wise semantic labels–and introduce it as the first benchmark for nighttime agricultural visual navigation. We also perform real-time autonomous navigation experiments with a physical robot operating at night. The data and code are available at: this https URL.
[CV-94] Learning from Complementary Ultrasound Representations for Liver Disease Classification MICCAI2026
链接: https://arxiv.org/abs/2607.12062
作者: Sabahattin Mert Daloglu,Gokce Bekar,Ceren Coskun,Senanur Sahin,Harvey Castro,Soner Hacihaliloglu,Halley P. Letter,Ilker Hacihaliloglu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Submitted to the MICCAI 2026 ASMUS Workshop (under review)
Abstract:Differentiating non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver disease (NAFLD) using ultrasound remains challenging due to subtle tissue alterations and the limited information available in conventional B-mode imaging. In this work, we investigate whether complementary ultrasound representations derived from the same acquisition can improve NASH versus NAFLD classification. Specifically, we combine conventional B-mode ultrasound with physics-guided and local phase-based image representations and evaluate their effectiveness using self-supervised masked autoencoders (MAEs) and graph convolutional networks (GCNs). Experiments were conducted on a multi-site Mayo Clinic cohort consisting of 2,547 liver ultrasound scans from 125 patients. Compared with conventional B-mode ultrasound alone, complementary ultrasound representations consistently improved classification performance, yielding gains of up to 32.4% in accuracy and 91.2% in F1-score. Furthermore, performance improvements were consistently observed across age groups, sex, race, ethnicity,and acquisition sites.
[CV-95] Representation and Reference Selection in Training-Free Synthetic Image Attribution
链接: https://arxiv.org/abs/2607.12052
作者: Meiling Li,Pietro Bongini,Benedetta Tondi,Mauro Barni
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 6 pages, 5 figures, 4 tables
Abstract:Synthetic image attribution aims at identifying the generator responsible for a given AI-generated image. Training-free reference-based attribution methods are easily scalable, since newly emerging generators can be incorporated by adding source-specific references rather than retraining a task-specific classifier. Their performance depends on two coupled factors: the representation space used for comparison and the way source-specific references are constructed. However, the interaction between these two factors remains largely unexplored. In this paper, we provide a controlled analysis of this interaction using references and off-the-shelf pretrained representations. We study representations extracted from different layers of CLIP and DINOv2, along with three reference selection methods with varying semantic constraints: arbitrary, semantically aligned, and resynthesis-based references. Our results show that attribution accuracy consistently peaks at intermediate representation levels, indicating that source-discriminative cues are more accessible before strong semantic abstraction dominates. We further show that intermediate representations are not completely semantically neutral, making reference selection critical: semantically constrained references reduce query-reference mismatch and improve attribution, especially under limited reference budgets. Resynthesis is most useful in low-reference regimes, while semantically aligned references provide a better accuracy-cost trade-off when a moderate-sized reference pool is available. Our findings show that training-free reference-based attribution should be understood as the interaction between where images are compared, how the reference set is constructed, and how many references are available.
[CV-96] SymbOmni: Evolving Agent ic Omni Models via Symbolic Concept Learning ECCV2026
链接: https://arxiv.org/abs/2607.12042
作者: Jinxiu Liu,Jianru Li,Tanqing Kuang,Xuanming Liu,Kangfu Mei,Yandong Wen,Weiyang Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: ECCV 2026 (49 pages, 10 figures, project page: this https URL )
Abstract:Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation. Yet prevailing monolithic models remain fundamentally constrained by their inability to learn cumulatively and evolve autonomously, which is a limitation we term the “perpetual novice” problem. They lack mechanisms for structuring experience into reusable knowledge and therefore rely on brittle, “from-scratch” reasoning for each task, resulting in poor compositional generalization and inefficient knowledge retention. Motivated by these limitations, we propose SymbOmni, an agentic omni-model designed for cumulative evolution through Symbolic Concept Learning. At its core is the Symbolic Concept Box, an optimizable memory module that abstracts low-level operations into reusable Symbolic Workflow Instructions. SymbOmni operates through an induction-transduction cycle: experiences are abstracted into symbolic concepts (induction), which are then adaptively composed to solve novel tasks (transduction). The training is done by verbalized backpropagation with language-based feedback to enable continuous self-improvement without gradient-based model fine-tuning. Comprehensive experiments validate that (I) SymbOmni significantly outperforms existing agent-based systems for iterative creation and also surpasses closed-source models (e.g., Nano Banana, GPT-Image-1) in both image quality and task success rates; (II) SymbOmni effectively reduces token consumption by over 40% while maintaining competitive generation quality; and (III) SymbOmni enables effective continual learning by achieving cumulative gains across multiple online-learning benchmarks and setting a new state of the art.
[CV-97] MetaView: Monocular Novel View Synthesis with Scale-Aware Implicit Geometry Priors ECCV2026
链接: https://arxiv.org/abs/2607.12000
作者: Yufei Cai,Xuesong Niu,Hao Lu,Kun Gai,Kai Wu,Guosheng Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted to ECCV 2026
Abstract:Current visual generation models are capable of producing high-quality content, yet they lack a coherent perception of the spatial structure. Existing generative novel view synthesis methods typically introduce explicit geometry priors, which enforce spatial consistency but inherently restrict generalization in large view changes. In contrast, recent interactive generative methods favor implicit scene modeling, offering greater flexibility at the cost of precise camera control and geometry consistency. In this paper, we propose MetaView, a diffusion-based monocular novel view synthesis framework that enables rendering under large view changes from a single image. Our key insight is to combine implicit geometry modeling with minimal yet essential explicit 3D cues: we incorporate implicit geometry priors from a feed-forward geometry perception network to regularize structure without imposing restrictive reconstruction pipelines, while leveraging metric depth to anchor the generation to a metric scale. This design allows MetaView to achieve both geometry consistency and precise controllability. Extensive experiments demonstrate that, under challenging monocular large viewpoint changes, MetaView significantly outperforms existing methods and exhibits superior generalization. Our code is publicly available at this https URL.
[CV-98] Anatomy-Privileged Distillation with Token Routing for MRI-Based Prediction of Perineural Invasion
链接: https://arxiv.org/abs/2607.11987
作者: Hyunsu Go,Youngung Han,Kyeonghun Kim,Junga Kim,Dohyun Kweon,Jinyong Jun,Sungha Park,Anna Jung,Induk Um,Yului Jeong,Suah Park,Jina Jeong,Pa Hong,Woo Kyoung Jeong,Won Jae Lee,Ken Ying-Kai Liao,Hyuk-Jae Lee,Nam-Joon Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Perineural invasion (PNI) is associated with poor postoperative outcomes in intrahepatic cholangiocarcinoma, but it is confirmed by surgical pathology. Existing preoperative imaging models often rely on radiologist-defined variables, contrast-enhanced imaging, or manual annotations. We propose an anatomy-privileged teacher–student framework for patient-level PNI prediction from T2-weighted MRI. During training, the teacher uses MRI with tumor and liver masks to learn dense token routing, and the student distills this guidance to retain and aggregate informative tokens under a fixed budget. Anatomical supervision is restricted to training, and the deployed model does not require masks at inference. In 155 patients, the proposed method achieved the highest mean AUROC of 0.750 among matched MRI-only baselines evaluated under the same protocol, with 1.43 GFLOPs and 8.02 ms per case on a Jetson Orin Nano Super Developer Kit.
[CV-99] SpikeDS: Dual Sparsity Spikformer for Perineural Invasion Prediction in 3D MRI
链接: https://arxiv.org/abs/2607.11986
作者: Induk Um,Youngung Han,Kyeonghun Kim,Yului Jeong,Jina Jeong,Hyunsu Go,Dohyun Kweon,Sungha Park,Junga Kim,Anna Jung,Suah Park,Hyuk-Jae Lee,Pa Hong,Woo Kyoung Jeong,Won Jae Lee,Ken Ying-Kai Liao,Nam-Joon Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Perineural invasion (PNI) is associated with poor prognosis in cholangiocarcinoma (CCA). However, its detection from 3D MRI remains challenging due to the subtle and spatially heterogeneous imaging signatures at the tumor periphery. Capturing such spatially sparse cues necessitates volumetric analysis of 3D MRI, but existing deep learning approaches incur prohibitive computational costs on volumetric medical images, limiting their clinical deployment. We propose Dual Sparsity Spikformer (SpikeDS), a spiking neural network architecture that jointly exploits activation sparsity from binary spike communication and spatial sparsity from window pruning based on firing rates. SpikeDS introduces Dual Sparsity Spiking Attention (DSSA), which combines two complementary mechanisms. The first is Window-based Expert Mixture Spiking Attention (W-EMSA), which selectively applies attention only to salient windows identified by their firing rates. The second is Cross-Window Spiking Self-Attention (CW-SSA), which enables global context exchange through an asymmetric scheme in which pruned windows still contribute as key-value sources. Evaluated on a clinical cohort of 139 CCA patients via 5-fold cross-validation, SpikeDS achieves an AUC of 0.753 while consuming only 14.4 mJ, surpassing the best baseline in both AUC and energy efficiency. These results suggest that dual sparsity provides an effective hardware-aware strategy for improving the efficiency of 3D spiking transformers without compromising diagnostic performance.
[CV-100] Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI
链接: https://arxiv.org/abs/2607.11962
作者: Fabian Mager,Lars Kai Hansen
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Self-supervised learning offers a compelling approach for medical imaging, where labeled data are scarce and acquisition costs are high. We present COJEPA, a self-supervised framework for volumetric brain MRI that combines a joint-embedding predictive architecture (JEPA) with a contrastive loss (CO), targeting two complementary properties: local predictivity and global discriminability. The model is trained without labels on T1-weighted structural MRI from two cohorts (HCP-YA and AABC, N=2286 , ages 22 to 90), extending I-JEPA to 3D with foreground-aware block masking, a hierarchical convolutional patch embedding, and world-space sinusoidal positional encodings. We evaluate all three objectives across zero-shot twin retrieval, brain tumor segmentation (BraTS 2024), and age regression (OpenBHB). COJEPA achieves the best monozygotic twin recall at rank@1 (0.84), the best finetuning age MAE (2.55 years on OpenBHB 3.0T), and matches CO on BraTS whole-tumor Dice, demonstrating that the combined objective yields representations that are simultaneously discriminative and locally structured.
[CV-101] Anomalous Frame Detection Using VLM-Based Description Comparison for Extracting Expert-Specific Actions and Contextual Decision-Making Scenes with Intra-Video Self-Similarity
链接: https://arxiv.org/abs/2607.11957
作者: Ryo Sakai,Kaname Yokoyama
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 11 figures, 2 tables
Abstract:Maintenance of critical infrastructures, such as railways and power plants, is essential for ensuring operational safety and reliability. However, the declining number of skilled maintenance workers highlights the need to transfer expert know-how to less experienced workers. Previous studies have attempted to extract candidates of expert knowledge by comparing videos of manual-based work with those of expert workers, mainly focusing on differences in observable actions. However, expert know-how is often embedded not only in actions but also in contextual decision-making during task execution. This paper proposes a method that detects anomalous frames between two task videos to automatically extract candidate scenes containing expert-specific actions and contextual decision-making scenes. The method generates frame-wise visual descriptions using a vision-language model (VLM). Expert-specific actions are extracted based on frame similarities computed from description comparisons between two videos, while contextual decision-making scenes are extracted using segment similarities derived from intra-video self-similarity of the descriptions. In simulated distribution board maintenance experiments involving 27 task scenarios, the proposed method achieved extraction rates of 65% for action candidates and 61% for decision-scene candidates, improving over conventional methods that achieved 59% and 33%, respectively. These results demonstrate the effectiveness of the proposed approach in discovering candidate scenes containing expert know-how.
[CV-102] GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization
链接: https://arxiv.org/abs/2607.11941
作者: Md Imam Ahasan,Guangchao Yang,A F M Abdun Noor,Kah Ong Michael Goh,S. M. Hasan Mahmud,Md Mahfuzur Rahman
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 20 pages, 8 figures, 4 tables. Under review at PeerJ Computer Science
Abstract:Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality. Existing deep learning-based low-dose CT (LDCT) reconstruction methods are typically optimized for fixed dose levels or specific anatomical regions, limiting their robustness and generalization in realistic clinical settings. We propose GenDiff, a generalizable diffusion-based framework for LDCT reconstruction that jointly models continuous radiation dose and anatomical information within a unified reconstruction network. The proposed framework integrates a Dose-Anatomy Encoder to learn acquisition-aware embeddings, a dose- and anatomy-conditioned cold diffusion backbone for iterative refinement, a physics-consistency update to enforce fidelity to the CT forward model, and a Structural Prior Refinement Module (SPRM) that preserves anatomical structures while suppressing dose-dependent artifacts. Extensive experiments on multi-anatomy clinical datasets, including unseen ultra-low-dose conditions as well as out-of-distribution phantom and animal datasets, demonstrate that GenDiff consistently outperforms state-of-the-art convolutional neural network and diffusion-based reconstruction methods. The proposed approach achieves superior reconstruction quality while maintaining strong robustness across different dose levels, anatomical regions, and acquisition domains, making it a promising solution for practical low-dose CT imaging.
[CV-103] SCA-Net: Temporal-Spatial Clique Attention for Interpretable Multimodal Pedestrian Trajectory Prediction ICDM
链接: https://arxiv.org/abs/2607.11939
作者: Md Mustafizur Rahman,Guangchao Yang,A F M Abdun Noor,Md Imam Ahasan,Md Mahfuzur Rahman,Md Ariful Islam
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures, 4 tables. Submitted to the IEEE International Conference on Data Mining (ICDM) 2026, Applied Track
Abstract:Accurate pedestrian trajectory prediction in crowded environments remains challenging due to the multimodal uncertainty of human motion and the variable complexity of motion dynamics across different scene contexts. Existing goal-conditioned models rely on static displacement structures that assign equal weight to all historical time steps, standard graph attention mechanisms, and fixed-capacity motion decoders that cannot adapt to local prediction complexity. To address these limitations, we propose TSCA-Net, a trajectory prediction framework built upon three complementary modules. The Temporal-Spatial Clique Attention (TSCA) module introduces learnable temporal gating into clique-based goal-history interaction, enabling time-aware modulation of historical observations relative to each candidate goal. The Cross-Pedestrian Clique Potential (CPCP) module models asymmetric pairwise agent relationships through a dynamic clique potential framework with a time-varying social graph. The Adaptive KAN Grid Refinement (AKGR) mechanism dynamically adjusts the B-spline grid resolution of a Kolmogorov-Arnold Network-augmented LSTM decoder based on per-agent goal distribution entropy, balancing model expressiveness against overfitting across varying motion complexities. Extensive experiments on the ETH/UCY and Stanford Drone Dataset benchmarks demonstrate that TSCA-Net achieves state-of-the-art performance, with average ADE/FDE of 0.13/0.20 m on ETH/UCY and 6.95/10.43 pixels on SDD. Comprehensive ablation studies confirm the complementary contributions of all three proposed modules.
[CV-104] Do You Remember? Toward Memory-Centric Multimodal AI
链接: https://arxiv.org/abs/2607.11919
作者: Xuguang Yu,Weigang Zheng,Minyue Yu
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 43 pages, 8 figures, 3 tables
Abstract:Human memory is reconstructive, not a faithful recording. Current multimodal LLMs (MLLMs) lack this capability: they process images through a frozen visual encoder, produce a one-shot text output, and discard internal representations. We present DoYouRemember, a three-stage architecture introducing reconstructive memory into MLLMs: (1) a VQ-VAE compresses images into discrete visual tokens, (2) a LoRA-fine-tuned LLM jointly attends to visual and text tokens, and (3) a Diffusion Decoder reconstructs images from the LLM’s hidden states. On 1,000 3D facial skin texture maps and 99,000 unlabeled facial images, we find that LLM hidden states contain approximately zero recoverable visual information – the same Decoder producing clear reconstructions from VQ-VAE tokens (pre-LLM) produces pure noise from LLM hidden states (post-LLM), demonstrating that the LLM understands images but does not remember them. Training a shared memory matrix M under backpropagation systematically fails due to gradient cancellation (O(1/sqrt(N)) attenuation). We identify three root causes and show that local EMA updating resolves all three: each image updates only its top-8 slots out of 64, preserving inter-slot diversity. The resulting M (229K parameters, 16x compressed) approaches the VQ upper bound on unseen test images. Scaling to 1,024 slots surpasses it (LPIPS 0.056 vs. 0.071), as M’s continuous representation avoids VQ quantization error. We unify these findings under an information-theoretic framework: memory is lossy compression, recall is decompression, and hallucination is an inherent property of lossy decompression rather than a defect.
[CV-105] Exact and Calibrated Diffusion Reconstruction for Digital Breast Tomosynthesis
链接: https://arxiv.org/abs/2607.12937
作者: Imade Bouftini
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Limited-angle digital breast tomosynthesis (DBT) reconstructs a volume from a few low-dose projections over a narrow arc. At a representative nine-view, 25^\circ protocol more than 98% of image space is unmeasured, so a learned prior must supply structure in the missing wedge. Conditional diffusion priors achieve strong perceptual quality here but leave three clinical obstacles: inexact data consistency, unlocalized hallucination, and uncalibrated uncertainty. We enforce measurements exactly by replacing the per-step proximal update of a conditional diffusion sampler with exact Euclidean projection onto the data-consistent set, computed via an m -dimensional dual system with a one-time Gram matrix AA^\top factorization. This projection costs 4.5 ms per step (a 248\times speedup) and drives the data residual to the double-precision floor ( 2.4\times10^-13 ). We prove it is the \rho\to0 limit of the proximal step, provide a no-harm theorem, and show that exactly consistent sample ensembles have variance supported on null( A ). Thus, the mean’s entire error lies in the unmeasured subspace covered by the uncertainty map. On patient-derived breast phantoms, this improves fidelity at no depth-resolution cost. Conversely, a proximal step applied post-update degrades quality, isolating the consistency step’s placement as decisive. Isotonic recalibration brings the ensemble spread to a calibrated error scale (expected calibration error 0.029\to0.008 ; standardized error 4.7\to0.96 ), ranking errors better than the pure prior. We also repair a 20.3% adjoint mismatch in a deployed projector via a materialized operator of record. This is the first data-consistent, uncertainty-calibrated learned reconstruction for limited-angle DBT. The solver naturally relaxes to discrepancy-ball and maximum-a-posteriori modes for noisy measurements.
[CV-106] Real-time fall detection based on vision for low-power edge platforms
链接: https://arxiv.org/abs/2607.12909
作者: Wenjun Xia,Zhicheng Peng,Haopeng Li,Zhengdi Zhang
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static pose classification or discrete temporal pattern matching, fundamentally overlooking the instability dynamics of the human support system. This paper proposes a physics-informed falling detection framework that recasts falling as a stability-loss event in a coupled dynamical system. We introduce a novel dual-LTC architecture comprising a Center-of-Mass (CoM) subsystem and a Base-of-Support (BoS) subsystem, both instantiated as Liquid Time-Constant (LTC) neural networks to continuously model inertial trajectory evolution and ground-contact adjustment through adaptive time constants, Physical interpretability of falling motion. A learnable coupling module emulates physical interaction between the two subsystems, while a Stability Manifold classifier operates in the joint latent space to detect boundary crossing via Lyapunov-inspired stability metrics. Complementary counterfactual trajectory projection and Time-to-Collision (TTC) estimation further enable irreversibility assessment and early warning. The architecture is designed to support a three-state prediction paradigm (Normal, Falling, Fallen); in this preliminary study, we validate the core stability discrimination capability on a two-class dataset (Normal vs. Falling), leaving the full three-state temporal transition to future work. Unlike conventional CNN–RNN pipelines, the proposed formulation encodes continuous-time mechanical inertia, yielding a sub-50K-parameter network capable of real-time inference on resource-constrained edge devices. Extensive experiments demonstrate competitive accuracy with superior physical interpretability, validating its efficacy for low-compute visual fall detection.
[CV-107] Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function
链接: https://arxiv.org/abs/2607.12586
作者: Xiao-qiang Zhai,Zhi-feng Pang,Peng Zheng,Ze-wen Li,Yan-zhe Hou
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 4 figures. Keywords: medical image segmentation, curvature regularization, loss function, active contour model, mean curvature, deep learning. Under review at Biomedical Signal Processing and Control
Abstract:Medical image segmentation is a crucial task in the field of clinical analysis and applications. Though deep learning techniques recently play a crucial role in several scenarios, the training at the individual pixel level leads to a lack of geometric prior information. Scholars proposed to integrate the Chan-Vese model into the loss function for training which can take into account the region and length of the region inside and outside the segmentation process and then improve the performance in medical image segmentation. However, these methods still lack an effective characterization of the segmented region. To overcome this problem, we introduce the mean curvature as a geometric natural constraint and propose a Deep Active Contour and Mean Curvature (DACMC) loss function where the convolution kernel is used to approximate the mean curvature to save computational cost. We have validated the performance of our method on the liver and spleen dataset. Our proposed method demonstrates new state-of-the-art performance on several segmentation datasets.
[CV-108] Uncertainty-Aware Multi-Source Retinal Fluid Segmentation in OCT
链接: https://arxiv.org/abs/2607.12212
作者: Animesh Kumar
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 2 figures, 5 tables. Code, model weights, and REST inference API are available on GitHub and Zenodo
Abstract:Measuring retinal fluid from optical coherence tomography (OCT) drives treatment decisions in macular disease, but manual annotation is slow and segmentation models trained on one scanner degrade on another. We present an attention-guided TransUNet that segments three fluid types across four independent OCT sources, combining a domain-adaptive normalisation scheme with an uncertainty estimate that flags unreliable pixels. The model reaches a mean fluid Dice of 0.78, and – most usefully for clinicians – its uncertainty is 1.34x higher exactly where expert graders disagree (p10^-4), turning a raw segmentation map into an actionable clinical triage signal.
[CV-109] Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation
链接: https://arxiv.org/abs/2607.12075
作者: Md. Sadibul Hasan Sadib,Md. Mohayminul Mukit,Rahmatul Kabir Rasel Sarker,Tahmid Alam Tamim,Md. Monir Hossain Shimul
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 34 pages, 8 figures, 7 tables, including supplementary material
Abstract:Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabilities, uncertainty estimation, and selective referral, particularly under dataset shift. Methods: We developed a calibrated deterministic five-member deep ensemble for ROI-based thyroid nodule classification and selective image-based triage. TN5000 was used for model development, five-fold cross-validation, member-wise vector-scaling calibration, and fold-specific threshold selection. TN3K served as an independent external dataset-shift evaluation. The framework used ConvNeXt-Tiny with squeeze-and-excitation attention, ensemble-mean malignancy probability, and mutual information (MI) as an ensemble-disagreement score. A three-tier policy assigned images to No-FNA suggestion, FNA recommendation, or radiologist review. Results: On pooled out-of-fold TN5000 predictions, the ensemble achieved AUC-ROC 0.9395, AP 0.9715, ECE 0.0088, and Brier score 0.0813. At 50% nominal MI retention, 7.2% of cases received a No-FNA suggestion, 39.9% an FNA recommendation, and 52.9% radiologist review, with 98.3% No-FNA NPV and 99.83% malignancy capture. On TN3K, AUC-ROC decreased to 0.7870, AP to 0.7254, ECE increased to 0.1899, and Brier score to 0.2281. The frozen TN5000 policy assigned 83.7% to review, 1.0% to No-FNA, and 15.3% to FNA recommendation. No malignant image entered the No-FNA pathway, but FNA-recommendation PPV fell to 76.6%. Conclusion: The framework showed strong internal discrimination and calibration, but limited external threshold transportability. Selective prediction may help identify images unsuitable for automated triage, but local recalibration, threshold validation, and prospective clinical evaluation are required before deployment. Comments: 34 pages, 8 figures, 7 tables, including supplementary material Subjects: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.12075 [eess.IV] (or arXiv:2607.12075v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2607.12075 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sadibul Hasan Sadib [view email] [v1] Mon, 13 Jul 2026 18:50:57 UTC (1,803 KB)
[CV-110] Analyzing Image Encoder Choices and Graph Homophily in GCN Frameworks for Breast Ultrasound Classification MICCAI2026
链接: https://arxiv.org/abs/2607.12054
作者: Sabahattin Mert Daloglu,Ceren Coskun,Harvey Castro,Soner Hacihaliloglu,Ilker Hacihaliloglu
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Submitted to the MICCAI 2026 ASMUS Workshop (under review)
Abstract:Breast ultrasound is widely used for screening, yet automated analysis remains challenging due to speckle noise, acquisition variability, and weak separation of benign and malignant cases in standard ultrasound imaging. Graph convolutional networks (GCNs) have recently emerged as a promising approach by leveraging relationships among similar patient samples. However, it remains unclear how the choice of image encoder influences graph construction and downstream classification performance. In this work, we systematically evaluate five image encoders spanning convolutional and transformer-based architectures for GCN-based breast ultrasound classification. Image embeddings are used to construct cosine similarity k-nearest-neighbor graphs, which are classified using a single-layer GCN with a linear classification head. Across three patientwise cross-validation folds, higher-capacity encoders consistently improve graph homophily and downstream classification performance, yielding gains in accuracy, AUC, sensitivity, specificity, and F1-score. Moreover, test-set graph homophily exhibits a strong linear correlation with classification accuracy, with higher-capacity encoders consistently occupying the high-homophily, high-accuracy region suggesting that encoder-driven improvements in graph structure are a key mechanism underlying the observed performance gains. These findings establish encoder selection as a critical factor in graph-based breast ultrasound classification and identify graph homophily as a key indicator linking representation quality to downstream classification performance.
人工智能
[AI-0] rraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
链接: https://arxiv.org/abs/2607.13028
作者: Zhouchonghao Wu,Akshay Rangesh,Weixin Li,Wei-Jer Chang,Zachary Lee,Tim Wang,Wei Zhan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Technical Report from Applied Intuition Research
Abstract:Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains. We present TerraZero, a procedural driving simulator and self-play training stack. A configurable C engine runs simulation on the CPU and policy inference on the GPU over a zero-copy path, sustaining 1.3M agent-steps per second on a single server-grade GPU, far faster than existing object-level simulators, while keeping fidelity lighter single-agent systems omit: heterogeneous agents, multiple dynamics models, and full traffic-rule enforcement. TerraZero treats logged data only as a source of real-world map geometry, populating each map with randomized rule-based road users and signal controllers and randomizing agent dynamics, rewards, and sizes per episode, so a map yields an unbounded set of scenarios. Every reported policy trains from scratch by reinforcement learning alone on a compute-efficient self-play recipe across GPUs, with zero human demonstrations and no fallback planner at inference. Policies generalize zero-shot across cities and datasets, including emergent left-hand-traffic driving without explicit supervision. As an ego policy, TerraZero is the first fully learned policy to top the InterPlan long-tail benchmark, ahead of larger learned planners; on routine-driving val14 it ranks among the best approaches and is the safest, posting the best collision and time-to-collision scores. On Waymo Open Sim Agents realism the same recipe outperforms other demonstration-free methods and is competitive with the strongest reference-anchored self-play method. One stack serves both roles: driving policies across dynamics for cars and trucks, and sim agents that jointly control vehicles, pedestrians, and cyclists.
[AI-1] Audio-Native Speech Recognition with a Frozen Discrete-Diffusion Language Model
链接: https://arxiv.org/abs/2607.13013
作者: Harsha Vardhan Khurdula,Abhinav Kumar Singh,Yoeven D Khemlani,Vineet Agarwal
类目: Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: 10 pages, 2 figures, 6 tables
Abstract:Automatic speech recognition is dominated by autoregressive decoders that emit one token at a time. We ask whether a discrete diffusion language model can transcribe speech instead, refining a whole transcript in parallel over a small number of denoising steps. We train an audio-native interface for DiffusionGemma, a 26B mixture-of-experts model that generates text by uniform, random-token discrete diffusion rather than the absorbing-mask scheme common to recent diffusion language models. A frozen Whisper encoder supplies acoustic features, a lightweight projector maps them into the model embedding space, and low-rank adapters let the frozen backbone attend to the new modality. About 42M parameters are trained, which is 0.16 percent of the backbone. We find that the natural training objectives fail to ground the audio because their gradient reaches the projector only through attention that has already dismissed it. A connectionist temporal classification loss applied through the frozen output head breaks this deadlock. The resulting model reaches 6.6 percent word error rate on LibriSpeech test-clean, transcribes in roughly eight parallel steps regardless of utterance length, and uses a single adapter trained on six languages, which we evaluate here on English, Hindi, and Mandarin. Comments: 10 pages, 2 figures, 6 tables Subjects: Artificial Intelligence (cs.AI); Sound (cs.SD) Cite as: arXiv:2607.13013 [cs.AI] (or arXiv:2607.13013v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.13013 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-2] Dynamic Resource Allocation for Ensemble Determinization MCTS
链接: https://arxiv.org/abs/2607.13007
作者: Jakub Kowalski,Adam Ciężkowski,Artur Krzyżyński,Mark H. M. Winands
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Simulation-based algorithms are especially suited for high-uncertainty environments such as adversarial board games with significant elements of randomness and hidden information. In particular, several Monte Carlo Tree Search (MCTS) variants are commonly used in such domains. In this paper, we propose a series of enhancements for Ensemble Determinization MCTS, introducing two axes for dynamic resource allocation. First, Dynamic Number of Determinizations, increases or decreases the number of currently used determinization trees depending on the behavior of so-far search. Second, Dynamic Simulation Allocation, splits the simulation budget nonuniformly across the determinization trees, using simulation-to-simulation decisions to choose the tree with potentially the best knowledge gain. As benchmark domains, we used three popular tabletop games: Jaipur, Lost Cities, and Splendor. Testing our proposed enhancements in iteration- and time-based settings showed that particular configurations yield a statistically significant increase in the algorithm’s strength.
[AI-3] Win by Silence: Deletion Non-Monotonicity Autonomous Exploitation and Typed-State Gating in LLM Plan Evaluation
链接: https://arxiv.org/abs/2607.12986
作者: Aleh Manchuliantsau
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 10 pages, 5 figures
Abstract:Plan evaluators can reward a strategic plan for becoming less explicit. This paper studies that failure in a staged expected-value scorer for LLM-generated venture routes. Proposition 1 gives the score change from deleting an interior transition while retargeting its predecessor and retaining downstream value: Delta_k = (prod_ik p_i)[c_k + (1 - p_k)R_k+1]. On a frozen 26-route cohort, all 57 admissible deletions matched the analytic identity and threshold sign, and every route had at least one score-improving deletion. A score-seeking optimizer, allowed to restructure routes but not told the exploit mechanism, found baseline-beating uncovered structures in 21/26 routes. GATE refused score release for 26/26 silenced routes with 0/26 honest suspensions; after refusal, 47/54 next revisions repaired to a covered structure, and strict covered improvement rose from 1/26 to 13/26. An adaptive compiler-aware co-author exposed the registry-provenance boundary: obligation-channel evasions remained 6/6 across all four v1/v1.5 conditions, while delta-indexed cost floors reduced beat-honest routes from 6/6 to 3/6 and fundability-by-silence from 5/6 to 0/6 without establishing semantic completeness. If a plan scores better only because it omits necessary work, the plan did not improve; the evaluation created an omission incentive. PCSC detects and neutralizes post-hoc omission splices over model-mediated typed-state records. In the cooperative setting tested, GATE acts as a deterministic search-shaping constraint, not merely a post-hoc filter. It does not verify the semantic completeness or real-world quality of arbitrary LLM-generated strategies.
[AI-4] Resist and Update: Counterfactual Report Coordinates for Incentive-Compatible LLM s
链接: https://arxiv.org/abs/2607.12985
作者: Sen Yang,Yuen-Hei Yeung
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Aligned language models routinely misreport under non-evidential incentive pressure: they agree with a confident user or overstate certainty even when their internal belief is unchanged. We cast this as a failure of internal incentive-compatibility (IC) and present a method for learning and certifying counterfactual report mediators that hold a model’s reports to a causal contract: invariant to forbidden influences (pressure, prestige, restyling) and responsive to licensed ones (genuine evidence). These two demands, resist and update, pull in opposite directions. We study them on a Bayesian-witness benchmark with known posteriors, in which the same user disagreement is licensed evidence or forbidden pressure purely by stated source reliability. We (i) causally identify, by interchange interventions rather than probe accuracy, low-rank report coordinates for answer, confidence, and caveat that are near-orthogonal and independently controllable, and (ii) introduce a training-free counterfactual report-coordinate (CRC) clamp that references the model’s own report under a counterfactually incentive-neutralized context. On the witness benchmark the two-pass clamp attains resist and update of 1.00 jointly (Wilson 95% CI [0.99,1.00]), a causal certificate under a constructible reference, not a deployed solution. Global decoding and steering show a single-parameter tradeoff; output-level fine-tuning matches both objectives only when both are enumerated; resist-only training loses evidence-responsiveness. The deployable single-pass compilation is lossy (0.73/0.97). The mechanism and clamp reproduce across three model families and transfer to a natural sycophancy benchmark (SycophancyEval). Our contribution is the interface and certification method: activation-level counterfactual incentive-invariance as a structural primitive for internal IC.
[AI-5] Form Not Content? A Preregistered Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code Models
链接: https://arxiv.org/abs/2607.12962
作者: Mehmet Iscan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 54 pages, 6 figures, 17 tables. Preregistered, placebo-controlled evaluation
Abstract:Frozen small code LLMs are deployed locally, yet the information guiding a retry after a failed attempt is still measured without placebo controls in the self-repair literature. We treat a failed program as a conjecture and an execution counterexample as an oracle-relative refutation, and introduce PoPE (Popperian Placebo-controlled Evaluation): a methodology for measuring whether evidence that falsifies LLM-generated code can be used operationally by that same model. In PoPE, error content is paired with channel-specific placebos that keep the predeclared scaffold while ablating task-relevant content or deranging the task-error assignment. Frozen small code models (0.5-1.5B) are evaluated under preregistered rules through a prompt channel and a weight channel (small-data adapter training), with four generations per arm-unit pair. In the prompt channel, public-tier screening unlocked 12 units under the content-ablated form placebo versus 10 under the live error-pattern arm on a 40-unit resistant band; the result was recorded as mechanism-null. In the weight channel, an 8-8 tie was observed between the error-content adapter and the intervention-free baseline (p=1.0), while the SHA-deranged placebo adapter stayed ahead with 10 unlocks; content-attributable superiority was not confirmed. These results do not constitute evidence of equivalence or non-inferiority. Equivalence was not tested separately. Findings are restricted to the public-tier screening endpoint; hidden-tier confirmation was deferred by design. We read this not as compiled criticism disappearing as information, but as the loss of its external role in testing a new conjecture: when a representation learned from the oracle is written back into the generation state, testing is replaced by conditioning. No working JEPA-RL controller is claimed. PoPE is presented as a placebo-controlled, retestable measurement standard.
[AI-6] Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
链接: https://arxiv.org/abs/2607.12924
作者: Jonas Ehrhardt,René Heesch,Oliver Niggemann
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters. In such settings Reinforcement Learning algorithms typically determine parameters with one-shot estimators, which makes their training sample inefficient. Though in most PAMDP environments explicit but incomplete knowledge (e.g., rules, safety constraints, or expert heuristics) is available, it is rarely directly used to increase the sample-efficiency of training Reinforcement Learning agents. We step into this gap and propose our novel Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL) algorithm. KGRL uses domain knowledge in a Datalog knowledge base to derive the set of applicable actions and feasible parameters for a given state. This allows it to prune non-applicable actions from the decision-space and constrain the parameter spaces of the remaining actions. We then use a gradient-based parameter refinement loop to estimate the optimal parameters during training and deployment of the agent. By recording activated rules along the trajectory, KGRL additionally provides local procedural explanations on the pruning of actions and constraining of parameters. Overall, KGRL guides the agent’s exploration and deployment toward feasible and constraint-aware decisions, while increasing sample efficiency during training. KGRL outperforms state-of-the-art RL baselines for PAMDPs in both, sample efficiency and episodic return.
[AI-7] UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies
链接: https://arxiv.org/abs/2607.12892
作者: Lirui Zhao,Modi Shi,Li Chen,Qi Liu,Ping Luo,Hongyang Li
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect task completion, making the quality of these signals critical. Since such dense labels are rarely available at scale, normalized time within a demonstration is often used as a scalable substitute: later frames are treated as higher progress. However, this time-derived label is only a noisy proxy for physical task progress. In contact-rich manipulation, a robot may make progress and then lose it through slips, failed grasps, or partial undoing, while the time-derived label continues to increase monotonically. We introduce Unsupervised Robotic Value Correction (UR-VC), an offline, training-free method for correcting time-derived progress labels. UR-VC exploits a simple regularity in demonstration data: similar states often recur across different episodes, but at different timestamps. Instead of trusting the timestamp from a single trajectory, UR-VC retrieves similar states from other episodes and aggregates their time-derived labels to obtain a corrected progress estimate. UR-VC requires no manual progress labels, reward annotations, or additional value model. We evaluate UR-VC on real bimanual cloth flatten-and-fold data, a long-horizon deformable-object manipulation task with visible intermediate progress. The corrected labels capture local regressions and non-uniform progress that normalized time cannot represent, while preserving the overall task trend. We further use the corrected signal to construct advantage labels for VLA training, following recent advantage-conditioned policy learning. UR-VC shows a positive trend in real-robot task success under matched data, model, and training settings.
[AI-8] A Multi-Agent System for Autonomous Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
链接: https://arxiv.org/abs/2607.12886
作者: Cameron Cagan,Pedram Fard,Jiazi Tian,Jingya Cheng,Shawn N. Murphy,Hossein Estiri
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Clinical notes contain many of the signs and symptoms that bring patients to care, yet this information rarely reaches structured fields. Existing extraction approaches either rely on context-insensitive rules that generate false positives or on supervised models that require substantial fine-tuning. We present Pythia, a multi-agent system that autonomously writes and optimizes extraction prompts for clinical concepts without manual prompt engineering or fine-tuning. Running on a locally hosted open-weights model, Pythia keeps clinical notes on local infrastructure and selects prompts using development-set sensitivity and specificity. We compared Pythia with a curated lexicon across 72 signs and symptoms from 400 clinical notes representing 387 patients. Development (n=300) and validation (n=100) sets were partitioned independently for each concept. Pythia achieved mean sensitivity of 0.76 and specificity of 0.95, compared with 0.82 and 0.76 for the lexicon, and matched or exceeded the lexicon on both metrics for 20 of 62 directly comparable concepts. For 14 concepts where the lexicon labeled every note positive, Pythia recovered mean specificity of 0.97 by requiring a present-tense, patient-attributed finding rather than any textual mention of a term. Specificity transferred from development to validation with minimal degradation across prevalences, whereas sensitivity transfer weakened below 5% prevalence, reaching a mean gap of 0.25 below 2% prevalence. A BERT classifier fine-tuned per concept on the same development set achieved mean sensitivity of 0.23 and collapsed to zero sensitivity for concepts below roughly 5% prevalence. These findings suggest that autonomous, fine-tuning-free prompt optimization can produce symptom extraction prompts that generalize effectively from development to validation while remaining deployable on local infrastructure.
[AI-9] Unveiling Complex Collective Behaviors from Simple Rewards IROS2026
链接: https://arxiv.org/abs/2607.12861
作者: Yize Mi,Jianan Li,Liang Li,Shiyu Zhao
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: Accepted by IROS 2026
Abstract:Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives. Unveiling the mechanisms behind this emergence is critical, but the disconnection between simple rewards and collective behaviors exacerbates interpretability challenges. This paper aims to reveal the hidden mechanisms in this process. We propose a two-stage EEC (\LinkIII) explanatory framework. This includes a novel analytical tool called the Agent Response Map (ARM), which reveals agents’ decision-making patterns across space and identifies regions of aggregation and avoidance. ARM reveals that the robots implicitly learn the geometric fields of the environment and utilize these structures as desired targets for coordinated movement. We validate this finding across two distinct tasks: a cooperative multi-robot shape assembly and a competitive predator-prey pursuit-evasion. 1) In the cooperative task, ARM identifies the unoccupied target interior as the desired destination for robot navigation. As the center becomes occupied, this target region automatically shifts toward the boundary, demonstrating the robots’ capacity to autonomously explore unoccupied areas. 2) In the competitive task, ARM surprisingly identifies the boundary of the predators’ Voronoi diagram as the convergence destination for prey agents. Together, these two tasks demonstrate the capability of ARM to discover the hidden geometric structures underlying MARL policies in robot swarms.
[AI-10] ChartGenEval: Corruption-Tested Multi-Dimensional Feedback for Rhythm-Game Chart Generation
链接: https://arxiv.org/abs/2607.12857
作者: Jhen-Ke Lin
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:A generated rhythm-game chart need not reproduce one official note sequence: many note choices can fit the same song and difficulty. Reference-note agreement therefore measures reconstruction, not the full design problem. We introduce ChartGenEval, a six-question evaluation framework with an automatic, corruption-tested core. It leaves note choice open while anchoring timing to the song: the matched official chart supplies only its authored timing map, never target notes. We test each core output with dose-controlled failures rather than assume that a familiar statistic measures chart quality. Across 80 held-out song groups, seven output axes satisfy prespecified sensitivity and invariance criteria in nine nonredundant tests. Complementary stress tests on the 40-song development panel expose two broader lessons. A chart-wide phase estimate recovers injected shifts of 15, 30, and 60 ms while chart-only outputs remain essentially unchanged. Common-pattern rewriting lowers mean language-model perplexity by 37%, and loop collapse raises mean self-similarity by 62%. ChartGenEval therefore reports separate, role-specific signals instead of one proxy or total score. This profile provides automatic feedback for comparing and iterating generators; selected outputs are candidate optimization targets or constraints after task-specific stress testing. Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.12857 [cs.SD] (or arXiv:2607.12857v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2607.12857 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-11] Reproducible Reservoir Computing with Thermally Driven Superparamagnets: Controlling Temperature Sensitivity
链接: https://arxiv.org/abs/2607.12840
作者: Zhengfei Chen,Alex Welbourne,Matthew O.A. Ellis,Dan A. Allwood,Eleni Vasilaki,Thomas J. Hayward
类目: Emerging Technologies (cs.ET); Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 19 pages, 6 figures and 4 tables. supplementary information included in the same PDF
Abstract:Unconventional computing systems must demonstrate robust performance under real-world environmental conditions to enable practical deployments. We have recently proposed superparamagnetic nanodot ensembles driven by strain-induced magnetoelectric coupling as exciting candidates for use as ultra-low energy consumption reservoir computing substrates. However, because their dynamics are governed by thermal activation effects, these systems are intrinsically sensitive to ambient temperature fluctuations, leading to degraded task performance when operated outside the temperature range used during training. In this paper we simulate how temperature variations affect the magnetization dynamics of such superparamagnetic ensembles, and quantify how this affects task performance. We then show how heterogeneous nanodot patterns that incorporate different sizes of nanodots with different characteristic timescales for thermal activation mitigate this problem. Benchmark results on the NARMA-10 task show that introducing optimized heterogeneity stabilizes performance of the reservoirs across a wide range of ambient temperatures (5-35°C), with little loss of ultimate performance. We also characterize the trade-off between performance and temperature stability and show that it can be tuned via reservoir hyperparameters. Our study demonstrates a key step in making these novel devices suitable for real-world deployment.
[AI-12] Solution of the Hempels statistical ambiguity problem and Causal AI
链接: https://arxiv.org/abs/2607.12826
作者: Evgenii Vityaev
类目: Artificial Intelligence (cs.AI)
备注: 14 pages
Abstract:This paper addresses Carl Hempel’s longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions are derived from statistical laws. To avoid such predictions, Carl Hempel proposed the Requirement of Maximal Specificity (RMS) for the statistical laws used in the inference. An analysis of the RMS refinements made by Wesley Salmon, Alberto Coffa, and James Fetzer led to the following definition of maximally specific statistical laws: “the lawlike premises of an adequate explanation must specify all and only those properties whose presence or absence made a difference to the occurrence of its explanandum-phenomenon.” However, there was no proof of a solution to the statistical ambiguity problem based on this definition. We use Nancy Cartwright’s definition of causes that raise probabilities across background contexts, and then introduce the concept of Causal Rules. Then we define a special semantic probabilistic inference procedure that incrementally refines these causal rules by incorporating all statistically relevant information. This procedure yields Maximally Specific Causal Relationships (MSCRs), for which we prove (Theorem 1) that predictions derived from them are consistent. This resolves the statistical ambiguity problem. The semantic probabilistic inference procedure provides a probabilistic causal learning system, which may be used in such new areas as Causal AI and Causal Machine Learning. They fundamentally explore causal inference as a tool for understanding cause-and-effect relationships within complex systems. Properties similar to RMS remain under discussion. Several notions related to RMS are considered: invariant feature learning, invariant causal prediction, and spurious association.
[AI-13] Human-AI Agent Interaction as a Neuroplastic Training Environment
链接: https://arxiv.org/abs/2607.12823
作者: Eranga Bandara,Ross Gore,Asanga Gunaratna,Ravi Mukkamala,Nihal Siriwardanagea,Gihan Siriwardanagea,Sachini Rajapakse,Isurunima Kularathna,Pramoda Karunarathna,Chalani Rajapakse,Sachin Shetty,Christopher K. Rhea,Ng Wee Keong,Kasun De Zoysa,Amin Hass,Shaifali Kaushik,Wathsala Herath,Preston Samuel,Anita H. Clayton,Atmaram Yarlagadd
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Interaction with AI agents has become one of the most frequent activities of everyday digital life. Whether conversing with an assistant, working with a coding copilot, or generating images, the interaction follows a common iterative loop: a request is issued, a result returned, appraised, and the request revised. We observe that this loop is a high-frequency stream of contact events – moments at which a result meets a person and a conditioned response may fire before deliberate appraisal – making everyday agent interaction an unrecognised neuroplastic training environment. When a result disappoints, reactive patterns of impatience, perfectionism, frustration, and self-criticism are repeatedly evoked, and under activity-dependent synaptic plasticity each uninterrupted cycle deepens the underlying pathway through long-term potentiation. Ordinary agent use may thus quietly strengthen the very patterns it provokes. We propose that the same training environment can be engaged to the opposite effect. Treating conditioned reactive patterns as physical neurone paths – activated through a pre-cognitive feeling tone that opens a brief regulatory gap – we develop a framework in which, at that gap, in place of the reactive re-prompt, a person performs behind-the-scenes observation: watching the neural process operate so the cascade does not complete and long-term depression weakens the path rather than potentiation strengthening it. We characterise this practice through three layers of observation and two modes of application: a user-guided mode requiring no change to existing tools, and an agent-assisted mode in which an ordinary agent is lightly configured to support observation at the gap. We illustrate the framework through generative image prompting, showing how a single frustrating session is behaviourally nearly identical whether or not it is observed, yet neurologically opposite.
[AI-14] Visual Access Boundaries in Vision-Language Model Reasoning
链接: https://arxiv.org/abs/2607.12815
作者: Hiroto Osaka,Shohei Taniguchi,Gouki Minegishi,Kai Yamashita,Masahiro Suzuki,Yutaka Matsuo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Chain-of-Thought (CoT) prompting is widely used as a test-time scaling strategy for Vision-Language Models (VLMs), but it remains unclear what is extended when VLMs generate longer reasoning traces. We ask whether CoT requires continued access to image tokens, or whether it mainly operates over visual information already made available earlier in the forward pass. We introduce Visual Access Sweep, a causal intervention that masks attention from generated-token queries to image-token keys along layer depth and generation time, and define the Visual Access Boundary (VAB) as the minimal access region that preserves task accuracy. Across six model configurations from Qwen2.5-VL and InternVL3, both no-CoT direct answering and CoT prompting exhibit finite VABs. In Qwen2.5-VL-32B and InternVL3 at 14B and 38B scales, when CoT is evaluated against the no-CoT full-access target, its VAB layer differs from the no-CoT boundary by at most two layers, despite substantially longer generations. This suggests that CoT does not primarily improve performance by prolonging direct image-token access throughout the reasoning trace, but by extending language-side computation over image-derived hidden-state information. We further show that CoT gains are constrained by perceptual readout. CoT helps when the queried visual attribute can be reliably read out by the model, but not when that readout is unreliable. A symbolic-attribute oracle shows that CoT can improve counting once ground-truth attributes are supplied as text, while a single-object probe-vs-decode check shows that hard attributes can be linearly recoverable from hidden states yet difficult for the model itself to output. Together, these analyses place the bottleneck at readout rather than counting.
[AI-15] PixelLoop: Shortcut Topological Navigation with Pixel-Level Loops IROS
链接: https://arxiv.org/abs/2607.12811
作者: Sarthak Chittawar,Vansh Garg,Aditya Vadali,Krish Pandya,Rohit Jayanti,Sourav Garg,Madhava Krishna
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 8 pages, 5 figures
Abstract:Although topological mapping and navigation have been studied extensively, the specific role and downstream effect of loop closures in purely topological representations has received relatively little attention. Importantly, loop closure over topological maps is distinct from loop closure over globally referenced trajectories and metric maps. Building on recent denser topologies grounded in pixel-level, relative 3D geometry, we propose PixelLoop which introduces loop closures directly in pixel space. Unlike sparse image-level edges or pose-graph corrections in SLAM, our pixel-level closures act as dense topological shortcuts that alter planning connectivity and cost propagation rather than merely aligning coordinates. This dense connectivity enables stable any-point-to-any-point navigation and produces costmaps that align accurately with geometric shortest paths. In particular, we showcase the distinct advantage of applying loop closures to fine-grained pixel topologies rather than image-level topologies. Across extensive simulated experiments, PixelLoop achieves over 35% absolute improvement in both Success Rate and SPL compared to image-relative baselines, with the largest gains in scenarios requiring shortcut exploitation. Results are further validated through real-world mobile robot deployments, demonstrating that dense pixel-level loop closures provide a practical and robust foundation for topological visual navigation. Project Page: this https URL
[AI-16] Autonomous Tracking and Terminal Guidance of Moving Targets for Fixed-Wing UAVs
链接: https://arxiv.org/abs/2607.12801
作者: Wei-Hao Liou,Teng-Hu Cheng
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:This study introduces a unified control framework for fixed-wing unmanned aerial vehicles (UAVs) fitted with a pan-tilt (PT) camera, intended to perform an end-to-end mission spanning from initial target detection to accurate terminal engagement. The proposed system employs a three-phase strategy: a vision-based target acquisition phase, an NMPC-based tracking phase, and a terminal guidance phase. During tracking, the framework uses an Unscented Kalman Filter (UKF) to fuse YOLO-based visual detections with inertial measurements, enabling robust target state estimation under unknown dynamics. To ensure reliable visual contact, we introduce a constraint-aware Nonlinear Model Predictive Control (NMPC) strategy that incorporates Control Barrier Functions (CBFs) to explicitly prevent UAV self-occlusion – a common limitation in fixed-wing tracking. Upon satisfying terminal engagement conditions, the system seamlessly transitions control to a quaternion-based Biased Proportional Navigation Guidance (BPNG) law, enforcing precise impact angle constraints. High-fidelity simulations demonstrate that the framework achieves stable, robust tracking and accurate terminal interception while strictly respecting the vehicle’s dynamic limits and camera field-of-view constraints.
[AI-17] Silent Alarm: A J-Space Protocol for Comparing Danger Recognition Across Models and Quantization Levels
链接: https://arxiv.org/abs/2607.12792
作者: Roman Prosvirnin,Victor Minchenkov,Alexey Soldatov,Vladimir Bashun
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 17 pages, 12 figures
Abstract:Jailbreak-robustness research typically evaluates safety through generated responses using an LLM-as-judge approach. Such evaluations, however, are sensitive to the benchmark’s grading procedure and capture only observed behavior on a given set of attacks, without directly revealing the hidden fragility of the underlying safety mechanisms. This work proposes JADR (Jacobian Assessment of Danger Recognition), a protocol that measures a model’s internal representation through Jacobian space (J-space, a recently proposed workspace of verbalizable concepts) before the first response token is generated. For every prompt and layer we record the top-k J-space tokens; these are grouped into six behavioral scenario axes and compared between a danger sample based on StrongREJECT and a safe control drawn from XSTest and OKTest. The method does not call on an external judge model: the computation runs entirely locally, on the activations of the model under evaluation, which lets us compare both different models against each other and modifications of a single model – quantization and fine-tuning in particular – on the same terms. The final comparison rests on the proposed SafetyAUC metric, complemented with bootstrap confidence intervals. The protocol is applied to six models (Qwen3-1.7B, Qwen3-4B, Qwen3-8B, Qwen3-Uncensored-4B, Qwen3-SafeRL-4B, Gemma 2 9B) across three weight-representation regimes – BF16, INT8, and INT4 – and checked against an independent behavioral evaluation with the StrongREJECT grader. The metric separates models with a strong versus a weak internal safety mechanism with statistical significance and captures substantively different effects across quantization regimes.
[AI-18] Accuracy and Normalized Accuracy under Length Bias: Analysis Guidelines and a Bayesian Alternative ICML2026
链接: https://arxiv.org/abs/2607.12767
作者: Koen Oostermeijer
类目: Artificial Intelligence (cs.AI)
备注: Accepted at ICML 2026
Abstract:Multiple-choice benchmarks that rank candidate completions by conditional log-probability suffer from a length bias: because log-probabilities sum over tokens, longer answers tend to be penalized relative to shorter ones in practice. A common mitigation is to normalize scores by completion length, but we show empirically that this heuristic frequently over-corrects, introducing a bias toward longer answers instead. We first analyze these scoring rules, characterizing when standard and length-normalized accuracy are appropriate and how their length biases depend on the distribution of completion lengths. Motivated by this analysis, we introduce \emphBayesian accuracy, a scoring rule that computes the posterior probability of each candidate under an explicit prior over answer length, thereby removing linear length effects. Bayesian accuracy is a drop-in replacement for likelihood-based multiple-choice evaluation, requires no additional forward passes, and consistently exhibits lower empirical length bias than both standard and length-normalized accuracy across benchmarks and few-shot settings.
[AI-19] Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination
链接: https://arxiv.org/abs/2607.12763
作者: Usman Haider,Karl Mason
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not account for system-level constraints, often leading to unsafe global behavior. In this work, we study constraint-aware aggregation for federated reinforcement learning in distributed energy coordination. We propose aggregation rules that incorporate both local performance and estimated constraint violation into the server-side update. Among these, a simple penalty-based rule, w_i \propto R_i - \alpha V_i , consistently provides the most reliable trade-off between reward and safety, without requiring dual optimization or modifications to local training. \textcolorblackWe evaluate our approach on DairyGridEnv, a benchmark modeling multiple farms coordinating battery storage under stochastic demand and a shared grid capacity constraint, and further assess robustness using real load-driven demand profiles from Finland and the German FIELD dataset. Across multiple seeds, penalty-based aggregation substantially reduces violations while improving reward relative to FedAvg in both synthetic and real load-driven settings. A combined reward-violation scheme exposes a tunable trade-off via \lambda , but is less stable. These results demonstrate that lightweight aggregation strategies can substantially improve empirical safety in federated reinforcement learning while preserving standard communication protocols.
[AI-20] LLM s Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
链接: https://arxiv.org/abs/2607.12733
作者: Julius Steiglechner,Lucas Mahler,Gabriele Lohmann
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) excel at pattern recognition and text generation, but their capacity for abductive inference - inferring latent hypotheses that explain observed behavior - remains poorly understood. Here, we introduce Elenchos (named after the Socratic method of cross-examination), a generative evaluation framework that measures abductive reasoning as a structural inverse problem. Given a reference formal system, such as the lambda-calculus, and a potentially mutated counterpart, agents must determine whether a mutation has occurred and infer the rule modifications responsible for the resulting behavioral differences. Evaluating frontier and mid-tier LLMs reveals a consistent detection-attribution dissociation: models often recognize that a system has been altered but struggle to identify the latent mutations causing the observed discrepancies. Performance degrades substantially under interacting mutations, where models frequently recover only a subset of the underlying mutations. Preliminary evidence also suggests diminishing returns from increased inference-time reasoning, with only modest improvements under larger reasoning budgets, though this finding requires further validation.
[AI-21] Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty
链接: https://arxiv.org/abs/2607.12730
作者: Sarah Al-Shareeda,Gulcihan Ozdemir,Heung Seok Jeon
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 30 pages, 9 figures, EPSR Elsevier Article
Abstract:Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs. Missing features must then be reconstructed, and their errors can propagate through the model. If this input uncertainty is not reflected, prediction intervals may become miscalibrated, affecting demand-response scheduling. Our work examines where uncertainty should be placed once inference inputs are reconstructed. We develop a unified one-day-ahead probabilistic forecasting framework that aligns temporal resolution, reconstructs the unavailable inputs, and derives causal features, and we compare a modular post-hoc residual-quantile scheme with an integrated in-model quantile-learning scheme. The comparison uses three mid-scale Deep Learning (DL) backbones: recurrent, hybrid recurrent, and attention-based Temporal Fusion Transformer (TFT) models, under identical inputs, forecasting horizon, preprocessing rules, and training budgets. Results show that uncertainty placement is backbone-dependent. Integrated quantile learning is most reliable with the TFT, yielding 2.2-3.6% MAPE and 28-83W RMSE on the labeled test window, while producing intervals about 5x narrower than the modular intervals at the closest-to-nominal coverage level. Diebold-Mariano tests support the TFT ranking and the mixed behavior of the recurrent backbones. A reconstruction-sensitivity test shows that reconstructed inputs increase the Quantile Score (QS) by 106% while interval width remains nearly unchanged, indicating that the model does not automatically absorb reconstruction-induced uncertainty. Robustness checks against non-DL baselines and seasonal hold-out weeks support this ranking. Our results expose the limits of post-hoc residual quantiles when inference depends on reconstructed inputs.
[AI-22] Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
链接: https://arxiv.org/abs/2607.12723
作者: Qiyuan Fan,Zhi Li,Junjie Li,XiaoFeng Wang,Bin Yuan,Deqing Zou
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities. These vulnerabilities stem from insecure host-container interactions and have become increasingly pervasive as cloud systems mount shared resources, such as GPUs and agent workspaces, into containers to support AI workloads. Existing defenses remain inadequate. Kernel-level protections are intrusive, can destabilize system calls, and have therefore not been accepted into the Linux mainline. Detection methods rely on static rule matching or manual code auditing. Static rules can flag path-related functions but fail to capture the semantics needed to determine whether a host-container interaction exists, causing many false positives. Manual review requires domain expertise, making it costly, inefficient, and difficult to scale. To address this threat, we present Bulkhead, an automated framework that integrates large language models (LLMs) with formal methods for semantic vulnerability discovery and remediation. Bulkhead uses a multi-agent system to identify and repair PaTra vulnerabilities through multi-dimensional knowledge patterns generalized from known cases. It first applies high-risk functional patterns to locate entry points for cross-boundary interactions in containerized code, then uses call-chain patterns to recover the corresponding execution paths at suitable depth. The Detection pipeline analyzes these call chains against the application scenarios and threat model, identifying vulnerabilities such as missing security checks and TOCTOU flaws in cross-boundary interactions, and generating proof-of-concept (PoC) exploits for validation. These PoCs then guide patch generation. To ensure remediation correctness, the Patch pipeline performs assertion-driven verification using predefined model-checking templates. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2607.12723 [cs.CR] (or arXiv:2607.12723v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.12723 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-23] Line-Anchored Feedback Cuts Token Costs and Improves Correctness in AI Code Editing
链接: https://arxiv.org/abs/2607.12713
作者: William Franz Lamberti
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 13 pages, 10 figures, 12 tables. Code, task bank, and raw model outputs: this https URL
Abstract:Generated tokens are a direct driver of the cost, latency, and energy of generative AI (GAI) code editing. We show the format of feedback is a lever on all three. We compare two deliveries of the same requested changes: a holistic prompt (control) versus the structured, line-anchored export of FileMark (treatment). FileMark is a VSCodium extension for inline comments on any file. In a paired experiment line anchoring cut generated tokens by 22% (Claude Opus) and 58% (Claude Sonnet), reaching 24%-80% on files of 100 lines or more, with four of seven models generating significantly fewer tokens after multiple-testing correction. Correctness rose where models had headroom: +2.0 points pooled and +5 to +7 points for three of five local models. An exploratory experiment in which the harness, not the GAI model, applies function-level patches shows the correctness benefit grows further when the edit-application burden is lifted: local-model correctness on 100+ line files roughly triples under anchoring. Line-anchored feedback reduces what stronger models spend and improves what weaker models get right.
[AI-24] MaxSAT-Based Feedback for Guiding Vision-Language Models in Sudoku
链接: https://arxiv.org/abs/2607.12711
作者: Pedro Orvalho,Guillem Alenyà,Felip Manyà
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注: Accepted at the 25th EPIA Conference on Artificial Intelligence, EPIA 2026. 16 pages, 1 figure, and 1 table
Abstract:Vision–Language Models (VLMs) have recently demonstrated promising performance on structured visual reasoning tasks, including grid-based puzzles. However, despite strong perceptual capabilities, these models lack explicit mechanisms for enforcing logical consistency and frequently generate assignments that violate underlying constraints. In this paper, we propose a neuro-symbolic approach that integrates formal constraint reasoning into the VLM solving process via a Maximum Satisfiability (MaxSAT) oracle. Rather than computing solutions directly, the symbolic component acts as a consistency validator and refinement engine. Candidate placements generated by the VLM are encoded as soft clauses in a partial MaxSAT formulation, while Sudoku constraints remain hard clauses. When inconsistencies arise, the MaxSAT solver identifies a largest mutually consistent subset of assignments, which is then translated into structured textual and visual feedback to guide subsequent refinements. We evaluate our approach on a Sudoku dataset across multiple open-source and closed-access VLMs. Results show that MaxSAT-based feedback improves logical consistency and increases the number of solved instances, particularly in full-board refinement mode. These findings demonstrate that symbolic optimisation can enhance the reliability of vision-language reasoning.
[AI-25] Jetson-PI: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference
链接: https://arxiv.org/abs/2607.12659
作者: Zebin Yang,Qi Wang,Yunhe Wang,Xiurui Guo,Bo Yu,Shaoshan Liu,Jiafeng Xu,Hao Dong,Meng Li
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 16 pages, 10 figures
Abstract:Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks. However, deploying VLA models on low-power onboard devices, such as the Jetson Orin, remains challenging due to their high computational complexity, which leads to substantial inference latency and low control frequency. Asynchronous inference can partially mask this latency by parallelizing action execution and subsequent inference, but it introduces two critical issues: perception-execution misalignment and long reaction time. In this paper, we propose Jetson-PI, a method for efficient VLA deployment on onboard devices via Foresight-Aligned Asynchronous Correction. To address misalignment, we train a lightweight future correction module that predicts future environment representation conditioned on committed actions, enabling the action expert to directly predict actions from the future time step. To reduce reaction time, we introduce confidence-based scheduling optimization that adaptively balances VLM and action expert invocations, complemented by system-level accelerations including CUDA graph reuse, GPU-resident intermediate buffering, and flow unrolling. Extensive experiments demonstrate that Jetson-PI achieves 8.66x and 5.41x improvements in control frequency compared with naive PyTorch and this http URL on NVIDIA Jetson Orin, while outperforming VLASH by 14.8% in average success rate on the LIBERO benchmark. The code of our asynchronous algorithm is available on this https URL, and our efficient this http URL-based inference engine is available on this https URL.
[AI-26] Evidence-Grounded Verified Agent ic Reasoning : A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs ICML2026
链接: https://arxiv.org/abs/2607.12650
作者: Junyu Ren
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Software Engineering (cs.SE)
备注: Accepted at the ICML 2026 TAIGR workshop. System name: EG-VAR
Abstract:Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions need not hold up under formal scrutiny. We present EG-VAR (Evidence-Grounded Verified Agentic Reasoning), a Lean 4-based tool-calling architecture in which the Lean kernel is the sole minter of Verified claims via tool-attestation axioms and declared source lifts. Every verified output structurally descends from an attested tool call (Thm. 3.1) and a kernel-checked chain of valid inference (Thm. 3.2); residual outputs are honest Abstain with a replayable audit trail. On a subcollection of TableBench numerical reasoning (n=120), EG-VAR attains 120/120 versus a 95% same-tool baseline; on counterfactual stress tests (5 domains x 2 models), EG-VAR stays 100% source-faithful while same-tool drops to 80-90% (no-tool 50-80%). With the LLM as deployment-time formalizer, residual semantic-formalization error is 3.3% on Sonnet and 1.7% on Opus. We position EG-VAR as a technical-governance interface for high-stakes empirical claims: a formal sidecar makes the target proposition, source scope, evidence boundary, proof obligation, and abstention condition auditable, eliminating unsupported Verified outputs today while turning formalization errors, lift and source-authority disputes, ambiguities, and abstentions into explicit audit targets. Over time, typed sidecars in datasets, APIs, public records, and AI-generated documents can amortize this formalization burden into reusable infrastructure.
[AI-27] Atomic Units of X: The Compression Layer of Intelligence
链接: https://arxiv.org/abs/2607.12634
作者: Sachin Dev Duggal,Pradyumna Swarnalatha Ramanna,Alexandros Vassiliades
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This paper proposes a theoretical framework for understanding intelligence as a process of atomic compression and compositional reuse. We argue that cognitive, biological, computational, and organizational systems achieve scalable intelligence by decomposing complex phenomena into reusable atomic units that can be recombined into higher-order structures. Drawing on evidence from cognitive science, information theory, evolutionary biology, software engineering, medicine, legal reasoning, education, music, and artificial intelligence, the paper develops the concept of atomic units as fundamental compression layers that support efficiency, transfer, interpretability, and evolvability. The central contribution is the Compression Calculus, a formal framework for comparing surface-level representations with atomic representations and for describing how compression gains compound across abstraction layers. We introduce the Compounding Cascade thesis, according to which each additional layer of abstraction multiplicatively increases representational efficiency rather than merely adding incremental savings. The paper further argues that contemporary AI systems often operate at suboptimal levels of representation, relying on token-level processing or document-level retrieval rather than stable, concept-level atomic structures. In this view, large language models are best understood not as complete knowledge architectures, but as dynamic fusion engines capable of navigating, sequencing, and recombining atomic units. The framework provides a foundation for designing self-evolving knowledge systems that can discover, refine, and compose new primitives over time. By reframing intelligence as compression through compositional abstraction, the paper offers a unifying perspective on expertise, knowledge representation, explainable AI, and the future architecture of adaptive intelligent systems.
[AI-28] Agent ic Service-Oriented Computing: A Manifesto for the Next Frontier of Service-Oriented Computing
链接: https://arxiv.org/abs/2607.12619
作者: Amin Beheshti,Rong N. Chang,Boualem Benatallah,Fabio Casati,Schahram Dustdar,Geoffrey Fox,Quan Z. Sheng,Yan Wang,Jian Yang,Albert Zomaya
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: Accepted at the 2026 IEEE International Conference on Web Services (ICWS); Corresponding author: Prof. Amin Beheshti; DOI https://doi.org/10.1109/ICWS72778.2026.00163
Abstract:The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goal-directed, adaptive, and tool-using computational actors. As these agents move from isolated cognitive prototypes into complex distributed workflows, they confront challenges that the Service-Oriented Computing community has studied for more than two decades: composition, interoperability, quality of service, lifecycle management, governance, security, and trust. Yet much of today’s agentic AI ecosystem is developing these foundations ad hoc, without the engineering rigour required for dependable enterprise and societal deployment. This paper introduces Agentic Service-Oriented Computing (ASOC) as a new research and practice area concerned with engineering agents as services, orchestrating services through autonomous and semi-autonomous agents, and governing ecosystems of agents and services under constraints of trust, cybersecurity, compliance, performance, and accountability. We articulate six foundational principles of ASOC (harness-ability, composability, lifecycle engineering, trustworthiness by design, goal-driven orchestration, and observability/accountability) and organise a five-dimensional research agenda spanning: (i) agentic services foundations and lifecycle engineering; (ii) composition, orchestration, and interoperability; (iii) governance, observability, and accountability; (iv) security, trust, and risk management; and (v) evaluation, certification, and Agentic QoS. We argue that the Services Computing community is especially well positioned to provide the conceptual and engineering spine for this emerging field, transforming agentic AI from fragmented demonstrations into dependable, service-based systems worthy of human and organisational trust.
[AI-29] Multi-Perspective Agent ic Program Repair via Code Property Graphs and Temporal Execution Graphs
链接: https://arxiv.org/abs/2607.12605
作者: Zhili Huang,Ling Xu,Hongyu Zhang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures, 10 tables
Abstract:Large language models (LLMs) have improved automated program repair (APR), but two limitations remain. First, raw execution traces are often too large and repetitive to serve as effective model context. Second, repeated patch sampling may produce different implementations without yielding distinct root-cause hypotheses or repair strategies. We present CT-Repair, an agentic APR framework representing static and dynamic evidence as queryable Code Property Graph (CPG) and Temporal Execution Graph (TEG). CT-Repair applies a three-stage filtering pipeline to construct compact TEGs. Three finite-state-machine-guided agents analyze each bug from static, dynamic, and hybrid perspectives and independently produce evidence-grounded repair strategies. A strategy-guided generation procedure instantiates these strategies as candidate patches and uses validation feedback to refine the most promising strategy. We evaluate CT-Repair on 854 Java bugs from Defects4J v3.0. In the mixed-model configuration, CT-Repair correctly repairs 489 bugs. Under a controlled GPT-5.4-mini configuration, it repairs 388 bugs, 19 and 30 more than ReinFix and RepairAgent, respectively. The union of the three evidence perspectives repairs 99 more bugs than the strongest individual perspective. The filtering pipeline also compacts runtime evidence, with execution filtering narrowing the candidate method scope by 94.85% on average and behavior filtering further reducing retained runtime records by 55.97%. These results show that structured runtime evidence and multi-perspective reasoning can improve repair effectiveness without relying solely on a larger patch-generation budget. Comments: 12 pages, 5 figures, 10 tables Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) ACMclasses: D.2.5; D.2.7 Cite as: arXiv:2607.12605 [cs.SE] (or arXiv:2607.12605v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.12605 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-30] Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring
链接: https://arxiv.org/abs/2607.12588
作者: Anna Gatzioura,Vrettos Moulos,Nina Baranowska
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:According to the recent European legislation, high-risk AI systems will have to adapt in order to comply with requirements related to specific areas, like risk management, data quality and governance, logging and traceability, technical documentation, transparency, human oversight, and accuracy, as outlined in the European Artificial Intelligence (AI) Act. As the standardisation process for AI is expected to remain iterative and, so far, there are no European standards on AI fully covering the challenges of algorithmic hiring, we propose specific standardisation-oriented recommendations related to the relevant AI areas specified by the European Commission. For each of these areas, we set the context by describing the requirements that AI systems in high-risk domains, and especially in recruitment, should fulfil, as well as the activities that should be carried out to ensure their appropriate use and desired performance, in line with the requirements deriving from the AI Act. Unlike existing horizontal approaches to AI governance and standardisation, this paper contributes a vertical, domain-specific framework for algorithmic hiring, and especially ranking-based recruitment systems, by mapping the requirements of the AI Act to concrete standardisation recommendations, focusing on lifecycle discrimination risks, fairness-aware data governance, explainability, human oversight, and post-deployment monitoring in recruitment systems. Even though our recommendations were informed by the outcomes of the European project FINDHR, they are not tied to the project’s technical artefacts and could be implemented using alternative methods, tools, or governance mechanisms.
[AI-31] Explainable-by-Design Audio Deepfake Detection via Wiener-Hopf Linear Prediction
链接: https://arxiv.org/abs/2607.12584
作者: Mattia Tamiazzo,Simone Milani,Massimo Iuliani,Marco Fontani
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multimedia (cs.MM)
备注: Accepted at ACM IHMMSec 2026
Abstract:The rapid advancement of synthetic speech generation methods has made audio deepfake detection a critical challenge in multimedia forensics. While recent approaches achieve high detection accuracy, they typically rely on black-box architectures that offer limited interpretability and high computational complexity. In this paper, we propose an explainable-by-design audio deepfake detection framework based on Wiener-Hopf linear prediction, processed by a lightweight 2D Convolutional Neural Network (CNN). This design enables a direct and transparent connection between classification outcomes and the acoustic properties of the signal. Experimental results on benchmark datasets demonstrate competitive detection performance while maintaining significantly lower computational complexity compared to state-of-the-art solutions. The interpretability analysis using Grad-CAM reveals that the classifier focuses on low-order predictor coefficients and on silence and transitional regions, suggesting that the Wiener-Hopf predictor captures reverberation characteristics and subtle statistical inconsistencies in synthetic speech. Finally, robustness experiments show that fine-tuning effectively recovers detection performance under common post-processing degradations, including additive noise, MP3 compression, and telephone filtering.
[AI-32] Deep Learning-based Surrogate Modelling of the LOD Method for Multiscale Problems
链接: https://arxiv.org/abs/2607.12570
作者: Marc Haltmayer,Jaemin Seo,Yuseung Lee,Sungyeop Lee,Jaehoon Jeong,Jae Yong Lee
类目: Numerical Analysis (math.NA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 56 pages, 4 figures
Abstract:Multiscale problems are notoriously difficult to tackle using traditional numerical methods, as accurately resolving fine-scale features often requires prohibitively fine discretizations. This challenge is particularly pronounced in applications such as materials science, fluid dynamics, climate systems, chemical processes, and complex networks. Recent neural operator models provide a promising data-driven alternative, but frequently struggle to achieve sufficient accuracy in the presence of strongly heterogeneous or oscillatory coefficients. In this work, we focus on the solution of elliptic PDEs with rough and high-contrast inputs. The Localized Orthogonal Decomposition (LOD) method is a well-established numerical approach for such problems, but it comes, however, at a substantial computational cost. We investigate the performance of popular neural operator architectures on these challenging multiscale problems and identify key limitations in their ability to resolve fine-scale structure. To overcome these challenges, we introduce LOD-MSNO (LOD-Multiscale Neural Operator), a hybrid approach that leverages the LOD method as a strong multiscale prior by building on its representation of the solution as a linear combination of problem-adapted basis functions, while addressing its main computational bottlenecks through data-driven operator learning. We further provide theoretical error estimates for the proposed coefficient-learning framework. Lastly, we demonstrate the potential of our proposed method to outperform current neural operator baselines in terms of accuracy for challenging multiscale inputs, while mainly retaining the computational efficiency of neural operator models.
[AI-33] Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel
链接: https://arxiv.org/abs/2607.12547
作者: Niccolò Caselli,Salvatore Lo Sardo,Francesco Massafra,Ippokratis Pantelidis,Samuele Punzo,Sathya Kamesh Bhethanabhotla
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at WM@Booth 2026
Abstract:We investigate whether temporal hierarchy can improve LeWorldModel on long-horizon goal-conditioned control. We introduce Hi-LeWM, an extension that freezes the pretrained low-level LeWM and adds high-level planning over latent subgoals. We evaluate Hi-LeWM on PushT and Cube across increasing goal offsets. Hierarchy does not automatically improve performance: at short horizons, the best configuration uses a one-step high-level horizon, while longer horizons reveal a mismatch between the learned high-level action space and the inference-time search distribution. Experiments with true future latent subgoals show that the frozen low-level controller can execute well-aligned intermediate targets, indicating that high-level subgoal generation is the main bottleneck. Unconstrained search can select latent macro-actions that appear favorable under the learned model but produce poor control targets. Constraining search around macro-actions encoded from training trajectories, with appropriate subgoal execution timing, recovers useful hierarchical regimes, improving over flat LeWM by +11.3 percentage points at medium-range horizons and +14.7 percentage points at the longest PushT horizon. Overall, temporal abstraction can benefit compact frozen LeWM, but only when high-level search remains compatible with the low-level controller
[AI-34] Evidence-Grounded AI for Musculoskeletal Care
链接: https://arxiv.org/abs/2607.12527
作者: Wenjie Li,Yujie Zhang,Fanrui Zhang,Haoran Sun,Renhao Yang,Junjun He,Weiran Huang,Yuanfeng Ji,Chenrun Wang,Kailing Wang,Hongcheng Gao,Kaipeng Zhang,Hanyu Wang,Angela Lin Wang,Xingqi He,Yilin Huang,Shiyi Yao,Lilong Wang,Yankai Jiang,Yirong Chen,Chenglong Ma,Jiyao Liu,Ming Hu,Gen Li,Yidong Xu,Chengyu Zhuang,Jiawei Liu,Yin Zhang,Lequan Yu,Lu Chen,Yinpeng Dong,Lei Liu,Carlos Gutierrez Sanroman,Yu Qiao,Weijie Ma,Xiaosong Wang,Lei Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Musculoskeletal diseases are among the leading causes of disability worldwide and create the greatest global need for rehabilitation. Because recovery, remodelling and degeneration often unfold over months to years, musculoskeletal care requires longitudinal management that repeatedly integrates evolving patient evidence, external medical knowledge and stage-specific functional goals. In routine practice, this evidence is fragmented across visits, departments and hospital systems, limiting individualized, evidence-based care. Here we report OrthoPilot, a clinical artificial intelligence system powered by a large language model that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal management. OrthoPilot autonomously retrieves real-time imaging, laboratory, pathology and order data and converts evolving patient states into evidence-based decisions from admission diagnosis to rehabilitation planning. We established a specialist-validated benchmark from real-world electronic health records spanning 1,000 disease codes. In a reader study across the complete care pathway, OrthoPilot was compared with 81 orthopaedic physicians and surpassed experts with 25 years of experience in diagnostic reasoning, clinical decision-making and management planning. It also outperformed all evaluated intelligent systems across 60 external clinical centres. In a prospective study of 1,870 complex cases, OrthoPilot increased full-chain management success by 10.6%. During an 8-month randomised deployment involving 8,240 inpatients, it increased cumulative cases per bed by 9.7% and improved patient-reported access to health information. These results move clinical AI from predicting isolated events toward executing longitudinal management across complete musculoskeletal care pathways.
[AI-35] OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
链接: https://arxiv.org/abs/2607.12523
作者: Emil Mittag,Richard Dazeley,Peter Vamplew
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 21 pages, 2 figures
Abstract:Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts. The detection of out-of-distribution conditions is pivotal to determining when an agent’s observations, transitions, or trajectory dynamics deviate from the assumptions underpinning its policy training. Current out-of-distribution (OOD) detection benchmarks typically evaluate image classifiers or static low-dimensional datasets, failing to account for the complex, action-dependent temporal structure inherent in RL trajectories. To address this gap, we present OOD-RL-Bench, a comprehensive and extensible framework designed to evaluate OOD detectors against categories of anomalies injected into RL trajectories. Detectors and anomaly injectors are integrated through shared interfaces and configuration, which allows new scoring methods and perturbation families to be evaluated without modification of the core benchmark loop. We evaluate the utility of the framework using a Deep Q-Network policy within the LunarLander-v3 environment. We assess the performance of each detector across a suite of anomaly types using matched-time AUROC, matched-time AUPRC, matched-time false-positive rate, detection delay, and segmented-onset metrics. Our analysis reveals significant performance variance across anomaly types: observation perturbations and regime switches are identified with high accuracy by several methods, while observation delay and action-conditioned dynamics remain difficult even when post-onset anomaly scores are compared against clean scores from the same timesteps. We make the framework, trained policy checkpoint, and complete results publicly available as a reproducible artefact.
[AI-36] he Model Knows Your Project Not You: Measuring Recognition in LLM s with NameRank
链接: https://arxiv.org/abs/2607.12520
作者: Bojie Li,Noah Shi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:What a frontier model recalls about a person or tool from its own weights – before any retrieval step – often shapes the first description a human sees, making that parametric corpus presence a measurement problem. Citations explain about a third of whether a model recognizes a researcher; we target the residual and build NameRank, a [0,1] recognition score: each of 4,685 entities in 54 cohorts is probed with one open-ended question across 36 models, and an independent judge returns a binary verdict against a curated gold – did the model state a specific, non-guessable fact about this exact entity? – so hallucination, context echo, and guesses earn nothing. Synthetic-null entities hold the floor near zero, and verdicts track the entity, not the model. One thesis organizes the findings: recognition is paid to named, indexable artifacts, not to credentials or titles. Every Olympic-style credential sits below a working-researcher baseline, because no named artifact ships with the medal, yet the ranking inverts at the marquee tier, where Nobel, Turing, and Fields laureates saturate the panel. For independent creators the tool out-ranks its maker, and the credential that does propagate is a named method or awarded paper. Being one of many named contributors to a celebrated artifact, by contrast, earns almost nothing – the authors listed on a flagship model report or system card sit near the recognition floor – because recognition attaches to the artifact’s own distinctive name, not to the roster behind it. No bibliometric predicts recognition well; top-density institutions out-recognize peers at matched citations; and on 258 news events recognition loads on peak salience, not persistence. A self-report probe shows introspection reads a corpus prior, not its own knowledge.
[AI-37] RACE: An Operational Reasoning Schema for Auditable Agent ic Commitments
链接: https://arxiv.org/abs/2607.12480
作者: Edward Y. Chang,Emily J. Chang
类目: Artificial Intelligence (cs.AI)
备注: 46 pages, 18 tables, 4 figures
Abstract:This paper defines TRACE (Typed Reasoning And Commitment Evidence): a typed, versioned schema for recording reasoning traces, a reference procedure for writing records against it, and one operating discipline, no durable state change without a record. The paper argues in three layers that reasoning is not in the language model: the autoregressive mechanism natively computes association; chain-of-thought and reinforcement learning inherit its limits; and the formal constructs of reasoning theory, from Socratic procedure to Pearl’s ladder, are absent as machinery. The schema answers the absence with fields and tests: the TraceRecord and its causal specialization, an eight-stage reference writer, a gate-first measurement regime, the TRACE-Bench protocol, and the consumers, memory admission, plan gating, temporal regret, and verdict reuse, whose more auditable decisions are the measure of the record. A record-consumer contract states what a record guarantees and what a consumer must honor in return, making the schema an operational interface rather than a passive document. Two worked examples run in the main text: a music-lessons argument traced from sentence to typed verdict, separating association, intervention, and prescription; and a flood search-and-rescue vignette in which a predictive world model reports confident plan success that its own support and out-of-distribution scores contradict, so the record defers the commitment, requests a bounded observation, revises append-only, and clears a different branch. The vignette is illustrative, not empirical; closed-loop evaluation is left to future work, so the contribution is the schema and its contract, not a performance claim. Appendices carry the full schema, writer algorithms and cost model, clinical and policy illustrations, the benchmark protocol, convergence metrics, and usage scenarios.
[AI-38] From Observation to Insight: Mechanistic World Models and the Quest for Autonomous Discovery
链接: https://arxiv.org/abs/2607.12474
作者: Ingmar Posner,Anson Lei,Bernhard Schölkopf
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in foundation models have transformed AI for Science, enabling remarkably accurate predictive performance across domains ranging from protein folding to weather forecasting. Yet prediction alone does not constitute scientific discovery. Scientific understanding depends on uncovering the reusable explanatory mechanisms that generate observations, whereas contemporary machine learning remains fundamentally organised around predictive mappings rather than explanatory structure. In this paper, we argue that scientific discovery is fundamentally a problem of knowledge organisation. To this end, we introduce Mechanistic World Models, a new design paradigm that places reusable mechanisms at the centre of representation, computation and learning. Drawing on insights from the philosophy of science, we derive the computational capabilities required for discovery, identify the design principles and inductive pressures that encourage explanatory knowledge to emerge, and formalise the anatomy of a mechanism-centric world model. Finally, we show how diverse research directions including mechanistic interpretability, causal representation learning, equation discovery and modular architectures capture complementary ingredients of this paradigm while lacking a unified framework. We propose Mechanistic World Models as a conceptual foundation and computational blueprint for moving AI beyond predictive forecasting towards autonomous scientific discovery.
[AI-39] Agent -Safety Evaluations as Load-Bearing Evidence: A Vendor-Neutral Cross-Harness Reconstructability Metric
链接: https://arxiv.org/abs/2607.12469
作者: Oleg Solozobov
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 36 pages, 3 tables. Reproducibility package (scorer, fixtures, Evidence Sufficiency Cards): this https URL
Abstract:Many agent-safety evaluation results are not yet load-bearing evidence: identical nominal outcomes (task success, attack success, monitor scores) may sit atop materially different evidence regimes. No vendor-neutral, runnable instrument scores reconstructability as an evaluation-validity metric: whether captured evidence can reconstruct the decision a claim depends on. This paper introduces a property-level reconstructability metric over eight decision-property classes and a cross-harness adapter emitting per-decision Evidence Sufficiency Cards backing a per-run monitor-coverage release check. It specifies a counterfactual-replay intervention protocol, implements its replayability-precondition probe, and defines a claim-evidence overclaim gap. On public and bundled traces, without new model runs, twelve-field sufficiency spans 0.458-0.833 across four inputs sharing a surface reading; replay preconditions are unmet in every scored trace. In a synthetic release-gate pair, the sufficiency gate blocks the raw variant (0.542) and passes the instrumented (0.667). Safety-evaluation claims should travel with their reconstructability vector; a reproducibility package regenerates every reported number.
[AI-40] An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge INTERSPEECH2026
链接: https://arxiv.org/abs/2607.12468
作者: Shuming Fang,Shuifei Zeng
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Accepted to INTERSPEECH 2026. 4 pages + references. Technical description of our 2nd MLC-SLM Challenge Task 1 submission
Abstract:We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (WavLM-Large) segmentation, CAM++ embedding-based two-speaker clustering, and a LoRA-adapted omniASR LLM 7B v2 recognizer, with no oracle segmentation or speaker labels at test time. On the official Development set (150 conversations, 21 language/accent categories) the system attains a macro tcpMER of 29.27%, versus 79.15% for the official baseline; on the Evaluation set it scores 50.23%. We also analyze two engineering choices that substantially affect tcpMER. First, embedding-based speaker clustering outperforms an end-to-end-style alternative that assigns speakers from ASR sc turn markers alone. Second, overlap-aware segmentation, although intended to raise diarization recall, increases tcpMER because overlapped speech is transcribed twice.
[AI-41] Do We Really Need Transformers for Global Spatial Information Extraction in Traffic Forecasting?
链接: https://arxiv.org/abs/2607.12462
作者: Qihang Zhang,Siyao Zhang,Letao Kang,Wenzhe Liang,Miao Zhang,Zhao Zhang
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 6 figures. Source code available at this https URL
Abstract:Existing traffic forecasting models commonly focus on extracting spatial dependencies, particularly global spatial information, which characterizes the representations obtained through interactions between each individual node and all nodes across the traffic network. However, the underlying mechanism by which such global information is modeled and extracted remains insufficiently investigated. Whether global information must be extracted by high-degree-of-freedom adaptive attention or can be captured by a simple global aggregation operator remains unclear. For this purpose, we design a controlled ablation framework that replaces only the spatial mixing module to test attention-based global interaction. Across six traffic benchmarks, uniform full-range mixing and standard spatial attention each achieve lower MAE on three datasets, with only a 0.14% difference in mean MAE, while the former reduces node-scale spatial mixing complexity from O(N2) to O(N). Mechanism analysis further decomposes spatial attention into a row-uniform global background and a non-uniform residual. The residual shows dataset-dependent marginal value, suggesting that spatial attention should be justified by stable gains beyond a row-uniform global background. The corresponding source code is publicly available at: this https URL
[AI-42] EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading
链接: https://arxiv.org/abs/2607.12455
作者: Jie Mao,Changlun Li,Xiang Li,Qiqi Duan,Jinhui Yuan,Xiang Liu,Yuyu Luo,Jing Tang,Xiaowen Chu,Nan Tang
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions. Large language models can accelerate this process, but directly relying on them to rewrite trading strategies often introduces hallucinated edits, strategy drift, and backtest overfitting. We propose EVOQUANT, a self-Evolving Verifier-guided framework for strategy Optimization in Quantitative trading. Our method utilizes LLMs to deeply diagnose performance bottlenecks, generates semantically controlled candidate edits, selects the best strategy through a multi-stage verification pipeline, and distills optimization experience into reusable knowledge for continual self-improvement. We evaluate our method using seven representative strategies: four from the A-share market and three from the Crypto market. Experimental results show that our method significantly improves the Sharpe ratio across all tested strategies: the average test Sharpe increases from -0.298 to 0.538, and the best-performing strategy achieves a 199% relative improvement. Ablation studies and stress tests under stricter conditions further validate the effectiveness and robustness of the framework. Overall, this work transforms quantitative strategy optimization from costly manual trial and error into an automated and verifiable iterative paradigm, offering a new path for applying large language models to financial strategy research.
[AI-43] he Computational Basis of Confidence in Large Language Models
链接: https://arxiv.org/abs/2607.12447
作者: Dharshan Kumaran,Viorica Patraucean,Maks Ovsanikov,Petar Veličković,Nathaniel Daw
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reliable confidence – the probability that a model’s own answer is correct – is essential for the trustworthy deployment of language models. Existing work has largely evaluated confidence by how well it predicts correctness and whether it is calibrated, leaving open a more fundamental question: what does the confidence signal itself represent? Answer logits may reflect a latent decision variable sufficient to compute normative confidence, or instead a heuristic preference signal that combines the available evidence in a non-Bayesian manner. We address this using statistical decision confidence (SDC), a normative framework from computational neuroscience. Treating the answer-logit difference (LD) as a candidate readout of the latent decision variable, we test the qualitative signatures predicted by SDC. Across three perceptual discrimination tasks and a memory-based decision task, spanning three multimodal non-reasoning models and one reasoning model, LD satisfied these signatures – including the diagnostic correct/error folded-X pattern – showing that, in these settings, answer logits behave as monotonic readouts of a latent decision variable rather than heuristic preference scores. In complex visual reasoning, LD continued to predict correctness beyond objective task difficulty, but the full geometric signatures of SDC were absent, illustrating the current boundary of the framework when explicit normative process models are unavailable. These results provide a computational account of confidence in multimodal language models, delineate when answer logits behave as readouts of a latent decision variable, and establish SDC as a unifying framework for studying confidence across biological and artificial intelligence.
[AI-44] Accepted Prefixes Are Not All You Need: A Negative Result on PEFT-Based Block-Diffusion Drafting
链接: https://arxiv.org/abs/2607.12422
作者: Abdurrahman Javat,Allan Kazakov
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Speculative decoding accelerates autoregressive language model inference by using a cheap drafter to propose multiple future tokens and a target model to verify them. A common design goal is therefore to improve draft quality while reducing auxiliary parameters and systems overhead. We study a negative result for this direction through PEFT-BD, a same-backbone speculative decoding method in which a LoRA-like adapter acts as a block-diffusion drafter for an autoregressive verifier. PEFT-BD is motivated by several attractive properties: it avoids tokenizer mismatch, avoids loading a separate draft model, adds only a small number of trainable parameters, and uses a BD3LM-style denoising objective to propose a block of tokens in parallel. Despite these advantages, PEFT-BD does not yield a practical speedup in our Qwen3-0.6B experiments. Although the method obtains nontrivial accepted prefixes, profiling shows that each speculative step requires an adapter-enabled full-backbone draft pass followed by an adapter-disabled full-backbone verification pass. Thus, the drafter is parameter-efficient but not compute-efficient. Our results isolate a simple but important condition for successful speculative decoding: the drafter must be substantially cheaper to execute than the verifier. Longer accepted prefixes alone cannot compensate when draft computation remains verifier-scale.
[AI-45] Isolation as a First-Class Principle for LLM -Agent System Safety: Concepts Taxonomy Challenges and Future Directions
链接: https://arxiv.org/abs/2607.12406
作者: Huihao Jing,Wenbin Hu,Shaojin Chen,Haochen Shi,Sirui Zhang,Hanyu Yang,Changxuan Fan,Zhongwei Xie,Hongyu Luo,Wun Yu Chan,Wei Fan,Haoran Li,Yangqiu Song
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The capability of LLM agents to function as the ``brain’’ of a system fundamentally expands the scope of analysis beyond a standalone model. Consequently, safety is no longer only about input–output content alignment. It also concerns system behavior and real-world execution outcomes. However, the current literature is fragmented across attack types, applications, and benchmarks. This makes it hard to explain why failures such as prompt injection, tool misuse, and memory poisoning often share the same structural cause, and how they spread through an agent workflow. In this survey, we treat isolation as a first-class principle for LLM-agent system safety. By isolation, we refer to the separation of user inputs, tool access, execution channels, inter-agent communication, and environment-originated context. We organize the literature with a boundary-centric taxonomy of five boundaries: user-agent, agent-tool, agent-execution, agent-agent, and system-environment. This view helps identify where the loss of isolation first occurs, how compromise propagates across boundaries, and which defenses are most relevant at each interface. We also summarize cross-boundary failure paths, discuss open challenges, and outline a research agenda for isolation-by-construction in future agent systems.
[AI-46] Critic Experience Bank: Self-Evolving Step-Level Confidence Estimation for LLM Agents
链接: https://arxiv.org/abs/2607.12397
作者: Yaopei Zeng,Congchao Wang,JianHang Chen,Nan Wang,Yurui Chang,Lu Lin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents act in external environments where each action changes the state that later decisions condition on, and where a single wrong step can waste interaction budget or trigger irreversible side effects long before the final failure is observed. Reliable deployment therefore requires \emphstep-level confidence estimation: a calibrated probability that each proposed action is productive, available \emphbefore the action is executed. Existing LLM confidence estimators are designed to score a response from the given prompt, but agent confidence also depends on execution consequences: whether similar actions in similar situations actually advanced the task after the environment responded. We introduce the \method (\methodshort), a self-evolving critic framework in which an LLM critic accumulates evidence from its own past judgments and their observed consequences. After each trajectory, a hindsight LLM that sees the full execution feedback votes on whether each step was productive. The resulting pseudo-labels populate a memory bank from which related productive and unproductive experiences are retrieved into the critic’s prompt whenever a similar step recurs. \methodshort requires no training and uses no ground truth step labels. Across three agent benchmarks and three critic backbones, \methodshort attains the best calibration (ECE and Brier) and ranking (AUC) in every dataset–critic combination, reducing ECE by up to 54% relative to the strongest training-free baseline.
[AI-47] PM-Bench: Evaluating Prospective Memory in LLM Agents
链接: https://arxiv.org/abs/2607.12385
作者: Genglin Liu,Saadia Gabriel
类目: Artificial Intelligence (cs.AI)
备注: Published as a conference paper at COLM 2026
Abstract:A significant challenge in agentic AI is prospective memory: the ability to execute an intention at a specific future cue or state while other activities are ongoing. We introduce PM-Bench, a text-based benchmark for measuring prospective memory capabilities in modern LLM agents. Inspired by the Virtual Week paradigm from cognitive science, PM-Bench evaluates how well LLM agents maintain user intentions, execute delayed intentions, and monitor latent environment changes. Over the course of a simulated seven-day week, agents must continue an ongoing activity while deciding whether any deferred task is due. We compare eight state-of-the-art LLMs on PM-Bench under eight different agent configurations. PM-Bench proves challenging across all settings: the best method, a GPT-5.4 agent, reaches only 65.1% F1 score under our evaluation. Furthermore, no single strategy for improving prospective memory dominates across models. We release PM-Bench as a controlled testbed for diagnosing these failures and developing training or inference-time interventions that support reliable prospective behavior.
[AI-48] How Many Tasks Are Enough for Agent Benchmark Decisions? A Replay Analysis of Public LLM Agent Benchmarks KDD2026
链接: https://arxiv.org/abs/2607.12338
作者: Wei-Jung Huang
类目: Artificial Intelligence (cs.AI)
备注: KDD 2026 Workshop Agentic AI Evaluation and Trustworthiness
Abstract:Agent benchmarks often compare two agents after all tasks have run, but costly evaluations make partial runs tempting. A task fraction alone does not show whether a partial run supports the same pairwise conclusion as the completed benchmark. We study this question by replaying completed public task-level records from SWE-bench, AppWorld, and tau-bench. A partial budget counts as enough only when it supports the completed benchmark’s decision, covers required task groups, and leaves no more than a target fraction of comparisons unresolved. The required task fraction varies sharply. At the strict 0 percentage point threshold on a 5 percentage point budget grid, AppWorld first meets all targets at 15 percent, tau-bench at 25 percent, and SWE-bench Verified at 90 percent; SWE-bench Lite does not meet all targets by 95 percent under the primary coverage rule. Partial-evaluation reports should state how much one agent must outperform another, how tasks are selected, what coverage rule is required, what decision rule is used, and how many comparisons may remain unresolved.
[AI-49] A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education
链接: https://arxiv.org/abs/2607.12296
作者: Amrita Ganguly,Aditya Johri,Nora McDonald,Areej Ali,Umama Dewan,Aayushi Hingle Collier
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Under review for SIGCSE conference
Abstract:With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions. In computer science (CS) courses GenAI adoption is especially high and the implications for student learning are significant. At the same time, instructors have also been forced to address the use of GenAI as students have started to use it for a range of functions. Currently, comparative analysis of guidance provided by institutions and its uptake in instruction is lacking. In this paper we bridge this gap by comparing institutional and computing course level guidance to better understand this terrain. We utilize secondary analysis of institutional and course syllabi guidelines from higher education institutions in the U.S. classified as research-intensive. Our findings reveal that although institutional guidance is more pro-use, at the course-level the uptake is still guarded. We discuss the implications and propose an instructor-centered framework to guide future adoption of GenAI.
[AI-50] A Longitudinal Analysis of Public Discourse on AI Ethics in Education Using Twitter Data
链接: https://arxiv.org/abs/2607.12295
作者: Akriti Bagale,Nafisa Mehjabin,Ali Ünlü,Aditya Johri
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Paper under review at Discover Education
Abstract:The rapid integration of artificial intelligence (AI) and generative AI (GenAI) into education presents significant opportunities to enhance teaching and learning, while raising ethical concerns about the responsible use of these technologies in educational settings. Understanding how the public perceives and debates these issues is increasingly important for educators, institutions, and policymakers seeking to integrate AI responsibly and equitably. Social media platforms, where such debates unfold frequently and at scale, offer a valuable lens for capturing large-scale, real-time public reactions to key developments as they emerge. In this study, we analyse five years (2019-2024) of discourse on Twitter (now X) to trace the evolving public conversation around AI ethics in education, paying particular attention to the release of ChatGPT as a pivotal moment that reshaped the nature and tone of that discourse. Using BERT-based topic modelling and SetFit sentiment analysis to identify dominant themes and track sentiment over time, we find that the discourse has been predominantly positive across the observation period, with negative sentiment concentrated around specific ethical controversies. More recently, anxieties about academic integrity and the broader implications of generative AI have come to dominate the conversation. Rather than reflecting a polarized debate, public discourse appears pragmatic and largely receptive to AI integration, though accompanied by growing calls for ethical oversight and institutional accountability. By providing a longitudinal account of public sentiment surrounding AI ethics in education, this study informs educators, institutions, and policymakers an empirically grounded understanding of public expectations, informing the development of responsible, transparent, and equitable approaches to AI integration across educational contexts.
[AI-51] rack Rank Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents
链接: https://arxiv.org/abs/2607.12267
作者: Ning Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Language agents that interleave reasoning and tool use degrade sharply as reasoning chains lengthen, even when each individual step is easy. We trace this to context dilution: an agent’s investigative state (what it has confirmed, what it suspects, and what it still needs) lives only implicitly in a growing context window, where early discoveries are buried under later retrievals. We introduce SLEUTH, which makes this state explicit and actionable through a structured epistemic working memory: the agent maintains Confirmed Facts grounded to sources, Active Hypotheses ranked by evidence, and Open Questions that directly drive its next action. Across five multi-hop benchmarks and five established baselines, SLEUTH’s advantage grows with difficulty, from +5 points on HotpotQA to +11 on 4-hop chains, surpassing Reflexion without multiple episodes. Analyzing where the remaining gap lies, we identify the evidence sufficiency problem: agents often find the answer but fail to commit, exhausting their budget on needless verification. A lightweight commitment trigger fixes this, but only when the agent already maintains structured state: the identical trigger applied to an unstructured agent yields no improvement, isolating organized epistemic state as the necessary condition for effective commitment. Finally, enforcing protocol adherence on a weaker model recovers up to +19 points on the hardest problems, showing that how an agent organizes its reasoning, not raw model capability, is the active ingredient for scaling multi-hop reasoning.
[AI-52] Rethinking the Evaluation of Harness Evolution for Agents
链接: https://arxiv.org/abs/2607.12227
作者: Yike Wang,Huaisheng Zhu,Zhengyu Hu,Yige Yuan,Zhengyu Chen,Shakti Senthil,Hannaneh Hajishirzi,Yulia Tsvetkov,Pradeep Dasigi,Teng Xiao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. First, harness evolution is itself an iterative search procedure that repeatedly evaluates and revises candidate harnesses using task feedback. As in agentic test-time scaling, it should therefore be compared with simple task-level search baselines under matched feedback and inference budgets to determine whether its gains arise from improved harness design or from additional search alone. Second, because the search and the final evaluation share the same benchmark, the reported gains risk overfitting to that specific task set. To address these concerns, we conduct an extensive evaluation comparing harness evolution with simple test-time scaling and discovery baselines under comparable feedback and inference budgets, and also evaluate evolved harnesses on held-out tasks to assess whether the discovered improvements generalize. Experiments on Terminal-Bench 2.1 with GPT-5.4 and Claude Opus 4.6 show that automatic harness evolution does not consistently outperform simple test-time scaling methods and exhibits limited generalization. Our results raise important questions about the effectiveness of automatic harness evolution and highlight the need for fairer evaluation protocols and benchmarks for automatic harness design. Our code is available at this https URL.
[AI-53] Good Benchmarks
链接: https://arxiv.org/abs/2607.12217
作者: Ivan Bercovich
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Good tasks are correct, solvable, verifiable, well-specified, and hard for interesting reasons. The best tasks describe a real problem an experienced practitioner would recognize, in language a practitioner would use, with tests that verify the outcome rather than the approach.
[AI-54] A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models
链接: https://arxiv.org/abs/2607.12200
作者: Rahul Gupta,Abhinav Mohanty,Payal Motwani,Venkatesh Saligrama,Satyapriya Krishna,Connor Harris,Gary Anthony Ackerman,Brandon Behlendorf,Tom Hobson,Theodore Wilson,Spyros Matsoukas
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
备注: 19 pages, 1 figure, preprint
Abstract:As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor’s ability to plan high-consequence Chemical, Biological, Radiological, or Nuclear (CBRN) misuse relative to public tools alone. Existing CBRN evaluations differ in non-expert definitions, threat scope, baselines, scoring rubrics, and decision rules, making results difficult to compare across studies. We introduce a Threshold Exceedance Criteria (TEC) framework that decomposes an uplift study into independently executable components: determining non-expert participant eligibility, defining the CBRN threat scope for the study, and statistically estimating material uplift. We then operationalize the TEC framework in a large-scale empirical study using a design that determines two forms of uplift: generative (where a model assists plan creation from scratch) and revisionist (where a model assists refinement of an existing plan). The study produced attack plans across the CBRN domains, which we evaluated through subject-matter-expert review to estimate generative and revisionist uplift. Applying the framework, our empirical study revealed domain heterogeneity: under this controlled pre-release evaluation, model-assisted plans sometimes received expert-equivalent instructional ratings, but confirmed material uplift was limited to the radiological domain. These findings informed mitigation and deployment-governance decisions rather than characterizing deployed model behavior. We conclude with methodological lessons for future CBRN uplift evaluations, emphasizing prespecified criteria, explicit baselines, separation of generative and revisionist estimates, and careful distinction between preliminary screening signals and confirmed risk determinations.
[AI-55] Connected by Construction: Learning Tractable Near-Tour Marginals for Traveling Salesman Problems
链接: https://arxiv.org/abs/2607.12127
作者: Ke Sun,Xinyuan Zhang,Xinwu Qian
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Learning-based methods for the traveling salesman problem (TSP) are often evaluated through the tours produced after decoding or search, but the learned object itself frequently lives in a surrogate space such as heatmaps, assignments, construction policies, or search-guidance scores. This hides the fundamental question: what Hamiltonian structure has actually been learned before decoding? In this study, we directly answer this question by learning TSP through a structurally meaningful latent object, rather than leaving most of the Hamiltonian structure to the final decoding stage. Based on a connected-by-construction rooted 1 -tree Gibbs family, we propose an end-to-end unsupervised learning pipeline called \emphC2TSP. The pipeline learns residual edge perturbations from unbiased TSP cost through implicit differentiation. For structural correction, a smoothed Held–Karp layer restores expected degree balance, while certificate-guided sharpening further pushes the connected distribution toward more tour-like structures. Experiments show that C2TSP yields strong decoding performance while preserving interpretable structural information. Ablations further verify that edge perturbation and certificate-guided sharpening jointly improve both tour cost and tour-like structure.
[AI-56] oward Trustworthy Autonomous Science: A Two-Year Community Roadmap
链接: https://arxiv.org/abs/2607.12113
作者: Rafael Ferreira da Silva,Milad Abolhasani,Peter Beaucage,Laura Biven,Michael Bussmann,Kyle Chard,Ryan Coffee,Stephen DeWitt,Sagar Dolas,Carrie Eckert,David Elbert,Ian Foster,Tirthankar Ghosal,Anna Giannakou,Tom Gibbs,Leslie Hamilton,Glenn Lockwood,Theresa Mayer,Ben Mintz,Raffi Nazikian,Sal Nimer,Amanda Randles,Woong Shin,Sreenivas Rangan Sukumar,Frédéric Suter,Mitra Taheri,Michela Taufer,Draguna Vrabie
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than anticipated. Multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field. Producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. We update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18), and scope the path forward to a two-year horizon. The first year concentrates on interfaces, protocol adoption, and the scaffolding of verification, and the second targets federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.
[AI-57] Representing and Generating Levels Over Time through Playtrace Reconstructive Partitioning
链接: https://arxiv.org/abs/2607.12097
作者: Emily Halina,Matthew Guzdial
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 5 figures, ACM Conference on the Foundations of Digital Games
Abstract:Video games are a dynamic medium experienced over time. While there are many Procedural Content Generation (PCG) approaches for generating video game levels, they often use representations that abstract away this dynamic nature. In this paper, we introduce a novel, domain-independent ``cake’’ representation for game levels over time which implicitly encodes dynamic information. We present a novel level generation approach Playtrace Reconstructive Partitioning (PRP) specifically developed for this cake representation. We compare against six state-of-the-art PCG approaches in the game domain of \textitSokoban, and find that our approach can generate valid levels without sacrificing solution diversity. We believe our cake representation more neatly encodes the implicit dynamic nature of games compared to existing representations, which allows for our domain-agnostic level generation algorithm PRP.
[AI-58] Sparse Autoencoders for Interpretable Out-of-Distribution Detection
链接: https://arxiv.org/abs/2607.12094
作者: Ayush Karmacharya(1),Luke Luschwitz(1),Lucia Romero(1),Yanan Niu(2),Joseph Campbell(1) ((1) Purdue University, (2) EPFL)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reliable detection of out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models. Neural networks often produce overconfident predictions for inputs that deviate from their training data, leading to significant degradation in performance. While many OOD detection methods focus on the final output layer, they neglect the rich hierarchical information present in intermediate network layers. This paper introduces a novel approach that leverages sparse autoencoders (SAEs) to learn interpretable features from these intermediate activations. We find that in-distribution (ID) and OOD data activate distinct sets of these sparse features. We propose a new OOD score derived from the cosine similarity between the sparse feature activations of a test sample and the mean activations of ID classes. Our post-hoc detection method not only achieves state-of-the-art performance on standard OOD detection benchmarks, but yields interpretable insights into how distribution shift affects learned representations.
[AI-59] Operationalising Multi-Dimensional Evaluation for Conversational Agents : A Scalable Governed Pipeline with Selective Re-evaluation and Model Benchmarking
链接: https://arxiv.org/abs/2607.12085
作者: Niranjan Kumar M,Balaji Nagarajan,Karthik Nair,Faysal Satter,Nithin Surendran
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 1 figure of design of architecture and 2 tables exploring the results and benchmarking
Abstract:Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality. Although LLM-as-a-judge methods provide scalable alternatives to human evaluation, production deployment introduces challenges in governance, reproducibility, cost, schema consistency, traceability, and reliability. We present GenAI Evaluation, a governed, configuration-driven pipeline for large-scale evaluation of retail conversational systems. It processes production chatbot logs through normalization, sharding, asynchronous execution, and schema-constrained LLM scoring. The framework evaluates helpfulness, truthfulness, clarity, tone alignment, and translation-specific dimensions. Selective re-evaluation processes only incomplete, malformed, or schema-invalid records, while schema locking, versioned configurations, validation logs, and record-level provenance support auditability. The framework processes approximately 50,000 records daily and has evaluated more than two million interactions. Validation used 12,980 stratified-random human-labeled records from four trained annotators. Classification covered 14 intents, 156 sub-intents, 18 major domains, and 129 sub-domains. The pipeline achieved a macro F1 score of 0.93 and 89% human-acceptability accuracy for translation.
[AI-60] AutoTrace: From Patches to Triggers via Agent ic Interprocedural Exploration
链接: https://arxiv.org/abs/2607.12058
作者: Arastoo Zibaeirad,Marco Vieira,Thomas Zimmermann
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Given a vulnerability-fixing commit, trigger localization asks which specific statement turns the vulnerable program state into a concrete unsafe operation. This question is harder than binary vulnerability detection because the answer demands interprocedural, causal reasoning: in a substantial fraction of real-world CVEs the triggering statement lies several call layers outside the patched function, beyond the reach of static rule sets and pattern-matching language models alike. We present AutoTrace, an agentic pipeline that localizes vulnerability triggers by exploring a code property graph layer by layer, with LLM agents deciding where to look next and deterministic admissibility gates deciding what evidence is required before a trigger can be reported. Agents never accept a trigger on their own authority; every reported trigger is backed by explicit evidence drawn from the graph, so the pipeline covers both intra- and interprocedural vulnerabilities without relying on ungrounded model judgment. On the full InterPVD benchmark, AutoTrace reaches 75.0% VulnHit and 80.8% FuncHit, surpassing the prior state of the art on the same corpus. Building on the same machinery, we construct SinkTrace-Bench, a dataset that exposes each vulnerability as a source-to-sink (S2S) causal chain from attacker-controlled input through propagation to the dangerous operation, drawn from matched vulnerable and patched program states. It comprises 1,542 verifier-confirmed, perfectly balanced vulnerable/safe samples whose label fidelity we audit against expert annotations. Benchmarking frontier LLMs on it, we find that even the strongest struggle to separate the matched pairs, exposing the causal-reasoning gap that trigger localization targets. Artifact available at this https URL.
[AI-61] Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLM s ICML2026
链接: https://arxiv.org/abs/2607.11997
作者: Nikita Kozodoi,Zainab Afolabi,Jack Butler
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Accepted to ICML 2026 workshop on weight-space symmetries
Abstract:Multi-task model merging combines separately trained expert models into a single model that handles all tasks without co-training. Standard practice merges experts at their optimal validation loss. We challenge this convention by systematically studying how training duration of domain experts affects the quality of the merged model. We fine-tune experts on five domains (Math, Code, Instruction Following, Multilingual, and Safety) across three model sizes (Qwen 3.5 0.8B, 2B, and 4B), saving checkpoints from 25% to 500% of the optimal training steps and evaluating five merging methods at each duration. Our findings reveal a striking method-dependent pattern: simple averaging degrades sharply with overfitting, while sparsification-based methods achieve their best performance well past the validation optimum. We formalize this through bias-variance decomposition analysis, drawing a parallel to random forests where averaging benefits from high-variance individual learners. These results suggest that training duration and merging method should be chosen jointly rather than independently.
[AI-62] Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure
链接: https://arxiv.org/abs/2607.11994
作者: Bipin Chhetri,Deepika Giri,Avishek Kadel,Rabin Kumar Karki,Akbar Siami Namin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8 pages, 2 figures, 4 tables; preprint submitted to IEEE COMPSAC 2026
Abstract:Classifying cybersecurity vulnerabilities using the Common Weakness Enumeration (CWE) taxonomy is challenging due to extreme class imbalance and strong hierarchical dependencies among weakness categories. Although oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are widely adopted to mitigate class imbalance, their effectiveness for hierarchical CWE text classification remains largely unexplored. This paper proposes a Hierarchy-Aware RoBERTa framework that explicitly incorporates CWE structural information through learnable parent-class embeddings, preserving taxonomic consistency. Our experiments demonstrate that synthetic interpolation in high-dimensional embedding spaces violates the inherent parent-child constraints of the CWE hierarchy, offering only marginal benefits for classical ML models while consistently degrading deep learning architectures. Evaluated on a CWE Research Concept dataset, the proposed model achieves a weighted F1-score of 0.76 without data augmentation, outperforming all baselines with notable gains on minority classes, including the Class category whose F1-score improved from 0.40 to 0.60 over the BERT baseline. Our results suggest that hierarchy-aware representation learning is a more principled alternative to oversampling for structured vulnerability classification.
[AI-63] LP Mining with LP2Graph: A Use Case for Railway Rescheduling
链接: https://arxiv.org/abs/2607.11980
作者: Jörn Maurischat,Nikola Bešinović,Michael Färber
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 2 figures. Work in progress, not yet submitted to a journal; comments welcome. Companion preprint to a talk at IFORS 2026, Vienna
Abstract:Like many optimization-driven domains, railway rescheduling relies on Mixed-Integer Linear Programming (MILP), yet the field’s modeling knowledge is scattered across hundreds of papers in incompatible notations, and narrative surveys organize it subjectively: they classify models by vocabulary rather than by structure, and reproduce neither. We present LP Mining with LP2Graph, a method that mines the structure of published LP and MILP formulations into a reproducible dataset and an induced taxonomy. Its core, LP2Graph, represents each formulation admitted by its canonical grammar as a typed variable–equation graph derived from a single canonical model; once a source is extracted into that model, everything downstream is deterministic. Each source is parsed into this model, homologized, and clustered bottom-up (over variables, then constraints and the objective, then whole-model structure) and, separately, by application domain and solution approach; the resulting groups are labeled by a rule-seeded, self-updating classifier. We validate the representation rather than assume it: per-cluster representatives are regenerated as independent LaTeX and re-solved across CBC, HiGHS and Gurobi against the optimum reported in the source paper. The outcome is an objective, repeatable taxonomy of variables, constraints and model types: the principled foundation on which our raiLPminer line of automated railway-rescheduling model development builds.
[AI-64] Gene Expression-Informed Jointly Controlled Generative Modeling for Precision Molecular Design
链接: https://arxiv.org/abs/2607.11978
作者: Hang Yuan,Chen Li,Wenjun Ma,Tadahiko Murata,Yuncheng Jiang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 7 figures, 7 tables. Source code: this https URL
Abstract:Precision molecular design aims to discover personalized drug candidates through joint control of multiple conditions, such as biological relevance and molecular design strategies. Biological relevance reflects cellular functional states under disease or perturbation conditions, while molecular design strategies provide complementary guidance in terms of structural intentions and property optimization. In this study, we propose JoPMol, a jointly controlled precision molecular generative model that integrates biological states encoded by gene expression profiles with molecular structure information expressed in text, and chemical properties quantified by numerical values within a unified modeling framework. This formulation enables coordinated generation and optimization of candidate molecules under joint condition control. Experimental results show that JoPMol outperforms state-of-the-art methods across multiple evaluation metrics. Moreover, JoPMol demonstrates strong generalization ability in both transfer tasks and biologically grounded simulation scenarios, validating its effectiveness for precision molecular design. The source code is publicly available at this https URL.
[AI-65] Signal-Guided Optimization for Machine Unlearning
链接: https://arxiv.org/abs/2607.11975
作者: Xujia Li,Dan Li,Jian Lou,Wenjie Feng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages, 6 figures
Abstract:Current machine unlearning methods predominantly rely on global, coarse-grained intervention strategies. They lack precise pilot signals to guide the unlearning process and fail to provide differentiable guidance across different unlearning tasks. Due to the varying memorization strengths of samples during original training, such a uniform strategy leads to two problems: some samples are over-unlearned, which harms model utility; while others are under-unlearned, leaving residual information that can be exploited by privacy attacks. In this paper, we propose GSUO, a guidance-signal-aware unlearning optimization framework that designs task-specific fine-grained guidance signals to steer the unlearning process and is applicable to both random-subset and class-wise forgetting tasks. Extensive experiments demonstrate that GSUO outperforms 14 baselines in terms of both unlearning effectiveness and generalization, while achieving high efficiency and significant speedups, validating its effectiveness for reliable machine unlearning.
[AI-66] Learning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance
链接: https://arxiv.org/abs/2607.11974
作者: Zixuan Shen(1),Bingchuan Wang(1),Zhi Wang(2),Yong Wang(1) ((1) Central South University, (2) Nanjing University)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages, 11 figures, 9 tables
Abstract:Most neural partial differential equation (PDE) surrogates learn how fields evolve after a grid has already been chosen. However, before any operator is applied, the grid has already determined how modeling capacity is allocated across space, resolution, and spectral bandwidth. We argue that this hidden design choice should itself be learnable, leading to a question different from standard operator learning: can a surrogate learn where resolution should exist before predicting field evolution? We formulate adaptive discretization as a physics-constrained conditional generation problem over valid mesh displacements. The success of diffusion models in PDE field prediction suggests their potential for learning adaptive discretizations under similar structured constraints. This leads to a two-stage diffusion framework: Stage 1 learns an r-adaptive displacement mesh conditioned on the observed dynamics, while Stage 2 predicts the solution evolution from the mesh-informed representation. The mesh generator is regularized by physics-aware proxy channels, geometric validity constraints, and local spectral concentration so that adaptation remains physically interpretable and numerically legal. Across five PDE regimes, the results show that diffusion-based learned discretization is competitive with adaptive-mesh and reduced-order baselines, with particularly strong gains in regimes where fixed or handcrafted allocation is insufficient. The main conclusion is not that there exists a universal optimal mesh rule, but that discretization should be learned in a regime-dependent manner: different spatial and spectral structures favor different allocation behaviors. This reframes adaptive meshing for neural PDE solvers from a solver-specific heuristic into a generative representation-learning problem.
[AI-67] Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems
链接: https://arxiv.org/abs/2607.11970
作者: Yubo Zhang,Xiaodong Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: 14 pages, 7 figures
Abstract:We develop an enhanced in-context learning (ICL) framework to improve the performance of pilot-based beamforming in multi-user multiple-input single-output (MU-MISO) systems. The proposed scheme integrates the ICL-Transformer backbone with the pilot encoder-decoder network (EDN) and the beamformer EDN. A crucial feature of our ICL network is that it can handle multiple channel models without retraining, enabled by the construction of model-specific context datasets. To improve convergence and robustness, we introduce three key innovations: (a) a curriculum learning (CL) strategy that smoothly transitions from supervised LMMSE-labeled imitation to unsupervised sum-rate maximization, (b) a self-evolving mechanism that dynamically expands and refines the context datasets for all channel models during CL-based training, and © a mismatch-aware extension that incorporates several mismatches into the general ICL framework and bypasses explicit channel calibrations. Ablation studies validate the effectiveness of the in-context architecture and enhanced training strategies. Simulation results over diverse communication environments show that the proposed scheme is able to rapidly adapt to both seen and unseen channel models without gradient-based parameter updates, and can mitigate the mismatch issues via intelligent context constructions. Furthermore, our scheme consistently outperforms the existing beamforming schemes under pilot-based settings, including the WMMSE benchmark and the recent Transformer-based methods.
[AI-68] Beyond Coordinate Gauge: An Audited Protocol for Detecting Donor-Specific Functional Fingerprints after Neural Collapse
链接: https://arxiv.org/abs/2607.11967
作者: Truong Xuan Khanh,Phan Thanh Duc
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 23 pages, 4 figures, 9 tables
Abstract:Independently trained neural networks have no shared neuron-index reference frame, so comparing them requires accounting for coordinate freedom. Neural Collapse sharpens this problem: networks converge toward a shared, low-dimensional geometry, raising the question of whether trajectory-specific functional variation remains distinguishable after convergence. We distinguish three claims - detectability, transplantability, and causal persistence - and address the first. Using five independently trained networks reconstructing Neural Collapse on MNIST, we apply a verified affine-correct alignment mapping donor heads into recipient coordinates. Donor-specific functional fingerprints remain distinguishable after recipient-level baseline correction: all 20 ordered donor-recipient pairs are correctly identified, with an exact permutation p=0.0083, robust to a leakage audit. These findings establish detectability under the test used here, but not transplantability or causal persistence. The study shows how alignment, ambiguity diagnostics, and leakage control combine to test cross-network variation in a controlled setting; whether this generalizes beyond it is open.
[AI-69] Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability
链接: https://arxiv.org/abs/2607.11963
作者: Mashrul Hossain,Nafesa Kibria,Fahim Shahriar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 7 Figures, Preprint
Abstract:The early detection of Chronic Kidney Disease using machine learning has attracted significant interest in healthcare-related computer science. Despite rapid advancements in this field, many reported studies remain inconsistent and potentially misleading. A significant drawback is the lack of organized evaluation regarding methodological concerns. Key issues include data leakage, limited access to temporal patient records and inconsistency in reported clinical indicators. This research offers a systematic literature review of existing CKD prediction studies using interpretable machine learning techniques, where nineteen relevant studies were selected via systematic searches across major academic databases. To assess methodological reliability, this study introduces a structured taxonomy of information leakage and a quantitative leakage scoring framework to systematically evaluate reliability across CKD prediction studies. The analysis reveals a strong relationship between leakage and inflated performance. Here, High leakage-studies report an average accuracy of 95.48%, compared to 80.2% for leakage-free studies, reflecting an increase of approximately 15.28%. Furthermore, a cross-study feature stability analysis shows that only a small subset of predictors is consistently reproducible, with over 80% lacking reliability. Overall, the findings suggest that many reported performance improvements stem from methodological limitations rather than true predictive capability.
[AI-70] Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses
链接: https://arxiv.org/abs/2607.11959
作者: Yuhui Bie,Guowei Xu,Yaojun Wang
类目: Artificial Intelligence (cs.AI)
备注: 28 pages, 8 figures
Abstract:Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone. For smart-greenhouse control, however, a single simulator return is not enough: a grower or control engineer also needs to know when the policy heats, enriches CO2, vents, manages humidity, deploys screens, or uses this http URL propose a reproducible calibration-first reward audit framework that keeps named greenhouse-control reward components comparable across simulator training, facility-adapted rollouts, logged Autonomous Greenhouse Challenge records, and actuator-rule distillation. In GreenLight-Gym, the framework decomposes the scalar reward into conditional temperature, CO2, humidity and vapor-pressure-deficit, screen, and actuation-proxy terms; adapts GreenLight to the second Autonomous Greenhouse Challenge logged climate traces; and scores the same components on logged greenhouse data.
[AI-71] Exact and Certified Data Shapley for Weighted k-Nearest-Neighbor Regression and Soft-Label Prediction
链接: https://arxiv.org/abs/2607.11956
作者: Zongye Lyu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS)
备注: 30 pages, 3 figures. Full proofs of all theorems in the appendices. Preprint of a manuscript submitted to IEEE Transactions on Knowledge and Data Engineering
Abstract:Data Shapley is the standard principled answer to which training points are worth what, and its k-nearest-neighbor (KNN) specialization is the version deployed in practice: the exact estimator shipped by toolkits such as pyDVL and OpenDataVal. Exact algorithms are known for unweighted KNN and for weighted KNN classification, but weighted KNN regression and soft-label prediction have resisted: the only exact method is an O(N^K) brute force, exponential in neighborhood size K. The obstruction: the weighted regression prediction is a ratio of two coalition-dependent sums, whose normalization denominator breaks the additive, threshold, and duplication structures the prior polynomial algorithms rely on. We close this gap. We give (i) the first pseudo-polynomial-time exact algorithm (polynomial in N and K at fixed lattice precision) for weighted KNN-regression Data Shapley, a counting dynamic program over the joint integer state (sum of w, sum of w*y), verified against exhaustive enumeration with zero mismatch on 12,716 adversarial instances; (ii) a certified FPTAS for continuous weights and targets, with a machine-checkable per-value error certificate never violated across 86,400 checks; (iii) a complexity landscape, including an unconditional Omega(D_w) output-size lower bound and access-model hardness results; and (iv) a weighted soft-label multi-class extension. We release an open-source, CPU-only library and the first exact weighted-regression Data Shapley ground truth. On downstream mislabel detection our exact values are statistically equivalent to Monte-Carlo Data Shapley (dataset-level TOST, n=8, p10^-4), the pre-registered outcome; the value of exactness is instead determinism, a certified error bound, and an exact reference for auditing estimators: Monte-Carlo did not reproduce the exact top-10% ranking at any budget tested, up to 3,000 permutations (~1.28e6 utility evaluations).
[AI-72] Scale-Aware Attention for Scarce Neural Data: An RG-Flow Transformer on Sleep-EDF EEG
链接: https://arxiv.org/abs/2607.11950
作者: Dibakar Sigdel
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Brain field potentials are scale-free: their power spectra follow a 1/f^\beta law whose aperiodic exponent \beta tracks cortical state, and sleep depth in particular is a shift in \beta . We ask whether a transformer endowed with an explicit renormalization-group (RG) inductive bias – the RG-Flow Transformer, which couples ordinary self-attention to a scale-aware stream with a learnable anomalous dimension \gamma , block-spin coarse-graining, and an entropy-gated synchronization bridge – has an advantage over a parameter-matched vanilla transformer on \emphreal, scarce EEG. Using the PhysioNet Sleep-EDF corpus with a strict leakage-free by-subject hold-out, we (i) benchmark RG-Flow against a param-matched vanilla transformer and a hierarchy-only ablation on 5-class AASM sleep staging, (ii) sweep the per-subject data budget to look for the inductive-bias crossover predicted when data are scarce, and (iii) test whether RG-Flow’s learned \gamma tracks the measured spectral exponent \beta out-of-sample – a quantity the vanilla model does not possess. Across 5 subjects and 5 seeds under leave-one-subject-out cross-validation, RG-Flow and the vanilla transformer are statistically indistinguishable on 5-class staging (77.3% vs 77.0% accuracy; paired p=0.294 ), and the predicted scarce-data crossover does not appear: vanilla is numerically ahead at every data-limited budget. What does separate the models is interpretability – RG-Flow recovers the continuous spectral exponent out-of-sample ( \beta -recovery R^2 = 0.416 ), a capability the vanilla architecture has no analogue for.
[AI-73] Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite UAI
链接: https://arxiv.org/abs/2607.11947
作者: Yushi Hirose,Hiroo Irobe,Takafumi Kanamori
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to The Conference on Uncertainty in Artificial Intelligence (UAI) 2026
Abstract:Typical semi-supervised learning (SSL) methods rely on distributional assumptions, and their performance degrades when these are violated. While PNU learning, a risk rewriting method, offers a distribution-free alternative, it is restricted to binary classification and its variance optimality remains unclear. In this paper, we propose a generalized framework that constructs unbiased risk estimators using linear combinations of component risks, subsuming PNU learning and extending to multiclass classification. We derive the minimum achievable variance, demonstrating our estimator can attain lower variance than PNU in asymmetric loss scenarios. Furthermore, we establish a generalization bound directly linking this variance reduction to improved learning performance. Based on these theoretical insights, we introduce two practical SSL methods that empirically match or outperform existing approaches on binary and multiclass benchmarks.
[AI-74] BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification
链接: https://arxiv.org/abs/2607.11943
作者: Raghvender Raghvender,Mahdi Abid,Ferran Brosa Planella,Charles Delacourt,Arnaud Demortière
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 9 figures
Abstract:Long-horizon physics-based simulations of battery degradation provide mechanistic insight but remain computationally expensive, limiting their use for dense exploration of operating conditions over extended cycle life. Here, we propose a hybrid physics-probabilistic learning framework for surrogate modeling of lithium-ion battery degradation trajectories at unseen charging rates. Cycle-resolved degradation data generated with a DFN/P2D electrochemical model in PyBaMM are first transformed into capacity-aligned voltage and derivative features and encoded using a Variational Autoencoder (VAE). The resulting two-dimensional latent space organizes degradation trajectories according to both cycle progression and charging protocol. A sparse multitask Gaussian process (GP) is then trained in this latent space using cycle number and C-rate as input variables, providing continuous interpolation of latent degradation dynamics together with posterior uncertainty estimates. Under protocol-level holdout evaluation, the latent-space GP accurately recovers unseen C-rate trajectories and exhibits uncertainty behavior consistent with the support of the training data. When queried at unseen interior C-rates, the model generates latent trajectories that remain coherently positioned between neighboring simulated protocols. Decoding the GP-predicted latent states through the frozen VAE decoder yields smooth voltage-capacity evolution, while Monte Carlo propagation of the GP latent posterior through an auxiliary latent to State of Health (SOH) predictor provides uncertainty-aware SOH estimates. The proposed BattVAE-GP framework therefore offers a computationally efficient and uncertainty-aware surrogate for long-horizon degradation modeling, providing a structured basis for extending battery health prediction toward richer operating conditions and future simulation-experiment fusion.
[AI-75] How Query Visibility Changes KV-Cache Compression Rankings: A Matched-Budget Audit
链接: https://arxiv.org/abs/2607.11942
作者: Daming Luo,Christy Liang,Junyu Xuan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures
Abstract:KV-cache compression methods are predominantly evaluated with the query appended to the context before compression – a query-aware protocol. Yet the economic case for a compressed KV cache is reuse: compress a document once, answer many future questions against it. In that deployment, compression must happen query-agnostic – before any question is seen. We present a matched-budget audit of six published compression methods against three trivial baselines on three open 7-9B models (144,300 paired evaluations on RULER-8192; 40,800 on LongBench; 50,000-resample paired bootstrap throughout). Everything is held fixed – model, compression ratio, instances, decoding – except the scoring rule. Three findings. (1) Query visibility changes the rankings: under the agnostic protocol, of the five audited methods that share a common attention backend, only KeyDiff beats a best-of-3 trivial baseline consistently (31 of 36 cells), and the most widely deployed method, SnapKV, loses to “keep the start and the recent window” on average (-0.066). (2) The per-method drop between the two protocols is ordered consistently with how visible the question is to each method’s scoring signal, legible in its source code: from Delta=+0.198 for SnapKV (the question sits inside its 64-token observation window) down to Delta=+0.011 for KeyDiff (its score contains no query term at all).
[AI-76] CARE-LoRA: Compressed Activation REconstruction for Memory-Efficient LoRA
链接: https://arxiv.org/abs/2607.11940
作者: Gengyu Zhang,Haiyin Ran,Zhengbao He,Yuhang Liu,Hanling Tian,Zhehao Huang,Xiaolin Huang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 2 figures, 12 tables. Code available at this https URL
Abstract:As the scale of large pre-trained models continues to grow, fine-tuning them under limited memory budgets has become increasingly challenging. Low-Rank Adaptation (LoRA), currently one of the most widely adopted parameter-efficient fine-tuning (PEFT) methods, mitigates this challenge by optimizing only low-rank adaptation matrices, thereby greatly reducing the number of trainable parameters. With the parameter overhead substantially reduced, the activations retained for backpropagation have emerged as the primary remaining memory bottleneck during LoRA fine-tuning. To address this, we propose CARE-LoRA, a data-aware Compressed Activation REconstruction framework. By exploiting the inherent projection structure of LoRA, CARE-LoRA replaces the full input activation with the low-rank compressed activation naturally produced by the LoRA branch. It further computes a lightweight reconstruction matrix during the forward pass with negligible additional computation cost, which is used during backpropagation to reconstruct the gradient signal, thereby keeping LoRA matrices fully trainable. Extensive experiments across diverse models and downstream tasks demonstrate that, while substantially reducing the overall memory footprint, CARE-LoRA achieves competitive or even superior performance compared with standard LoRA and representative LoRA variants. Our code is publicly available at this https URL .
[AI-77] Mathematics of Data Science
链接: https://arxiv.org/abs/2607.11938
作者: Afonso S. Bandeira,Amit Singer,Thomas Strohmer
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Probability (math.PR)
备注:
Abstract:This book is about the mathematical foundations of data science. 1. Introduction 2. Curses, Blessings, and Surprises in High Dimensions 3. Singular Value Decomposition and Principal Component Analysis 4. Linear Regression and Regularization 5. Graphs, Networks, and Clustering 6. Nonlinear Dimension Reduction and Diffusion Maps 7. Linear Dimension Reduction via Random Projections 8. Optimization for Data Science 9. Classification 10. A Mathematical Introduction to Deep Learning 11. Large Sample Limit of Graph Laplacians 12. Community 13. Concentration of Measure and Gaussian Analysis 14. Matrix Concentration Inequalities 15. Compressive Sensing and Sparsity 16. Low-Rank Matrix Recovery Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Probability (math.PR) Cite as: arXiv:2607.11938 [cs.LG] (or arXiv:2607.11938v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.11938 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Thomas Strohmer [view email] [v1] Sat, 11 Jul 2026 08:31:44 UTC (15,747 KB) Full-text links: Access Paper: View a PDF of the paper titled Mathematics of Data Science, by Afonso S. Bandeira and Amit Singer and Thomas StrohmerView PDFTeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-07 Change to browse by: cs cs.AI cs.IT math math.IT math.PR 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-78] AAAI-26 Dual Submissions: Novel Challenges
链接: https://arxiv.org/abs/2607.11918
作者: Kiri L. Wagstaff,Joydeep Biswas,Erich Merrill III,Bo An,Ida Camacho,David J. Crandall,Matthew E. Taylor
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 12 pages, 5 figures, 2 tables
Abstract:Dual submissions, in which identical or substantially similar papers are simultaneously submitted to one or more archival venues, without cross-citation or disclosure, are a growing problem for the AAAI Conference and other scientific publication venues. These submissions increase the burden on the peer-review system and pollute the scientific record. As part of the AAAI-26 review process, we (conference organizers) compared AAAI main-track submissions to nine other archival venues with overlapping review periods. We also searched for dual submissions within the AAAI-26 main track. We employed title+abstract similarity assessment to prioritize highly similar paper pairs for subsequent triage by an LLM-based overlap assessment tool, followed by manual review of the highest severity pairs. Manual review of such pairs led to the desk-rejection of 141 AAAI-26 main-track submissions. We seek to alert future organizers, and the broader artificial intelligence research community, to the enormous growth in dual submissions. The incidence of exact duplicate submissions, which are easy to detect, has been eclipsed by the number of papers that use different words to describe the same contribution, which are extremely time-consuming to detect. The growth in this phenomenon is likely facilitated by increasing access to generative AI tools. We include several recommendations for addressing this challenge, including (1) updating the AAAI Multiple Submission Policy and educating the community about acceptable practice, (2) having dual-submission checking tools in place before submissions close, (3) working across venues to converge on consistent policies and penalties to aid in reducing the incidence of dual submission, and (4) creating a community-driven adversarial challenge to accelerate the development of robust detection tools. Comments: 12 pages, 5 figures, 2 tables Subjects: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG) ACMclasses: K.4.3; K.7.4 Cite as: arXiv:2607.11918 [cs.DL] (or arXiv:2607.11918v1 [cs.DL] for this version) https://doi.org/10.48550/arXiv.2607.11918 Focus to learn more arXiv-issued DOI via DataCite
[AI-79] Burst Spiking Neural Networks
链接: https://arxiv.org/abs/2607.11914
作者: Jiahong Zhang,Sijun Shen,Man Yao,Han Xu,Mingqiang Huang,Yonghong Tian,Bo Xu,Guoqi Li
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注: 18 pages, 21 figures, 1 supplementary material PDF, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence
Abstract:A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs). This work further argues that realizing this ambition requires improving not only accuracy but also robustness, defined as the ability to maintain correct predictions under input perturbations. We identify two key issues in existing SNN methods that undermine robustness. First, binary spiking activations can produce large activation-state changes under small perturbations. Second, the lack of effective weight constraints makes network outputs more sensitive to input variations. To this end, we propose Burst Spiking Neural Networks (BuSNNs), built upon Burst-enhanced Spiking Neurons (BSNs) and a Dynamic Weight Constraint (DWC) mechanism. BSNs incorporate burst firing to provide a graded spiking pattern. This spiking mechanism mitigates perturbation-induced transitions in activation states and thereby enhances robustness. DWC penalizes connection weights based on activation states, effectively reducing weight magnitudes and improving robustness while preserving accuracy. We provide theoretical analyses to support these robustness effects. Experimental results further show that, on smaller-scale benchmarks such as CIFAR-10, BuSNNs outperform both SNN and ANN counterparts in accuracy and robustness. On large-scale ImageNet, BuSNN with the MS ResNet-34 backbone further improves top-1 accuracy and corruption robustness over the corresponding SNN baseline by 3.18% and 2.66%, respectively. Despite using spike-based activations, BuSNNs surpass 4-bit activation-quantized ANN baselines and approach 8-bit ANN baselines on ImageNet. They also preserve SNNs’ low-power advantage. This work studies the accuracy-robustness problem in SNNs, advancing their practical viability in robust and energy-efficient applications.
[AI-80] owards Self-Evolving Agents : A Human-Inspired Adaptive Exploration-Exploitation Framework for Genetic Network Programming
链接: https://arxiv.org/abs/2607.11913
作者: Ali Kohan,Mohamad Roshanzamir,Roohallah Alizadehsani,Seyedali Mirjalili
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advancements in agentic AI have increasingly moved toward graph-based methods, driven by the demand for explainable, human-centered, and non-linear reasoning workflows. A prominent example is Genetic Network Programming (GNP), a self-evolving algorithm that utilizes directed graphs to evolve interpretable decision structures for agents. As in most evolutionary algorithms, effectively balancing exploration and exploitation is a key aspect of GNP. However, this trade-off has received limited attention in the GNP literature. To address this gap, we draw inspiration from human developmental patterns, where children prioritize broad experimentation and action over deliberation, with this tendency reversing with age. By mapping transitions between GNP’s judgment nodes to deliberation and processing nodes to action, we propose Human-Inspired GNP (HGNP), a novel adaptive framework that dynamically regulates the exploration-exploitation balance throughout the evolutionary process. The method consists of novel adaptive crossover and mutation operators, and a cycle elimination mechanism. HGNP not only improves the evolutionary process but also provides a framework for adjusting the exploration-exploitation balance based on the characteristics of the target environment and its search space. This approach is more effective than tuning via crossover and mutation probabilities in standard GNP. The modifications are general and can be applied to almost all GNP variants. When integrated with standard GNP and two recently introduced GNP variants and evaluated on the Tileworld benchmark, HGNP demonstrated significant performance improvement in agents’ strategy. The combination of HGNP with Situation-based GNP (HGNP-SBGNP) achieved the best overall results.
[AI-81] SeqGPT : A Constrained Transformer Agent for the Inverse Designof Multi-Panel Composite Structures
链接: https://arxiv.org/abs/2607.11910
作者: Driss Chraibi(Toulouse INP),Alejandro García Pis,Stéphane Grihon,Sixin Zhang(IRIT)
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:Optimizing composite stacking sequences to match continuous targets (e.g., Lamination or Buckling Parameters) with discrete manufacturing constraints represents a challenging combinatorial inverse problem that regularly occurs in composite design especially when numerical optimization approaches are used (bi-step, bi-level configurations). In multipanel configurations, this complexity is further intensified by blending, a global compatibility/continuity requirement between the different panel stackings. This study presents SeqGPT, a conditional Transformer agent developed to replace computationally expensive iterative methods. To ensure both global continuity and manufacturing feasibility by construction, we implemented a hybrid neurosymbolic decoding strategy. SeqGPT predicts a conditional distribution that guides a Constrained Beam Search, where any branch violating blending rules is strictly pruned. Numerical experiments on the 18-panel horseshoe benchmark demonstrate that SeqGPT generates solutions near-instantaneously with buckling performance comparable to evolutionary methods, offering a significant speed-up compared to the state of the art.
[AI-82] In-Context Reinforcement Learning under Non-Stationarity: A Survey
链接: https://arxiv.org/abs/2607.11906
作者: A Run,Ziluo Ding
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest in in-context reinforcement learning (ICRL): the ability of a pretrained or fine-tuned decision model to infer latent task rules and improve future behavior from interaction context, without test-time parameter updates. This line of work asks when trial-and-error evidence, rewards, transitions, demonstrations, feedback, or retrieved experience can make learning-like computation happen inside the context window. However, existing surveys of ICRL mainly organize the field around pretraining objectives, architectures, context formats, evaluation protocols, and theoretical mechanisms, while the non-stationary setting remains comparatively underexamined. In changing environments, accumulated context is not merely more evidence about a fixed task: the reward specification, transition kernel, observation channel, action interface, constraint model, or demonstration and memory distribution can fall out of alignment with the current regime. Previously useful context can therefore become stale, misleading, or useful again when an old regime returns. We survey non-stationary ICRL as the problem of adapting through context while deployed policy parameters remain fixed: the policy must infer both the current decision rule and which parts of its accumulated evidence still support that rule. We define non-stationary ICRL, relate it to meta-RL, decision sequence modeling, retrieval-augmented RL, value- and model-aware ICRL, and reward-feedback agents, and organize the literature along three questions: what changes, how the change unfolds, and how observable the change is to the agent.
[AI-83] OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes
链接: https://arxiv.org/abs/2607.11896
作者: Shuangshuang He,Shuo Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Atmospheric and Oceanic Physics (physics.ao-ph)
备注:
Abstract:Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms. Chemical transport models (CTMs) provide gridded forecasts but retain local biases, whereas graph neural networks (GNNs) track monitoring sites well at short lead times but do not produce gridded outputs. Here we present OmniPM-Net, a Convolutional Conditional Neural Process (ConvCNP)-based fusion model that reconciles these two forecast types within a shared spatial representation. A terrain-aware Gaussian set convolution lifts irregular GNN station forecasts onto a regular grid, where a multi-scale Spatial Source Attention (SSA) module blends them with Copernicus Atmosphere Monitoring Service (CAMS) forecasts; a shared omni-query readout then decodes this representation into consistent PM10 predictions at either stations or grid cells over a 108 h horizon. Evaluated across 1,618 air-quality monitoring stations throughout China over the full year of 2024, OmniPM-Net matches the station-level accuracy of the stronger GNN baseline (mean absolute error 21.14 versus 22.00 ug/m3) and reduces the CAMS mean absolute error by 30%, while simultaneously delivering the gridded fields that the discrete GNN cannot. Its clearest gains are in the high-concentration tail, where the 90th-percentile MAE falls by 9% relative to the GNN and 25% relative to CAMS, and during dust episodes, where it improves categorical detection skill while tracking the evolving spatial trajectory.
[AI-84] So Many Opinions So Many LLM s: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis
链接: https://arxiv.org/abs/2607.11890
作者: Abdullah Akinde,Mariam Akinde,Rasheedat Emiola,Ahmed Akinsola
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale. Building on previous work that employed traditional machine learning to classify text (“So Many Responses, So Little Time: A Machine-Learning Approach to Analyzing Open-Ended Survey Data”) [1], this study investigates how different large language models (LLMs) understand and analyze NSSE open-ended survey responses. We focus on several cutting-edge LLMSs-OpenAI’s GPT series, Twitter-roBERTa-base model, and Meta’s LLaMA-and compare their performance to the previous machine learning models in tasks like sentiment analysis and thematic classification. Our research analysis assesses model agreement, classification accuracy, and interpretability of reasoning. The findings reveal that current LLMs routinely beat classic machine learning models in classification accuracy, particularly in understanding complex mood and theme patterns in student replies. While LLMs have superior accuracy, they differ greatly in how explicitly and consistently they justify their predictions and apply category boundaries. These distinctions highlight crucial trade-offs when using LLMs for qualitative analysis: increased predictive strength comes with issues in consistency and explainability. Our findings illustrate the benefits and drawbacks of utilizing various LLMs for large-scale qualitative research, and we provide practical advice for researchers looking to balance automation and interpretive rigor.
[AI-85] Optimal Adaptive Market Making: A Theoretical Framework for High-Yield Liquidity Provision in Perpetual Futures Markets
链接: https://arxiv.org/abs/2607.11888
作者: Minmin Zeng,Yi Liu
类目: Artificial Intelligence (cs.AI)
备注: 42 pages, 23 figures
Abstract:We develop a rigorous theoretical framework for optimal market making in perpetual futures markets with zero maker fees. We model the market maker’s problem as a stochastic optimal control problem on a filtered probability space, where the controls are adaptive bid-ask spreads and inventory hedging decisions across two exchanges. Our contributions include: (i) a PnL decomposition theorem separating revenue into spread income, adverse selection loss, inventory carrying cost, hedging friction, and funding rate exposure; (ii) the Hamilton-Jacobi-Bellman equation for the joint spread-inventory-hedging control problem under CARA utility with a verification theorem; (iii) High-APY Regime Theorems characterizing profitable regions via five dimensionless parameters, culminating in a Master APY Formula; (iv) analysis of zero-fee economics on decentralized perpetual exchanges with optimal entry-exit thresholds; (v) optimal cross-exchange hedging policies with funding rate dynamics and a hedge regime trichotomy; (vi) a robustness margin quantifying parameter uncertainty tolerance; (vii) exponential drawdown probability bounds and a universal APY-VaR identity; (viii) ergodic inventory distribution under optimal control with Bayesian adaptive estimation; (ix) Kelly-optimal leverage with ruin boundaries; and (x) multi-pair portfolio allocation with diversification saturation results. Numerical analysis with twenty-three figures reveals phase transitions between profitable and unprofitable regimes. Our framework unifies and extends the Avellaneda-Stoikov, Gueant-Lehalle-Fernandez-Tapia, and Glosten-Milgrom paradigms for modern decentralized venue microstructure.
[AI-86] ABot-Agent OS: A General Robotic Agent OS with Lifelong Multi-modal Memory
链接: https://arxiv.org/abs/2607.10350
作者: Jiayi Tian,Shiao Liu,Yuting Xu,Jia Lu,Zihao Guan,Honglin Han,Di Yang,Minqi Gu,Yifei Qian,Tianlin Zhang,Yanqing Zhu,Zeqian Ye,Menglin Yang,Fei Wang,Xu Hu,Xiuxian Li,Wei Zhang,Shihui Su,Yiyan Ji,Jingbo Wang,Ziteng Feng,Jiaheng Liu,Zhaoxiang Zhang,Xiaolong Wu,Mingyang Yin,Zedong Chu,Mu Xu
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Code: this https URL
Abstract:Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution. We present ABot-AgentOS, a general robotic Agent Operating System that sits above low-level controllers and provides a deliberative agent layer for scene-conditioned planning, context-isolated skill execution, multi-stage verification, multi-modal memory, and edge-cloud collaboration. To evaluate such systems, we introduce EmbodiedWorldBench, an executable benchmark with 16 indoor, outdoor, and hybrid scenes, four difficulty levels, and over 200 tasks involving navigation, object search, NPC dialogue, dynamic events, and trace-grounded scoring. ABot-AgentOS further introduces Universal Multi-modal Graph Memory, a persistent source-grounded substrate that converts dialogue, visual observations, spatial context, temporal relations, and task traces into typed nodes and edges. A failure-driven self-evolution loop converts diagnosed memory failures into gated runtime evo-assets that are promoted only to later evaluation splits, preventing current-split ground-truth leakage while enabling continual improvement. On an initial EmbodiedWorldBench subset, ABot-AgentOS improves over a single-controller baseline in both task success and goal completion. Across memory benchmarks, ABot-AgentOS Static achieves 87.5 on LoCoMo, 59.9 on OpenEQA EM-EQA, 88.6 on Mem-Gallery, and 76.5 Acc@All on NExT-QA; self-evolution further improves LoCoMo to 88.7, OpenEQA to 60.4, and Mem-Gallery to 89.0. These results suggest that a general Agent OS layer can improve long-horizon embodied execution while providing persistent, auditable memory for continual interaction.
[AI-87] Answering Without Referring: How AI Search Rewrites the Webs Economic Bargain
链接: https://arxiv.org/abs/2607.07652
作者: Qiaoni Shi,Kai Zhu,Kai Gu
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:Search engines have long allocated attention on the web by routing users from queries to websites. AI search changes this arrangement because information needs can be resolved inside the intermediary. Using URL-level Comscore U.S. desktop clickstream, we compare ChatGPT and Google information-seeking occasions and exploit ChatGPT Search access expansions to estimate traditional search displacement. ChatGPT produces outbound clicks in only 5.2% of conversation sessions, far below Google’s referral ratio. The remaining clicks are not a scaled-down Google stream: they skew toward specialized destinations and away from ad-supported sites. Wider access cuts search use by 9.4%, with search-referral losses largest for informational categories. Our findings identify a central economic shift in digital intermediation: AI search might satisfy information needs inside the intermediary while weakening the referral bargain that has linked search, traffic, and content production on the open web.
[AI-88] When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis
链接: https://arxiv.org/abs/2607.12780
作者: Mehdi Saeedi,Eddie Richter,Paul Hartke
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: 11 pages, 8 figures, 7 tables, IEEE International Conference on Quantum Computing and Engineering (QCE) 2026
Abstract:Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates. We study this problem using a compact 44.8M-parameter encoder-decoder transformer with structured circuit tokenization, evaluating on parameterized circuits (2-6 qubits) and Clifford+T circuits (3-6 qubits). On parameterized circuits, a hybrid approach – structure from the transformer, angles from classical optimization – achieves median fidelity 1.000 on 3-6 qubit circuits. On Clifford+T circuits, where all gates are discrete and no post-processing is possible, the model learns valid syntax and accurate T-Count statistics, yet exact equivalence degrades sharply with target length – from 88% on circuits with =9 gates to near zero beyond 26 gates. We trace this failure to autoregressive drift: early-token divergence cascading irrecoverably through left-to-right decoding. Two levers partially mitigate the drift: inference-time strategies that generate multiple candidates and select via equivalence verification raise exact-match rates from 7% to 22.5%, while scaling training data by 2.5x pushes them to 39.5%. Yet the degradation with target length persists – even with more data, exact equivalence drops from 94% on short circuits to under 4% beyond 26 gates. The contrast between settings is our central finding: when approximate outputs can be rescued by post-processing, the transformer succeeds; when exact discrete correctness is required, autoregressive drift limits reliability, with both inference-time search and data scaling as effective levers while training-side fine-tuning and model-level diversification are not. Comments: 11 pages, 8 figures, 7 tables, IEEE International Conference on Quantum Computing and Engineering (QCE) 2026 Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) Cite as: arXiv:2607.12780 [quant-ph] (or arXiv:2607.12780v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.12780 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-89] Partial Identification with Multiple Nonlinear Measurements of a Latent Regressor
链接: https://arxiv.org/abs/2607.12219
作者: Burhan Ogut,Michelle Yin
类目: Econometrics (econ.EM); Artificial Intelligence (cs.AI)
备注:
Abstract:We study linear regression when the regressor is latent and observed only through multiple noisy measurements, each a smooth but possibly nonlinear function of the latent variable. The problem is acute in the measurement of occupational exposure to artificial intelligence, where competing scores yield downstream estimates that differ by a factor of eleven. A regression on any single measurement recovers a source-specific coefficient rather than the structural one. We fix the latent scale by requiring the consensus measurement function to be linear and bound the remaining curvature heterogeneity across sources relative to slope. Under this bound, the structural coefficient lies in a closed-form interval centered at a symmetric cross-source estimator. The interval is invariant to unknown source loadings, and its half-width is second order in the curvature bound and sharp to the same order. With at least four measurements, the bound is estimable from the joint distribution of the sources through a split-instrument auxiliary regression, and Imbens-Manski confidence intervals with the Stoye critical value attain uniform coverage over the curvature class, including at the point-identified boundary. The application matches six exposure measures to an American Community Survey panel of 8.88 million person-year observations for 2015 to 2024. The post-2022 employment coefficient changes sign between the language-model measures and the Webb patent-text measure, and an ex ante factor-analytic rule separates the Webb measure as a distinct construct. The five retained sources yield a loading-invariant consensus coefficient of -0.239, with a partial-identification half-width of 1.23 percent of the point estimate, or 1.88 percent at the one-sided 95 percent upper bound on the curvature. We read the application as measurement reconciliation rather than as a causal estimate of AI displacement.
[AI-90] he Benjamini–Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests
链接: https://arxiv.org/abs/2607.12208
作者: Edgar Dobriban
类目: atistics Theory (math.ST); Artificial Intelligence (cs.AI); Methodology (stat.ME)
备注:
Abstract:We show that the Benjamini–Hochberg procedure can fail to control the false discovery rate (FDR) at its nominal level for correlated two-sided Gaussian p -values. We construct a factor model for which, at level \alpha=0.01 , a rigorous interval-arithmetic certificate proves FDR0.0104 for all sufficiently large numbers of hypotheses. This disproves a conjecture widely believed to be true for twenty years. Monte Carlo experiments are consistent with the theoretical result. The proof was obtained by GPT-5.6 Pro and carefully checked by the author.
[AI-91] HPC-Enabled Video-based Coastal Wave Parameter Estimation Using V-JEPA and Deep Spatiotemporal Learning
链接: https://arxiv.org/abs/2607.11998
作者: Abubakar Hamisu Kamagata,Dharm Singh Jat,Attlee Munyaradzi Gamundani,Saravanakumar Paramasivam,Babangida Sani,Aliyu Zakariyya
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:High deployment cost, poor spatial coverage and susceptibility to storm conditions are all challenges faced by traditional in-situ methods. This paper presents a video-based and high performance computing (HPC) enabled deep learning framework for joint sensor free estimation of five coastal wave parameters, namely significant wave height (Hs), maximum wave height (Hmax), peak period (Tp), zero upcrossing period (Tz) and wave direction (theta) from monocular coastal video. The proposed architecture comprises of a V-JEPA (self supervised) ViT Small backbone for robust spatiotemporal feature extraction in visually challenging scenarios, a dual-stream SlowFast temporal encoder for broad bandwidth representation of wave motion in both hydrodynamic breaking and swell regimes, an optical flow stream based on Farneback optical flow algorithm for adding saliency information to the structure with emphasis on hydrodynamically active wavelength bands of waves, and a multi-task regression layer with dispersion constraints (Airy wave dispersion lambda_p = 0.1). The model was trained on an NVIDIA DGX A100 cluster and was early stopped at epoch 31 and achieved Pearson correlation coefficients of 0.451, 0.578, 0.643, 0.680 and 0.832 for Hs, Hmax, Tp, Tz and wave direction respectively, with generalization ability to geographically diverse held out test data sites. While operating in a data-limited regime (6 annotated training scenes), the framework demonstrates statistically significant temporal correlations (PCC of 0.451 to 0.832), confirming proof of concept feasibility; R2 values (max 0.246) indicate that variance capture will improve with larger annotated datasets.
[AI-92] Removable Defects: The Economics and Limits of Deliberate Deficiency
链接: https://arxiv.org/abs/2607.11983
作者: Cheng Qian
类目: Econometrics (econ.EM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: 30 pages
Abstract:A specialist tolerates blind spots that a generalist does not. Usually this is treated as a cost to be minimized. We treat it as a design variable: a deficiency can be kept because it pays and removed on demand in the rare situation where it would be fatal, by routing to a compensation channel. We give three results. First, an advantage condition under which keeping the deficiency is a computable economic position; structurally it is the Ehrlich-Becker market-vs-self-insurance margin applied to a competence gap, with the detector as a Townsend costly-state-verification technology. Second, a two-sided characterization of removability. A coupling lemma shows that when the deficiency is a coarsening of perception, no switch can separate benefit from harm, yielding a converse (a confounded detector earns zero premium, and any within-defect policy insisting on positive premium is driven, under multiplicative dynamics, to negative long-run growth) and an achievability result (a detector outside the deficiency earns a positive premium). Together, over structured uncertainty classes with severity capped or miss rate O(1/L): a defect is profitably removable iff the detector-relevant distinction survives the restriction and the advantage condition holds; the premium is the support function of the class’s ROC set at an economic price vector. Third, observation defects and capacity defects differ exactly on whether access to the deployment distribution rescues them; the gap decomposes as cross-leak plus a closure deficit, and per-task randomization buys back the latter, never the former. The detector can be learned from declared fatal categories at a training bill linear in loss severity (up to a log factor). The results synthesize Chow’s reject option, Kelly growth under ruin, and selective prediction.
[AI-93] BAT-RM: A Boundary-Aware Transformer with Region-Aware Multi-Directional Mamba for Clinically Deployed Cervical Cancer Radiotherapy Auto-Contouring
链接: https://arxiv.org/abs/2607.11949
作者: Istiak Ahmed,Kazi Shahriar Sanjid,Galib Ahmed,Md. Tanzim Hossain,Md. Anwarul Islam,Shahrukh Khan,Md. Ashrif Rahman Arian,Md. Nishan Khan,Md. Misbah Khan,S M Hasibul Hoque,Rahnuma Shahrin Rista,Md. Jobairul Islam,Sheikh Anisul Haque,Md Arifur Rahman,Syed Md. Akram Hussain,Syeda Nashra,Sayeed Shafayet Chowdhury,Md. Mostafa Kamal Sarker,M. Monir Uddin
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI)
备注:
Abstract:We present a clinically deployed end-to-end auto-contouring system for cervical cancer radiotherapy planning, anchored by the Boundary-Aware Transformer with Region-Aware Mamba (BAT-RM), a hybrid architecture that integrates Sobel-gated boundary attention, a linear-time, multi-directional Mamba module for long-range context, and a boundary-skeleton-guided fusion gate. This design achieves linear-time complexity for long-range context modeling, avoiding the quadratic cost of full spatial self-attention. The full pipeline spans multi-institutional data collection, rigorous inter-rater quality assurance, external validation in an independent cohort, and a web-based clinical interface natively compatible with Varian, RayStation, and Monaco. Against four baselines, BAT-RM achieves superior performance across seven anatomical classes, with statistically significant improvements in target volumes, including GTV and CTV, and in organs at risk such as the rectum and bladder. A prospective multi-center reader study involving 13 radiation oncologists demonstrated that AI assistance elevates junior oncologists’ IoU from 0.899 to 0.965, approaching senior-level accuracy, while reducing contouring time by more than 80%. The system also reduced expert consultation rates and improved inter-reader consistency, reflecting gains in both efficiency and quality assurance. Following clinical deployment at a partner hospital, the system reduced patient wait times from days to hours without additional staffing, enabling same-day or next-day initiation of treatment for routine cases. BAT-RM demonstrates that a rigorous research pipeline, from data curation to clinical deployment, can translate directly into measurable patient benefit in resource-constrained settings where the demand for radiotherapy far exceeds specialist capacity.
[AI-94] Sensitivity to Subjective Expected Utility Maximization: A Methodological Study with an Illustrative Application to LLM Decision-Making
链接: https://arxiv.org/abs/2607.11920
作者: Jeff Helzner
类目: Econometrics (econ.EM); Artificial Intelligence (cs.AI); Methodology (stat.ME)
备注: 64 pages, 5 figures. Code and data archived at Zenodo: this https URL
Abstract:Evaluating decisions made under uncertainty is hard when labeled outcomes are scarce, costly, or confounded with luck. We treat subjective expected utility (SEU) maximization as a stated standard and define a graded measure – SEU sensitivity – of an agent’s conformity to it. The vehicle is a softmax choice model with a sensitivity parameter \alpha on SEU-valued alternatives; the contribution is a sequence of identifiability results for \alpha and for belief and utility parameters (\beta, \delta) , validated in Stan via prior predictive checks, parameter recovery, and simulation-based calibration (SBC), with finite-sample caveats intact. In the uncertain-choice-only model m_0 , \alpha is identifiable given the expected-utility vector \eta and sharply recovered, while (\beta, \delta) are only weakly informed: the posterior barely contracts and concentrates on a \beta - \delta trade-off. In the extended model m_1 , \delta becomes identifiable in principle via a \beta -free risky block, but its practical recovery gain at realistic sample sizes is negligible (matched-count CI-width reduction under 1%), and that block yields no detected \alpha -precision gain at matched choice count. These are two distinct phenomena: for \delta , identifiability does not imply precise estimability at realistic n ; for \alpha , identifiability is silent about what governs finite- n precision. Marginal SBC passes for both models even where the joint posterior is weakly informed – a demarcation we make precise. A two-by-two application (GPT-4o and Claude 3.5 Sonnet, each on insurance-claims triage and Ellsberg-style urns, with sampling temperature as the lever) runs end-to-end on real LLM choice data, detecting a structured comparative \alpha effect in two of four cells.
机器学习
[LG-0] he Spectrum Is Not Enough: When Context Helps Time-Series Forecasting
链接: https://arxiv.org/abs/2607.13006
作者: Mert Onur Cakiroglu,Mehmet Dalkilic,Hasan Kurban
类目: Machine Learning (cs.LG)
*备注:
Abstract:A growing family of indices scores how predictable a series is from its spectrum. Practitioners increasingly read these scores as answering a different question: whether \emphadding context, a longer lookback, a retrieval plug-in, or a pretrained model, will help. These are not the same question. The value of context is a property of the operating point, not of the series. Any index built from the power spectrum is invariant under phase randomization, whereas the beyond-second-order value that retrieval and foundation models supply is not, because a phase-randomized series is asymptotically Gaussian. We state this as an impossibility result and isolate it with surrogate pairs that fix the spectrum and the marginal by construction. We then give a label-free, configuration-level diagnostic, the coverage deficit, whose principal term measures beyond-spectrum structure as the gain of analog over linear prediction. On seven benchmarks the prediction holds: window-keyed retrieval’s value collapses across surrogate pairs (ECL median +33%!\to!-35% , p10^-40 ) while every spectral index stays frozen; a foundation model’s value splits into a surviving second-order part and a small beyond-linear margin that collapses; a longer linear window’s value survives. Leave-one-dataset-out, the structure term predicts the sign of beyond-spectrum value where the spectral indices trail it, and the reverse holds for the second-order mechanism. We introduce no new forecaster; the contribution is the distinction, a controlled comparison, and a diagnostic for the deployment decision. Code: this https URL.
[LG-1] Watermark Forensics for Generative Models: An Information-Theoretic Perspective
链接: https://arxiv.org/abs/2607.13003
作者: Xiaoyu Li,Zheng Gao,Xiaoyan Feng,Jiaojiao Jiang,Yulei Sui,Jiankun Hu
类目: Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: The abstract has been shortened to comply with arXiv’s length limit
Abstract:A watermark in a generative model’s output is usually asked only whether a text is machine-made. The same mark can do more: attribute it to the user who produced it, extract a hidden payload, or localize the part that survives editing. These form a forensic ladder, and we ask what each rung costs in the sample length n . One object organizes the answers. Let S be the secret the mark carries (a user’s identity or payload), and let the information profile \nu(t)=I(S;X_t\mid X_t) record how much the t -th token reveals about S given the earlier ones. Its total mass pays for attribution and extraction; how that mass is spread pays for localization; and detection alone is paid for not by information but by presence, the distance from the marked to the unmarked distribution. The literature’s two quality models, a mark subtle on every token and one that stamps a few tokens loudly, are two incomparable ways of capping this profile. Our main theorem settles the ladder’s entropy column. For statistically distortion-free schemes, attributing a text to one of N users costs \Theta(\log N/h) tokens over every stationary-ergodic source of entropy rate h , sharp to a (1+o(1)) factor: to our knowledge the first tight entropy-rate law for multi-user attribution (via exact alignment). The natural collision-counting analysis overcharges without bound; only a decoder thresholding each candidate by its own realized surprisal attains the rate while almost never implicating an innocent user. A matching converse makes the law two-sided, and extraction of an \ell -bit payload costs \Theta(\ell/h) . Two gaps are real, not modeling artifacts: a \Theta(\log N) -token window in which a text is provably machine-made yet unattributable, and a footprint-resolution uncertainty principle. Experiments on GPT-2, Pythia-410M, and Qwen2.5 recover the predicted constants. Comments: The abstract has been shortened to comply with arXiv’s length limit Subjects: Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG) Cite as: arXiv:2607.13003 [cs.CR] (or arXiv:2607.13003v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.13003 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-2] Efficient Sequential Calibration with O(T2/3-ε) Error Bound
链接: https://arxiv.org/abs/2607.12928
作者: Zihan Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study the online binary sequential calibration problem. A recent breakthrough by \citetdagan2024breaking overcomes the classical (T^2/3) barrier for calibration error. Building on this result, we present an efficient randomized forecaster that achieves an expected calibration error (O(T^2/3-\varepsilon)) for some constant (\varepsilon0). Our forecaster combines the \textscSPR-Calibration procedure \citepdagan2024breaking with an outer Blackwell-style correction layer. The \textscSPR-Calibration procedure controls calibration with respect to a surrogate sequence of conditional-mean estimates, while the correction layer controls the additional error incurred when these surrogates are used to approximate the true outcomes. The analysis decomposes the total calibration error into the surrogate calibration error and the residual discrepancy between the surrogate sequence and the true outcomes. The former is bounded by the \textscSPR-Calibration guarantee in \citetdagan2024breaking, and the latter is controlled using a quadratic potential argument together with the sparsity of the \textscSPR-Calibration forecaster. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.12928 [cs.LG] (or arXiv:2607.12928v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.12928 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-3] Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence
链接: https://arxiv.org/abs/2607.12916
作者: Blanca Cano-Camarero,Ángela Fernández-Pascual,José R. Dorronsoro
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-class collapse and inter-class contrast while preserving sufficient flexibility for neural networks to approximate geometrically optimal embeddings with large angular separation between classes. We provide a theoretical analysis positioning CoCo with respect to related objectives such as dot regression and cross-entropy, showing that the new proposed loss benefits from closer initialization to the optimal configuration, more informative gradients, and stronger incentives for class-wise representation collapse. Extensive experiments on diverse tabular datasets from the OpenML-CC18 benchmark show that CoCo achieves competitive performance with state-of-the-art methods, including kernel SVM, Random Forest, dot regression, and cross-entropy-based neural networks. In addition, both theoretical arguments and empirical analyses demonstrate that the proposal promotes tighter class clustering and faster convergence. These results highlight CoCo loss as an effective objective for learning discriminative representations while maintaining competitive predictive performance.
[LG-4] Energy-Based Physics-Informed Form Finding for Clustered Tensegrity Structures
链接: https://arxiv.org/abs/2607.12888
作者: Jing Qin,Muhao Chen
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Tensegrity form-finding and physical property prediction are fundamental inverse problems in structural mechanics, which aim to determine equilibrium configurations and internal force distributions. These problems are challenging due to strong nonlinearity arising from the coupling between geometry and forces, the need to ensure structural stability, and the enforcement of constraints such as boundary conditions and symmetry. Moreover, traditional methods often lack robustness to noise and outliers. This paper proposes an energy-based learning framework for clustered tensegrity form finding and physical property prediction. The proposed approach incorporates total potential energy minimization and constitutive relations into the training objective, enabling the simultaneous prediction of equilibrium nodal configurations and associated physical quantities, including member forces and force densities. By incorporating energy-based physical losses directly into the learning process, the framework improves physical consistency, robustness, and data efficiency. Numerical experiments on tensegrity structures, including prism and lander systems, show the great potential of the proposed approach and demonstrate its capability for scalable form finding and accurate prediction of structural properties.
[LG-5] Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis
链接: https://arxiv.org/abs/2607.12868
作者: Sigma Jahan
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: Accepted at ICSME Data Tool Track, 2026
Abstract:Deep learning systems often fail due to subtle implementation faults that alter training behavior. Recent work has studied how to detect and diagnose such failures from changes observed across training epochs. However, the software engineering community still lacks a public dataset of per-epoch training runs with documented fault history, feature extraction details, and clear reuse support for fault detection and diagnosis tasks. We present Deep4ge, a controlled benchmark of 14,227 training runs generated from 59 adapted TensorFlow/Keras deep neural network (DNN) programs collected from Stack Overflow. We generated faulty variants using 27 source-code transformations that introduce known faults across seven categories. The dataset contains 9,845 faulty runs and 4,382 correct baseline runs. For each run, we record 4 evaluation metrics and 26 features that measure training behavior at every epoch. These features capture weights, gradients, activations, accuracy and loss trends, learning rate, and hardware use. Deep4ge supports binary fault detection, multi-class fault diagnosis, and early fault prediction from partial training runs. We release the dataset and fault-injection framework at this https URL.
[LG-6] oward Localizing and Repairing Bias in Transformer Attention Heads
链接: https://arxiv.org/abs/2607.12863
作者: Sigma Jahan
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: Accepted in ICSME NIER track, 2026
Abstract:Transformer language models are increasingly used as software components, yet biased outputs remain difficult to localize and repair inside the model. Existing fairness testing and repair methods largely operate at the input-output or retraining level, while recent work suggests that bias-related behavior can concentrate in a small set of attention heads. This paper studies whether attention heads can be localized and repaired through a targeted inference-time intervention. We introduce ROBIN, a white-box head-level fairness debugging method that ranks attention heads using sensitivity to fairness probes and removes a small bias subspace from selected head outputs. In a four-model pilot study, ROBIN reduces the measured WinoBias gap across all models while preserving language-modeling quality better than whole-head zeroing. These preliminary results suggest that head-level bias repair should consider not only which heads are selected, but also how selected heads are modified.
[LG-7] Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storag e Control
链接: https://arxiv.org/abs/2607.12856
作者: Takumi Shioda,Kohei Terashima,Tatsuo Nagai
类目: Machine Learning (cs.LG)
*备注: 29 pages, 8 figures
Abstract:Buildings are expected to shift cooling loads in response to grid conditions. Thermal energy storage (TES) enables this shift, but scheduling it well requires planning hours ahead under storage constraints. Model predictive control (MPC) and reinforcement learning are difficult to scale across buildings. This study instead adapts an open-weight reasoning model through reinforcement learning with verifiable rewards (RLVR). We convert exact offline dynamic-programming (DP) action values into dense rewards for every candidate action. Using only 30 training prompts, reinforcement fine-tuning (RFT) trains the model as an upper-level scheduler that outputs hourly heat-pump setpoints from text-based states and forecasts. Evaluation uses a deliberately simple office-building TES benchmark where exact DP is tractable and the optimum is known. RFT reduces the open-weight model’s emissions from 70.5 to 61.2 kg-CO2, close to the DP optimum of 60.8 kg-CO2. GPT-5 nearly matches DP and MPC without task-specific training, while GPT-4o, a non-reasoning LLM, produces higher emissions than the no-storage baseline, so inference-time reasoning appears important. Trace analysis shows that RFT mainly stabilizes observable planning patterns (candidate comparison, look-ahead, and feasibility checking) rather than creating a new strategy. Robustness and generalization tests clarify what transfers: the reinforced planning patterns persist under forecast errors and an unseen TES condition and carry over to a battery task, but its different structure limits the gains. DP-based verifiable rewards offer a practical way to adapt open-weight reasoning models to building storage scheduling. These results motivate higher-fidelity tests of whole-building control and scalable verifiers for city-scale energy management.
[LG-8] Directional Constraints for Efficient Exploration in Safe Reinforcement Learning IROS
链接: https://arxiv.org/abs/2607.12784
作者: Paolo Magliano,Puze Liu,Jan Peters,Davide Tateo,Raffaello Camoriano
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: This paper has been accepted for publication at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pittsburgh, USA, 2026. 8 pages, 8 figures
Abstract:Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However, real-world deployment in open-ended environments requires strong safety guarantees to prevent dangerous or harmful behaviors. Safe Reinforcement Learning methods address this requirement by enforcing safety constraints. Nevertheless, learning under constraints often reduces learning speed and could lead to suboptimal task performance, as the agent must solve a more complex constrained optimization problem compared to unconstrained settings. To tackle this issue, in this work, we propose an extension of the ATACOM framework, a state-of-the-art reliable safety layer that can be integrated with existing Reinforcement Learning algorithms to enforce constraints derived from prior knowledge of the system or learned directly from data. Our proposed method, named ATACOM Directional Constraints (ATACOM-DC), significantly improves the safety-performance trade-off by introducing directional constraints that distinguish between actions approaching and moving away from constraint boundaries, activating constraint enforcement only when necessary. We evaluate our method across a range of challenging robotic control tasks in simulation, analyzing both constraint-violation costs and achieved task performance. Code and additional material at this https URL.
[LG-9] What Makes a Representational Prior Work? Feature Families Label-Free Invariances and Critical Windows in Grokking
链接: https://arxiv.org/abs/2607.12735
作者: Gunner Levi Howe
类目: Machine Learning (cs.LG)
*备注:
Abstract:Companion work showed the grokking delay is causally the time to form task-structured representations, injectable via a contrastive prior. Here we characterize what makes such a prior work, across four axes, in 188 new runs. Content: a coherent, learnable prior built from the wrong feature family (magnitude bands) blocks generalization like a random partition (1/15 vs 0/20 grok; p=0.43 between them), confirming the companion’s prediction that priors act at the level of the circuit’s features. Supervision: a fully label-free invariance prior – positives are commuted pairs (a,b)\sim(b,a) only – generalizes in 15/15 runs at a median 2.7\times speedup, more reliably than the label-supervised prior itself ( p=0.038 ), and combined with a weight-norm clamp yields the strongest method we test (median 17\times , 5/5) – strongest meaning reliably fast: plain cross-entropy with a clamp matches this speed only at the exact critical norm, while the prior keeps it fast across the entire clamp range. Timing: the prior is only needed early – applied solely during the first 2000 epochs (4% of budget) it generalizes 10/10 at 2.7\times , beating continuous application (8/10, 1.25\times ) and a duration-matched later window ( 2.1\times ). Setting: the dissociation replicates on modular multiplication and across depths and normalization variants, and a clamp sweep quantifies the companion’s central claim: structure injection flattens the weight-norm delay-law exponent about 17-fold (plain cross-entropy slows 31\times per +10 norm units, a lower bound as higher cells are censored, versus 1.22\times with the prior). Honest boundary: tasks that generalize before memorizing have no delay to control. Feature-family alignment decides whether a prior permits generalization; invariance content suffices for acceleration without labels; a brief early window captures nearly all of the benefit.
[LG-10] AdaPCLA: Adaptive Prior-Calibrated Logit Adjustment for Long-Tailed Longitudinal EHR Generation
链接: https://arxiv.org/abs/2607.12645
作者: Shuai Cui,Chen Wenxuan,Wenjie Du,Jian Lou,Dan Li,Wenjie Feng
类目: Machine Learning (cs.LG)
*备注: 40 pages, 10 figures
Abstract:Generative modeling of longitudinal Electronic Health Records is increasingly important for privacy-preserving research, yet standard autoregressive models tend to underrepresent the co-occurrence structure of tail events (i.e., diseases, symptoms), reducing the fidelity and faithfulness of generated data for rare subpopulations. To this end, we propose AdaPCLA framework, which enables generative models to adaptively fit and generate EHR data through a data distribution-aware training strategy; this is achieved by internalizing data knowledge parameters by simulated annealing training. It also supports training-free adaptation to a diverse clinical population for generation through zero-shot distribution control. Moreover, our theoretical analysis characterizes rare-code logit updates through the label-wise empirical NTK and derives a prior-internalization bound for how annealing speed and NTK conditioning affect retained prior signals. Experiments on real-world data show that AdaPCLA achieves consistent gains in tail plausibility, downstream utility, and zero-shot control; in particular, it improves TailPairSeen over HALO by 114.2% on MIMIC-III and 65.1% on MIMIC-IV, outperforms GPT-style generation by 3.5% F1 for zero-shot cross-population adaptation.
[LG-11] Learning Forced Multibody Dynamics on Lie Groups
链接: https://arxiv.org/abs/2607.12627
作者: Martine Dyring Hansen,Marta Ghirardelli,Elena Celledoni,David Martin de Diego,Brynjulf Owren
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS); Symplectic Geometry (math.SG)
*备注:
Abstract:We propose an architecture for learning the dynamics of mechanical systems based on discrete forced Euler-Lagrange equations on Lie groups using only position data. By formulating the dynamics directly on manifold-valued configuration spaces, the method naturally respects the geometric structure of the systems and preserves geometric invariants and conservation laws. The reliance on position measurements alone makes the framework applicable in settings where velocity data are unavailable or noisy. The approach extends naturally to multibody systems, accommodates external control inputs, and demonstrates strong performance on both synthetic and real-world datasets.
[LG-12] he Geometry of Memorization: Finite-Time Spectral Sensitivity as a Diagnostic for Flow Matching Models
链接: https://arxiv.org/abs/2607.12616
作者: Shuchan Wang
类目: Machine Learning (cs.LG)
*备注: 15 pages, 2 figures, code available at this https URL
Abstract:Continuous-time generative frameworks construct probability paths between base and target domains by optimizing time-dependent velocity fields. While theoretical targets favor straight trajectories, empirical networks develop complex path deformations. This paper presents the Finite-Time Spectral Sensitivity (FTSS) g(t), a gradient-free, forward-pass metric that exposes flow geometry by tracking the root-mean-square singular value of the state-transition matrix. Serving as a continuous proxy for stable rank, g(t) reveals a distinct geometric pathology under data scarcity: while generalizing models maintain stable effective dimensions, overfitting causes a spectral collapse. We leverage this structural phenomenon to develop an internal geometric audit based on g(t). Our framework detects generative memorization using purely internal trajectory dynamics, removing the need for external membership queries or baseline data comparison.
[LG-13] Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices
链接: https://arxiv.org/abs/2607.12599
作者: Raheen Junaid Wani,Smruti R. Sarangi
类目: Machine Learning (cs.LG)
*备注: 22 pages, 13 figures
Abstract:Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption. While recent deep-learning approaches have improved detection accuracy, many remain computationally expensive and often fail to capture subtle anomalies due to limited multi-scale sensitivity. Autoencoders are widely used for anomaly detection because they reconstruct normal patterns well, leading to elevated reconstruction errors for anomalous inputs. Their simplicity and efficiency also make them suitable lightweight backbones for handling multi-scale inputs. To address these challenges, we propose a Lightweight MultiScale AutoEncoder (LMSAE) network for univariate time-series anomaly detection, designed to be compact and computationally efficient. LMSAE leverages the Discrete Wavelet Transform (DWT) to extract multi-scale features and employs a multi-scale loss function to improve sensitivity to subtle or hidden anomalies. Experiments on benchmark datasets demonstrate competitive or superior detection performance despite using significantly fewer parameters and a model size of less than 500 KB. LMSAE also achieves low-latency, low-power inference on the NVIDIA Jetson Nano, with 9x reduction in inference latency and 2x reduction in power consumption, making it ideal for edge deployment.
[LG-14] Environment Parameter Gradient Theorem for Policy-Environment Co-Design in Reinforcement Learning
链接: https://arxiv.org/abs/2607.12590
作者: Amber Srivastava
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 6 pages, 1 figure, 1 table
Abstract:Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, however, the environment itself is alterable: physical or operational parameters can be tuned to shape the transition dynamics and costs experienced by the agent. This motivates jointly optimizing both the policy and the environment design parameters. To this end, we establish an Environment Parameter Gradient Theorem – a formal expression for the gradient of the value function with respect to environment parameters. The key theoretical device is a generalized action-value function Q_\pi,\xi(s,a,\zeta) , which comprises two copies of the environment parameters: \zeta governs the cost and transition dynamics at the current state–action pair, while \xi governs the future rollouts. This decoupling yields a tractable closed-form gradient expression and is essential to the theorem’s derivation. Building on this result, we develop a model-free algorithm that simultaneously learns the optimal policy and the environment parameters. We demonstrate the efficacy of our framework on a UAV network design problem, where the optimal UAV placement (environment parameters) and communication routes (governed by the policy) are learned jointly to minimize the total communication cost in the network.
[LG-15] From Preimage Search To Source-Grounded Feature Inversion
链接: https://arxiv.org/abs/2607.12526
作者: Kaixiang Shu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Interpreting a neural network requires understanding what its internal features extract from a particular input. Feature inversion seeks to express a selected feature in the input domain, but canonical iterative methods search for an input whose re-encoded representation matches the target. Because many inputs can satisfy this constraint, target matching alone does not specify the inverse associated with the sample that generated the feature. We formulate source-grounded feature inversion by conditioning the inverse on the source-local network geometry at the target-generating input. At each boundary of the computational DAG, backpropagation provides the correct reverse dependencies but transports an adjoint signal rather than an upstream-state estimate. We locally repair this signal with a closed-form matrix Wiener map from a mean-seed VJP to the upstream state, followed by a second Wiener map for the JVP forward-consistency residual, and compose the repaired states through the same DAG in one finite reverse pass. One calibrated zero-intercept map family supports new inputs, depths, channels, and channel groups across diverse CNN and Transformer architectures, tensor components, and visual distributions without query-specific optimisation. Matched target and source controls verify that each inverse depends on the selected feature and the local operators of the sample being explained, rather than a target-independent image template. Prediction-conditioned feature atlases align these visualisations with independent interventions on the corresponding internal features. Together, source-grounded feature inversion opens the model’s hidden feature hierarchy to inspection at the level of individual layers and channels, linking what the network extracts from an input to the internal evidence that shapes its decision.
[LG-16] What Does Goodness Measure? A Likelihood-Ratio Account of Forward-Forward Learning
链接: https://arxiv.org/abs/2607.12501
作者: Paolo Giannitrapani
类目: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Machine Learning (stat.ML)
*备注:
Abstract:The Forward-Forward (FF) algorithm trains each layer locally, so that a scalar goodness - the sum of squared activations - is high on real inputs and low on contrastive ones, with activations normalized between layers. Both choices are usually treated as heuristics. Under an explicit generative model they are not: the squared goodness is the sufficient statistic of a likelihood-ratio test between two zero-mean populations differing in scale, and the FF threshold is its boundary. It generalizes: anisotropic populations yield a Mahalanobis goodness, the plain square being its isotropic case; heavy-tailed populations yield a saturating statistic whose slope is a posterior precision - divisive normalization - with bounded evidence and an advantage only under aggregation. The same lens characterizes the inter-layer normalization: it must remove the length while preserving per-coordinate energy, explaining a depth collapse we observe under unit-norm normalization; and the pairwise objective admits a scale-inflation shortcut that a whitened goodness removes.
[LG-17] Sample Efficient Generative Optimization for Molecular Design
链接: https://arxiv.org/abs/2607.12488
作者: Sarina Kopf,Cristina Nevado,Philippe Schwaller
类目: Machine Learning (cs.LG)
*备注:
Abstract:Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly. The practical impact of an optimization method therefore hinges on its sample efficiency: how few evaluations it needs to find strong candidates. We introduce Sample Efficient Generative Optimization (SEGO), a framework for Bayesian optimization on adaptively generated molecules. In SEGO, a probabilistic surrogate model forms a hypothesis about where hits lie in chemical space, a generative model is steered to propose candidates in that region, the most promising candidate is selected via an acquisition function, and the resulting oracle call is used both to sharpen the surrogate and to anchor the generator in real reward. SEGO attains state-of-the-art performance on the practical molecular optimization (PMO) benchmark using only one tenth of the oracle calls consumed by other methods, and on a multiparameter docking task it reaches ten hits in roughly half the oracle calls of existing approaches. These gains move molecular optimization closer to campaigns driven by direct experimental feedback.
[LG-18] Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection
链接: https://arxiv.org/abs/2607.12454
作者: Martin Uray,Saverio Messineo,Roland Kwitt,Stefan Huber
类目: Machine Learning (cs.LG)
*备注: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution will be published in Computer Aided Systems Theory - EUROCAST 2026, Lecture Notes in Computer Science, Springer
Abstract:Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-specific models that are costly to train and hard to scale. Foundation Models (FMs), pre-trained on broad data with strong zero-shot generalization, have recently become available for univariate time series forecasting, raising the question of whether they can address MTSAD without task-specific training. We investigate the zero-shot application of a univariate forecasting FM, TimesFM, to industrial MTSAD on the Secure Water Treatment (SWaT) benchmark, evaluating two strategies: treating the FM as a per-feature forecaster with thresholded prediction errors, and as an embedder whose intermediate representations feed standard outlier detectors. Neither of our proposed setups is competitive with established baselines; embeddings reveal only partial separation between normal and anomalous segments, insufficient for reliable detection. The cause is that the FM is too effective at capturing temporal dynamics, yielding low error even within fully anomalous windows, so persistent anomalies become indistinguishable from normal behavior. However, these observations yield valuable insights: the error peaks at anomaly boundaries, indicating FMs reliably detect distribution changes. We conclude that the proposed naive zero-shot FMs are unsuitable for MTSAD but promising for change-point detection.
[LG-19] Fisher Rank Inflation: A Spectral Signature of Memorization under Label Noise
链接: https://arxiv.org/abs/2607.12438
作者: Satwik Bathula,Anand A. Joshi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Deep networks trained with label noise often learn clean structure before memorizing corrupted labels. We show that this transition leaves a spectral signature in the centered scatter of per-example last-layer gradients. Its effective rank transiently expands during memorization and contracts after corrupted labels are fit. We call this phenomenon Fisher Rank Inflation. Corrupted labels increase effective rank by injecting spectral mass into low-energy or previously unused eigendirections, increasing the entropy of the gradient spectrum. We derive a first-order leave-one-out attribution formula, identify conditions under which corrupted examples contribute more strongly than clean examples, and explain why attribution signals weaken once the normalized Fisher-gradient spectrum stabilizes. We test these predictions on CIFAR-10, CIFAR-100, and CIFAR-10N using SmallCNN, ResNet18, and Vision Transformers. Across settings, Fisher effective rank exhibits a consistent inflation–collapse trajectory aligned with memorization. At peak-rank checkpoints, corrupted examples are enriched among the highest rank-contributing samples, with top-100 noisy fractions from (69.2%) to (96.2%) across five-seed synthetic-corruption experiments and (94.4%\pm1.9%) on CIFAR-10N. First-order spectral attribution closely matches exact leave-one-out contributions in convolutional models and remains enriched in the Vision Transformer. Peak effective rank increases monotonically with corruption severity, from (28.88\pm1.95) under clean training to (97.09\pm1.78) at (60%) corruption. In several settings, the retrospectively identified onset of rank inflation precedes observable test degradation. These results establish Fisher Rank Inflation as a spectral signature connecting corrupted-example enrichment, corruption severity, and the transition from structure learning to memorization.
[LG-20] PolarBM: Complex-valued Boltzmann Machine for Modeling Audio Signals in Polar and Log-polar Coordinates
链接: https://arxiv.org/abs/2607.12417
作者: Toru Nakashika,Kohei Yatabe
类目: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS); Machine Learning (stat.ML)
*备注: Submitted to IEEE Trans. ASLP
Abstract:Although vast amounts of data, such as audio signal spectra, are naturally represented using complex numbers, conventional machine learning methods often simplify complex-domain problems by employing frameworks designed for real-valued variables. While this simplification offers computational benefits, it discards structural information regarding the inherent relationship between amplitude and phase. In this paper, we propose a novel Boltzmann machine (BM), named PolarBM, capable of naturally handling complex-valued variables in the polar coordinate (i.e., an amplitude-phase representation). PolarBM defines a probability density function for complex variables in which the phase explicitly depends on the amplitude, thereby capturing the physically important relationships of complex-valued signals. Furthermore, to process audio signals in accordance with human auditory perception, we propose LogPolarBM, which models amplitude on a logarithmic scale. This extension yields a flexible conditional probability density function, a power-weighted noncentral complex Gaussian (PW-NCCG) distribution, whose marginal amplitude distribution encompasses the Rice, Nakagami, and noncentral chi distributions as special cases. For practical applications, we also introduce the restricted variants of these proposed models: PolarRBM and LogPolarRBM. Experimental results demonstrate that by explicitly modeling the dependency between amplitude and phase, the proposed RBMs achieve superior modeling accuracy compared to conventional models, including deep neural networks. Although our experiments focus on audio signals, the utility of the proposed BMs is not limited to audio applications; their potential extends widely across various fields of science and engineering that involve complex-valued data, such as wireless communications and quantum mechanics.
[LG-21] Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN
链接: https://arxiv.org/abs/2607.12409
作者: Xiang Zhao,Ronghui Quan,Yaqi Xiao,Junlin Chen
类目: Machine Learning (cs.LG)
*备注: This paper consists of 21 pages, including 6 tables and 10 figures
Abstract:Parachutes are widely utilized in aviation, aerospace and lifesaving missions. As the initial stage of parachute deployment, suspension line extraction and straightening directly determines the smooth implementation of subsequent inflation procedures. This ultra-short process involves intricate dynamic load variations. Most existing studies adopt numerical integration of ordinary differential equations to calculate line tension, yet this method fails to rapidly acquire tension values at arbitrary positions along suspension lines. This paper develops a physics-informed neural network (PINN) algorithm for tension prediction during line extraction and straightening, which outperforms traditional integration methods in both computational efficiency and numerical accuracy. Furthermore, the regulatory law of binding tape parameters on line dynamic tension is investigated. Comparative validations against flight test data and conventional numerical results verify the reliability and effectiveness of the proposed PINN framework.
[LG-22] ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series
链接: https://arxiv.org/abs/2607.12391
作者: Saiyue Lyu,Zhitian Zhang,Ruizhi Deng,Thibaut Durand
类目: Machine Learning (cs.LG)
*备注: Accepted at TMLR 2026
Abstract:We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type. To address the inherent uncertainty of future events, we introduce ReDiTT, a retrieval augmented conditional diffusion transformer that operates in latent space. ReDiTT retrieves structurally similar latent sequences from a memory bank during both training and inference and incorporates them as reference conditions through cross attention. This retrieval based conditioning allows the model to attend to relevant temporal dynamics and provides global structural guidance for generation. As a result, ReDiTT stabilizes long horizon forecasting and improves sample diversity. Experiments on seven real world datasets demonstrate state of the art performance on next event prediction and long horizon forecasting. Our code is available at this https URL.
[LG-23] Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences
链接: https://arxiv.org/abs/2607.12382
作者: Arash Nikzad,Sasan Sarbishegi,Ali Dasmeh,Muhammad Asif,Parsa Gharavi,Erik Husom,Sagar Sen,Andrew B. Lehr,Olivier Penacchio,Ana Clemente,Tristan M. Stöber
类目: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:How can an agent build a structured map of its world from nothing but an ongoing sequence of raw sensory input and its own movements, especially when natural variation means exact sensory patterns rarely repeat? The Clone-Structured Causal Graph algorithm (CSCG), a normative hippocampus model, shows how an interpretable map can be learned from aliased observations. However, CSCG requires a predefined discrete alphabet, and its expectation-maximization formulation is not easily combined with existing neural network modules, preventing the end-to-end processing of raw image sequences. We remove this barrier by reformulating CSCG as a single, fully differentiable module, gradCSCG, and coupling it to a learned vector-quantized variational autoencoder (VQ-VAE) perceptual front-end. A soft emission forward pass allows the map-learning objective to flow back into perception, while a set of loss-balancing mechanisms mitigates module collapse during joint training. We demonstrate, first, that gradient training reproduces CSCG’s results on original symbolic grid worlds by recovering room topology from heavily aliased observations. Second, we show that map recovery remains robust on MNIST image sequences, where each visit to a location yields a newly sampled image of its assigned digit. Across four heavily aliased environments, the end-to-end pipeline successfully uncovers the underlying adjacency graph with high edge precision and recall, directly from visual input. This work provides a proof of principle that CSCG can serve as a composable building block in a deep learning architecture.
[LG-24] SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems
链接: https://arxiv.org/abs/2607.12380
作者: Yuxuan Ren,Fan Yang,Jianhua Yao,Yatao Bian
类目: Machine Learning (cs.LG)
*备注: conference
Abstract:Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture. Cross-domain training would mitigate per-domain data scarcity, but direct generation in 3D coordinate space cannot easily handle the heterogeneous structural priors of all three domains, and no prior latent autoencoder is simultaneously lossless and architecturally general across all three. We introduce SinAE, a single-architecture flow-matching autoencoder for molecules, crystals, and proteins, with vanilla Transformer encoder and decoder and no equivariant, graph, or domain-specific operators. Rather than requiring the encoder to capture fine-grained geometry, SinAE shifts the reconstruction burden into an iterative flow-matching decoder, achieving near-lossless reconstruction across domains and reducing reconstruction errors by orders of magnitude relative to prior latent baselines. The same per-token latent supports a standard Diffusion Transformer prior that reaches strong performance on molecular, crystal, and protein generation benchmarks. Joint molecule–crystal training strictly improves both domains, providing direct evidence of cross-domain transfer through a shared atomic latent. Code is available at this https URL .
[LG-25] Same Loss Same Noise Opposite Schedules: Noise Structure and Optimizer Normalization Jointly Determine Whether Learning-Rate Cooldown Helps
链接: https://arxiv.org/abs/2607.12360
作者: Subham Singh,Ashutosh Mishra,Subha Raut
类目: Machine Learning (cs.LG)
*备注: 11 pages, 12 figures
Abstract:The cooldown phase of a warmup-stable-decay (WSD) learning-rate schedule, now a default in large-model pretraining, lowers the final training loss in some settings and does nothing in others. We give a provable account of which case obtains, and it turns on two properties together: the structure of the gradient noise and whether the optimizer normalizes its update. On a strongly convex objective with multiplicative (gradient-proportional) noise, stochastic gradient descent contracts geometrically at a constant learning rate, so cooldown has nothing to improve. Under the same objective and noise, sign-based and normalized methods, the standard surrogates for adaptive optimizers, settle on a noise floor of order \eta^2 and reach the minimizer only as the learning rate is driven to zero; any additive noise then reinstates a floor for every method. The mechanism is elementary: an SGD step shrinks in proportion to the gradient and so anneals itself, whereas a normalized step keeps unit scale and cannot. We solve the signSGD stationary law on the quadratic exactly and obtain the floor constant in closed form, prove a local form of the dissociation under (L_0,L_1) -smoothness, extend the floor to normalized SGD in dimension d1 by a scale-invariance argument, and establish robustness to momentum and heavy-tailed noise. Simulation confirms every prediction, and we demonstrate the resulting noise-regime diagnostic on a real classification task with directly measured gradient noise. The mechanism explains whether cooldown helps; the interior cooldown fraction used at scale lies outside stationary landscape-and-noise geometry.
[LG-26] Reducing information dependency does not cause training data privacy. Adversarially non-robust features do ICLR’26
链接: https://arxiv.org/abs/2607.12354
作者: Rasmus Torp,Shailen K. Smith,Adam Breuer
类目: Machine Learning (cs.LG)
*备注: In The Fourteenth International Conference on Learning Representations (ICLR’26), 2026
Abstract:In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks. We show that extensive exposure can persist without rote memorization and is instead caused by a tunable connection to adversarial robustness. We begin by presenting three surprising results: (1) recent defenses that inhibit reconstruction by Model Inversion Attacks (MIAs), which evaluate leakage under an idealized attacker, do not reduce standard measures of information dependency (HSIC); (2) models that maximally memorize their training datasets remain robust to MIA reconstruction; and (3) models trained without seeing 97% of the training pixels, where recent information-theoretic bounds give arbitrarily strong privacy guarantees under standard assumptions, can still be devastatingly reconstructed by MIA. To explain these findings, we provide causal evidence that privacy under MIA arises from what the adversarial examples literature calls ``non-robust’’ features (generalizable but imperceptible and unstable features). We further show that recent MIA defenses obtain their privacy improvements by unintentionally shifting models toward such features. To establish this causal relationship, we introduce Anti Adversarial Training (AT-AT), a training regime that intentionally learns non-robust features to obtain both superior reconstruction defense and higher accuracy than state-of-the-art defenses. Our results revise the prevailing understanding of training data exposure and reveal a new privacy-robustness tradeoff. Comments: In The Fourteenth International Conference on Learning Representations (ICLR’26), 2026 Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.12354 [cs.LG] (or arXiv:2607.12354v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.12354 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-27] Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization
链接: https://arxiv.org/abs/2607.12349
作者: Ruoxi Gao,Jiangweizhi Peng,Ziqi Chen,Frazier N. Baker,David C. Kombo,John L. Kane Jr.,Andrew A. Scholte,Yi Li,Matthew J. LaMarche,Luigi I. Iconaru,Hans-Peter Biemann,Mingyi Hong,Xia Ning
类目: Machine Learning (cs.LG)
*备注:
Abstract:Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models follow the structure-based drug design (SBDD) paradigm, generating molecules to fit a target binding pocket. However, existing diffusion-based SBDD methods typically couple pocket and ligand representation learning, model interactions only at the atom level, and prioritize binding affinity over other developability properties. Here, we introduce conDitar-dev, a conditional diffusion-based SBDD framework for generating ligands with strong binding affinities and favorable ADMET properties. It consists of three modules: msPRL, a pretrained multi-scale pocket representation learning module; conDitar, a pocket-conditioned diffusion model guided by msPRL representations; and paOPT, a generation-time method for optimizing ligand developability. On a newly curated benchmark of human disease targets, conDitar outperforms state-of-the-art SBDD baselines, achieving an average binding score of -8.85 kcal/mol. Across five ADMET properties, conDitar-dev improves performance by up to 73% over conDitar. To further validate the abilities of conDitar-dev to generate developable molecules, we have applied it to two validated druggable targets: programmed death-ligand 1 (PD-L1) and colony-stimulating factor 1 receptor (CSF1R) proteins. Top-ranked generatively designed molecules and their analogs have been experimentally synthesized and biologically tested. Two molecules generated directly by conDitar-dev for PD-L1 exhibited SPR-derived K_D values of 3.49 and 3.75 \mu M, respectively. Hit expansion based on conDitar-dev-designed molecules identified selective CSF1R inhibitors with IC _50 values as low as 200 nM, while also uncovering opportunities for drug repositioning.
[LG-28] Robust Design of Integrated Sensing and Communication in LEO Satellite Systems
链接: https://arxiv.org/abs/2607.12337
作者: Hezhen Yang,Xiaoming Chen,Qi Wang
类目: Information Theory (cs.IT); Machine Learning (cs.LG)
*备注:
Abstract:With the growing demand for satellite sensing and communication, the limited wireless resources are difficult to support multiple satellite systems. Therefore, it is desired to investigate integrated sensing and communication (ISAC) in low Earth orbit (LEO) satellite systems to enable multi-functionality within a single satellite, thereby saving both spectrum and orbital resources. In this paper, a framework for ISAC in LEO satellite systems is established, where a satellite can simultaneously sense multiple targets and serve multiple communication users (CUs) over the same spectrum. Considering the limited onboard energy of satellite, a novel robust beamforming design algorithm is developed with the goal of minimizing total transmit power while satisfying the mean squared error (MSE) requirements for sensing and signal-to-interference-plus-noise ratio (SINR) requirements for communication in presence of channel phase uncertainty which exacerbates the cross-functional interference. According to theoretical analysis, the proposed algorithm for ISAC in LEO satellite systems is effective. Moreover, extensive simulations confirm the superiority of the proposed algorithm over baselines.
[LG-29] Gradient Flow Dynamics and Implicit Bias of Diagonal Linear Networks under Infinitesimal Initialization
链接: https://arxiv.org/abs/2607.12332
作者: Jiajie Zhao,Jianxing Wang,Junjie Yang,Zhiwei Bai,Yaoyu Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study the gradient flow dynamics of diagonal linear networks for regression tasks under infinitesimal initialization. Extending Theorem 1 from Pesme Flammarion (2023), we generalize the analysis to both deep diagonal linear networks and a broader class of two-layer diagonal linear networks (as defined in Definition 4.1). Specifically, we demonstrate that the training trajectories of these models can be equivalently characterized by the proposed Algorithm 1. We further prove that this algorithm converges to the solution of a modified \mathcall_1 norm minimization problem. As a result, we establish that the implicit bias of both network architectures corresponds to a modified \mathcall_1 norm in the regime of infinitesimal initialization. Additionally, we provide insights into the underlying mechanisms governing these dynamics by identifying the Structural Invariant Manifold (SIM) (Zhao et al., 2026) as the key geometric structure that shapes the learning process.
[LG-30] A hybrid analytical-PINN model for subsurface simulation of geothermal heat exchangers in heterogeneous underground
链接: https://arxiv.org/abs/2607.12271
作者: Moke Rao,Thomas Hamacher,Smajil Halilovic
类目: Machine Learning (cs.LG)
*备注: 29 pages, 18 figures
Abstract:In this paper, a parametric physics-informed neural network for solving the heterogeneous soil thermal problem with borehole heat exchangers (BHEs) as singular sources is developed. There are three novel features in the present framework; namely, (i) the singularity is naturally removed by using analytical line source models; (ii) using the explicit formulation for gradient thermal conductivity enables physics-informed learning of the parametrization featuring the conductivity; (iii) the learned correction is utilized as an efficient universal corrector via superposition principles. We first introduce the decomposition of the temperature change and transform the approximation of the entire heterogeneous response to the correction compensating the difference between the practical solution and idealized homogeneous approximation. In such a way, the delta function singularity is excluded and the bulk heat transfer is captured for the sake of facilitating the effective training of the neural network. The original problem is then reformulated as a governing correction diffusion or advection-diffusion equation subject to a homogeneous initial condition. The linearly varying thermal conductivity is used to model the soil heterogeneity. We propose a physics-informed neural network to approximate a universal corrector with respect to a single borehole with unit heat extraction rate. As a result, the network is trained by minimizing the physics-informed and data-anchored loss function that is evaluated for sampled conductivity parameters on adaptively selected training points. In addition, we include the location indicator function regarding the source as a feature input of network and find that it helps the network to process the local information. We perform numerical tests to exhibit the effectiveness of the proposed method based on three different analytical models.
[LG-31] Quantum Port-Hamiltonian Neural Networks: Learning Conservative and Dissipative Dynamics via Measurement-Induced Nonlinearity
链接: https://arxiv.org/abs/2607.12269
作者: Dibakar Sigdel
类目: Machine Learning (cs.LG)
*备注:
Abstract:We introduce Quantum Port-Hamiltonian Neural Networks (Q-pHNNs), a family of parameterised quantum circuits that learn classical dynamics in a structure-preserving manner. The framework relies on the Isomorphic Hamiltonian Mapping (IHM): the skew-symmetric interconnection matrix \mathbfJ corresponds to unitary gate evolution, and the positive-semidefinite dissipation matrix \mathbfR corresponds to Measurement-Induced NonLinearity (MINL) realised via mid-circuit measurement and classical feedforward. This ensures conservation and passivity are enforced by construction rather than penalty terms. We instantiate the IHM in four architectures: (1) a Quantum HNN that learns conservative energy manifolds and extracts Hamilton’s equations exactly via the Parameter-Shift Rule; (2) a Q-pHNN using Born-rule measurement for dissipation; (3) a Q-pHNN jointly learning the energy ansatz and damping coefficient; and (4) a topology-entangled Quantum Graph Neural Network for N -node coupled-phasor networks. Experiments on the nonlinear pendulum and damped harmonic oscillator demonstrate: (i)~ 1.35% relative energy drift with a symplectic integrator and scale correction; (ii)~ 100% energy monotonicity for the MINL circuit; and (iii)~ 12.1% error in damping-coefficient identification from vector-field snapshots with no direct supervision on the damping coefficient.
[LG-32] Saturation Makes Quantization Error Additive: A Coverag e Model with a Certificate
链接: https://arxiv.org/abs/2607.12266
作者: Joshua Hill
类目: Machine Learning (cs.LG)
*备注: 39 pages, 7 figures, 17 tables
Abstract:Mixed-precision quantization must decide which parts of a model to keep at higher precision. A common premise, shared by sensitivity-based methods such as HAWQ and CoopQ, is that the loss from quantizing a set of layers can be reconstructed from per-layer or pairwise sensitivities measured in isolation. We test this premise at the 4-bit weight-and-activation precisions now being deployed, treating the change in loss f(S) from quantizing a layer set S as a set function on the Boolean cube and analyzing it through two classical changes of basis. This analysis yields two findings. First, across configurations drawn from the deployment distribution, 85–93% of the variance of f is explained by per-layer effects alone. Second, a monotone transform of a sum of per-layer terms reproduces f 's ranking of configurations, misordering at most 2% of pairs. We propose the coverage model f(S)=c\bigl(1-\prod_i\in S(1-a_i)\bigr) , which reproduces the measured variance profile of f to within a few percent from its L fitted break-rates. This structure supports two predictors of a configuration’s loss, each with L+1 parameters. The additive model is the optimal first-order predictor. By Parseval’s identity its mean-squared error equals the variance of f left unexplained by per-layer effects, which we measure on full lattices, estimate out of sample at full-network scale, and report with every result as a certificate of how well any additive model can do. The coverage model itself is the second predictor. As allocators at matched memory, they attain the lowest KL divergence among the compared allocators on models from 30B to 355B parameters. Below four bits, the resulting allocations continue to solve code and reasoning tasks at budgets where allocations from gradient sensitivities no longer produce terminating generations. Comments: 39 pages, 7 figures, 17 tables Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.12266 [cs.LG] (or arXiv:2607.12266v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.12266 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-33] From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models
链接: https://arxiv.org/abs/2607.12260
作者: Muhammad Ashad Kabir,Sirajam Munira
类目: Machine Learning (cs.LG)
*备注: Author-prepared preprint. The Version of Record was published in AIME 2026, Springer, and is available via the DOI
Abstract:Early screening of chronic kidney disease (CKD) is essential for preventing irreversible progression; however, many machine learning (ML)-based screening methods remain difficult to deploy in community and resource-limited screening settings due to their reliance on large labeled datasets, resource-intensive pathology tests, or high-dimensional clinical features, and limited robustness to population and distributional shifts. This study examines the feasibility of using large language models (LLMs) for early-stage CKD screening in a zero-shot setting, without dataset-specific training. We propose a feature-guided zero-shot framework that evaluates LLM performance using a selected set of clinically meaningful, readily available community-based features, rather than exhaustive clinical inputs. Feature selection was guided by ML-based analysis to identify a compact, clinically relevant subset of variables. Tabular patient records were subsequently serialized into text using standardized prompt templates to enable zero-shot inference. The zero-shot performance of four LLMs (LLaMA-3, Qwen-3, Mistral, and GPT-4o-mini) was evaluated using both the full feature set and the selected subset. Generalizability was assessed across three heterogeneous CKD datasets spanning three countries. Across models and datasets, the selected feature set yielded consistent and statistically significant improvements in balanced accuracy and probability estimates, achieving performance levels suitable for screening purposes. These findings suggest that LLMs can support clinically meaningful, training-free CKD screening using minimal community-accessible patient features, offering a practical complement to conventional ML methods in real-world screening contexts.
[LG-34] Understanding Structured Health Data through Interaction-Aware Mixture-of-Experts
链接: https://arxiv.org/abs/2607.12255
作者: Ji Hwan Park,Ying Ding,Tianjin Guo
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study interaction-aware mixture-of-experts for post-stroke rigidity prediction using multi-level views of structured health records. Despite minimal performance gains, routing attribution reveals systematic importance differences across views, underscoring view construction as key to interpretability.
[LG-35] Proximity Features: Privacy-Compliant Cold-Start Personalization at Airbnb KDD2026
链接: https://arxiv.org/abs/2607.12246
作者: Wei Jiang,Bin Xu,Hui Gao,Bharathi Thangamani,Weiwei Guo,Sundar Srinivasavaradhan,Tracy Yu,Huiji Gao,Michael Kinoti
类目: Machine Learning (cs.LG)
*备注: 8 pages, 3 figures, 2 tables. Accepted for poster presentation at the KDD 2026 Workshop on Two-sided Marketplace Optimization (TSMO 2026)
Abstract:Personalization in two-sided marketplaces relies heavily on user-level features, yet for platforms with infrequent, high-consideration purchases, a large fraction of users lack sufficient history for effective recommendation, spanning both paid and organic channels. At Airbnb, a substantial share of search requests comes from logged-out or first-time users, with this challenge especially pronounced on paid-channel landing pages, leaving traditional user-level features unavailable for a large fraction of traffic. Privacy regulations and increasing restrictions on third-party cookies further limit identifier-based tracking for non-essential use cases. This paper introduces Proximity Features, a privacy-compliant feature system that groups users by geographic proximity using geo-IP data and an adaptive clustering algorithm, producing aggregated user-level signals for groups of approximately 1,000 nearby users without requiring a persistent individual identifier at inference time. Privacy is preserved by design: the pipeline operates on consented, aggregated data only within consent-gated privacy controls. The system is deployed in production at Airbnb, serving multiple surfaces including marketing landing pages and destination recommendation, with engagement emails integration under way. Online A/B experiments demonstrate statistically significant lifts in bookings, with the largest gains observed among users with absent or stale history. Comments: 8 pages, 3 figures, 2 tables. Accepted for poster presentation at the KDD 2026 Workshop on Two-sided Marketplace Optimization (TSMO 2026) Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.12246 [cs.LG] (or arXiv:2607.12246v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.12246 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-36] Cluster-Weighted EDMD
链接: https://arxiv.org/abs/2607.12243
作者: Lorenzo Tomaz,Judd Rosenblatt,Flavio Kicis,Thomas B. Jones,Diogo Schwerz de Lucena
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: Accepted at the International Conference on Scientific Computing and Machine Learning 2026 (SCML2026)
Abstract:Extended Dynamic Mode Decomposition (EDMD) approximates Koopman operators from data, but a single global operator is inefficient when different state-space regions exhibit distinct local dynamics. We introduce Cluster-Weighted EDMD (CW-EDMD), which jointly learns a soft phase-space partition and a per-cluster EDMD operator. Its Expectation-Maximization (EM) objective assigns each transition based on both geometric proximity and prediction residuals, so clusters specialize where local Koopman models are accurate rather than where the data are dense. On Lorenz, damped pendulum, and Duffing systems, across 36 configurations and 10 seeds, CW-EDMD improves matched-degree EDMD in one-step and 5s-rollout prediction. Across 288 paired comparisons, there are significant error reductions in 258 cases, increases in 4, and no differences in 26. Median one-step error reductions are 57x, 2.7x, and 12x on pendulum, Duffing, and Lorenz, respectively.
[LG-37] Gradient-Free Topology Adaptation for Power Flow Surrogates via In-Context Whitening
链接: https://arxiv.org/abs/2607.12241
作者: Ayushi Jolotia,Parikshit Pareek
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 10+6 Pages
Abstract:Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of magnitude of accuracy when a line outage changes the topology. This degradation is an operator shift. The altered admittance matrix changes the input-to-output map, so identical inputs yield a different output distribution. Existing methods correct this with target-topology data and per-topology gradient steps. We ask whether the correction can instead be made statistical and gradient-free. We propose In-Context Whitening (ICW), which trains an ACPF surrogate in an output space whitened by the base topology’s first two moments, and adapts it to an unseen N-1 or N-2 topology by re-estimating that whitening from a few hundred solved cases on the new topology. This adaptation is gradient-free, weight-free, and architecture-agnostic. We prove that among affine whiteners the unique choice that preserves the coordinate-wise semantics of the physical output vector is ZCA whitening, so within efficient invertible corrections, two moments are sufficient. Across the IEEE 30-, 118-, and 300-bus systems under N-1 and N-2 contingencies, ICW reduces overall error by 6 \times to 28 \times over frozen surrogates (up to 54 \times per-quantity under N-2) and cuts worst-bus power-balance mismatch by up to 30 \times , with consistent gains across three backbones. At deployment scale it matches or beats gradient-based adaptation in accuracy while adapting 21 \times to 34 \times faster, with a cost that parallelizes on commodity CPU cores rather than requiring one GPU per contingency.
[LG-38] Forgetful Attention: A Trainable Support-Vector Memory with Certified Selection and Exact Unlearning
链接: https://arxiv.org/abs/2607.12204
作者: Vishwajith Ramesh
类目: Machine Learning (cs.LG)
*备注: 21 pages, 6 figures, 10 tables. Code: this https URL
Abstract:Attention can be viewed as an online learner over context, yet existing test-time memories cannot certify that dropping a token leaves outputs unchanged or delete its influence outright. We introduce Support Vector Attention (SV-Attention), a max-margin memory whose weights are support coefficients of a one-class SVM with fixed box parameter C. Its active-set partition gives reserve tokens exactly zero weight, certifying output-preserving eviction; a reversible incremental solver deletes a token to recover the state produced by retraining without it under the same C. In fp64 experiments, decrement and refit recover identical partitions whenever the optimum is unique, and their decision functions match to a median deviation of about 10^-9 (10^-13 on learned keys); the 10^-2 worst case is confined to ill-conditioned duplicates and remains below coefficient decay in every regime. The exact path reuses the maintained KKT inverse in a custom backward. Training uses a separate stabilized batched approximation and does not carry the exact-deletion certificate; it reaches 9,125 tokens/s on a 3.22M-parameter model, while remaining 35.8 times slower than an MPS softmax reference. At matched budgets, certified selection reaches 0.86 vs. 0.32 rare-item recall and retains 0.80 vs. 0.05 deterioration hours on real MIMIC-IV streams. We also demonstrate surgical forgetting, exact editing, patient-record deletion, and a forgettable retrieval memory over real sentence embeddings. On enwik8, the hybrid obtains 2.178 BPC vs. 2.383 for a matched-state sliding-window Transformer across seven seeds (8.6% paired improvement, p=0.001); a three-seed TinyStories result is directionally positive but not significant (p=0.057).
[LG-39] Decentralized Gradient Descent: Bottleneck Regimes and Budget Complexity
链接: https://arxiv.org/abs/2607.12172
作者: Nicolò Michelusi
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: Submitted to IEEE Transactions on Information Theory
Abstract:Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents. While its convergence properties are well understood, less is known about the communication and computation resources required to attain a prescribed accuracy. In this paper, we study DGD from a resource-aware perspective and characterize the communication-computation budget required to attain a target error level. We develop a bottleneck-centric framework in which different factors dominate the optimization dynamics at different error scales. Specifically, we identify operating regimes governed by initialization, objective heterogeneity and network connectivity, gradient noise, and communication noise. To capture these effects, we introduce two fundamental quantities: the gradient-Diversity-to-Network-connectivity Ratio (DNR) and the Gradient-to-Communication-noise Ratio (GCR). We show that these quantities determine the sequence of bottlenecks encountered during optimization and the corresponding budget-optimal operating strategy. Using a multi-stage analysis, we derive optimal stepsize selections and explicit budget-complexity bounds that quantify the budget resources required to attain a prescribed accuracy. The resulting expressions reveal how the overall budget decomposes into contributions associated with successive bottlenecks and provide insight into the fundamental tradeoffs among objective heterogeneity, network connectivity, gradient noise, and communication noise.
[LG-40] From Geometric Recovery to Causal Validation: A Reproducible Audit of Sparse Autoencoder Features from Superposition Geometry to Causal Inertness
链接: https://arxiv.org/abs/2607.12166
作者: Mohamed Abdessalem Bal
类目: Machine Learning (cs.LG)
*备注: 21 pages, 8 figures, 6 tables. Code and reproduction pipeline: this https URL
Abstract:Sparse autoencoders (SAEs) are the standard for decomposing superposed neural representations into interpretable features, and evaluation relies predominantly on correlational recovery metrics – cosine similarity between ground-truth directions and decoder atoms. We show this conflates two distinct claims: decoder-geometry alignment and encoder-activation behavior. We reproduce the superposition phase diagram of Elhage et al. (2022), identifying a convergence artifact at high sparsity and an under-described diffuse sharing regime at extreme overcompleteness. We reproduce the TopK-versus-L1 comparison of Gao et al. (2024), with direct evidence of L1 shrinkage. Our central result is causal: subjecting every recovered feature to ablation and steering, we find up to 77% of features passing a recovery bar (cosine = 0.90) in a degraded SAE – and 9% in a well-trained one – are causally inert: the matched atom never fires when the feature is present, including matches at cosine ~1.000. We package the method as sae-causal-audit, a model-agnostic instrument with a deterministic pipeline. Re-auditing refines the finding: inertness decomposes by cause into structural inertness (antipodal-pair geometry, present in good SAEs) and competitive inertness (a TopK pathology of degraded SAEs), and by direction into read- and write-inertness, which five antipodal pairs dissociate completely – unmonitorable yet steerable through the same atom, with steering specificities of 143-310 attached to zero ablation effects. We document why byte-exact reproducibility is unavailable by construction, and propose reporting it as a stack of claims with explicit scopes. Applying the instrument to a production SAE reproduces the pattern at small scale (14% inert) and surfaces an atom-collision signal: a handful of atoms recur as the nearest match for dozens of unrelated concepts, replicated across three batches.
[LG-41] Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling
链接: https://arxiv.org/abs/2607.12143
作者: Yulong Yang,Clara O’Farrell,Christine Allen-Blanchette
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: Presented at 28th AIAA Aerodynamic Decelerator Systems Conference and Seminar
Abstract:Accurately modeling the dynamics of planetary parachute and entry vehicle systems is critical for Entry, Descent, and Landing events such as vehicle separation and sensor activation. These dynamics are difficult to capture with traditional system-identification methods as parachute motion is highly nonlinear, the governing equations are not fully known, and relevant test data are scarce and expensive to acquire. In this work, we sidestep these challenges by leveraging a physics-aware generative modeling approach that learns parachute dynamics directly from data. The proposed method, Symplectic Parachute Generative Adversarial Network (SPar-GAN), adapts a Hamiltonian generative architecture to the parachute setting by conditioning on canopy design and freestream velocity, while enforcing conservation of energy through symplectic integration. We apply SPar-GAN to subscale parachute tests conducted at the National Full-Scale Aerodynamics Complex and show that it reproduces qualitatively accurate pitch-yaw dynamics of different parachute configurations while recovering a compact two-degree-of-freedom phase-space consistent with canopy axisymmetry. These results suggest that physics-constrained generative models can characterize parachute dynamics across operating conditions and may help reduce the volume of physical testing required to assess performance.
[LG-42] An Agent ic AI Scientific Community for Automated Neural Operator Discovery
链接: https://arxiv.org/abs/2607.12122
作者: Luis Loo,Ulisses Braga-Neto
类目: Machine Learning (cs.LG)
*备注:
Abstract:We present an agentic approach to autonomous neural operator discovery based on an AI scientific community, which consists of a swarm of virtual laboratories that interact under a citation-based economy of influence. Highly-cited labs found new labs that follow their research direction and replace non-performing labs. Each virtual lab contains three agents: an LLM planner that proposes an architecture, a numerical worker that trains and measures it, and an LLM reviewer that participates in cross-lab peer review. All labs share a common vocabulary consisting of DeepONet (branch-trunk), Fourier, Transformer (attention), wavelet, and residual convolutional neural operator building blocks. We evaluate the neural operator AI scientific community on five problems, namely piecewise regression, the linear advection and Burgers 1D PDEs, and the Navier-Stokes and Darcy flow 2D PDEs, while repeating the simulation three times for each problem. The results show that the neural operator AI scientific community is capable of discovering high-accuracy, low-parameter-count neural operator architectures. All 9,623 LLM calls are logged and audited, which reveals that the virtual lab LLM planners choose to hybridize in 99.8% of their logged decisions, consistently returning multi-family hybrids. Moreover, we conducted an ablation study by replacing the LLM agents in each lab by rule-based alternatives, which caused the scientific community to collapse to non-hybridized single-family stacks in several cases, showing that LLM agency is needed to preserve diversity. The results suggest a no-free-lunch theorem for neural operators: there is no universal winner. The code, configurations, and the complete LLM transcripts are released at this https URL.
[LG-43] FlashDiff: Efficient Regional Execution and Scheduling for Diffusion Model Serving
链接: https://arxiv.org/abs/2607.12121
作者: Yaqi Qiao,Ping He,Songrun Xie,Ayush Barik,Chensong Zhang,Zhengzhong Tu,Fan Lai
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Diffusion models have become the central backbone for modern image, video, and audio generation, but their efficient service remains a challenge. Unlike autoregressive decoding, diffusion inference repeatedly updates high-dimensional spatial or temporal latents over many denoising steps. This all-region execution pattern makes generation latency high and limits serving throughput. Existing multi-GPU parallelization methods can reduce per-step computation, but often introduce substantial activation exchange overhead, causing communication to offset or even outweigh the benefits of parallel execution. This paper presents FlashDiff, a diffusion serving system that improves inference efficiency through adaptive regional execution and scheduling. FlashDiff is based on the observation that diffusion refinement is not uniform across latent regions or denoising steps: different regions often stabilize at different rates, while neighboring steps exhibit strong temporal correlation. FlashDiff leverages these properties to selectively execute only regions that require further refinement and to reallocate the resulting compute slack across concurrent serving requests. FlashDiff consists of three mechanisms. First, it decomposes the latent representation into coherent execution regions using early-stage attention signals, preserving semantic structure while exposing fine-grained parallelism. Second, it uses a lightweight runtime controller to estimate region activity and bypass low-impact updates when further refinement is unlikely to affect output quality. Third, it applies an affinity-aware online scheduler that co-locates dependent regions, balances residual load across GPUs, and reuses reclaimed compute capacity to improve serving efficiency. Across real-world image, video, and audio workloads, FlashDiff reduces end-to-end serving latency by 30-97% and improves throughput by 1.2-2.2x. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2607.12121 [cs.DC] (or arXiv:2607.12121v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.12121 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-44] Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design
链接: https://arxiv.org/abs/2607.12105
作者: Yifei Chen,Shihan Lu,Ed Colgate,Kevin Lynch
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 25 pages, 15 figures, 9 tables
Abstract:In-hand manipulation without external sensing is challenging due to uncertainties from finger-object contacts and disturbances by gravity. While reinforcement learning has shown promise in learning complex finger gaiting, existing approaches do not prioritize maintaining well-conditioned grasps for sustained manipulation. We introduce two complementary physics priors for robust in-hand rolling: a global grasp-quality prior derived from classical grasp analysis and a local contact-geometry prior based on fingertip curvature. The grasp-quality prior is used as a dense reward-shaping term that encourages well-distributed contacts with improved worst-case wrench resistance. The contact-geometry prior is expressed in the fingertip geometry that mechanically shapes the contact interface toward task-aligned rolling while reducing off-axis drift. We evaluate the effect of these priors on learning in-hand rolling manipulation for a multifingered robotic hand manipulating three different objects at four palm orientations. Results show significant improvement in rotation efficiency, grasp stability, and disturbance rejection, suggesting that physics priors embedded in both learning and fingertip morphology improve task robustness and sim-to-real transfer. An overview video can be found at this https URL.
[LG-45] raceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models
链接: https://arxiv.org/abs/2607.12104
作者: Yuvraj Sehgal,Sneh Patel,Mahsa Panahandeh,Naser Ezzati-Jivan,Francois Tetreault
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: 11 pages, 2 figures, 6 tables. Author’s accepted version. Published in the Industry Track of the 34th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE Companion '26), Montreal, QC, Canada. Code: this https URL
Abstract:Machine learning models for system diagnostics rely on kernel execution traces to capture fine-grained system behavior, but collecting production traces in industrial systems is costly due to runtime overhead, storage demands, and privacy constraints. We present TraceSynth, a diffusion-based framework for generating synthetic kernel traces that augment limited real data for downstream ML tasks. TraceSynth models traces as multi-channel sequences (event types, timestamps, CPU affinity, thread identifiers, and process metadata) using a Transformer-based denoising diffusion process with constraint-guided repair to enforce system invariants. Across six benchmarks, results show strong workload dependence. For deterministic, compute-heavy workloads (scimark2), synthetic augmentation achieves 87.2% F1-Macro at context length L=4096, only 2.6 percentage points below real-only baselines. Context length is the dominant quality factor, with L=4096 yielding a +104% relative improvement over L=256, while constraint-guided repair improves synthetic data quality by up to 4.3%. Ablation studies show that lightweight 2-channel models retain 97-99% of the performance of full 6-channel models at roughly half the computational cost. TraceSynth supports cost-effective augmentation of kernel execution traces in production observability pipelines and helps identify when synthetic data can substitute for limited real traces.
[LG-46] Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs
链接: https://arxiv.org/abs/2607.12067
作者: Emad Izadifar,Zahed Rahmati
类目: Machine Learning (cs.LG)
*备注:
Abstract:Institutional equity holdings disclosed in SEC Form 13F filings provide a rich temporal record of portfolio decisions by large investment managers. However, forecasting future allocations and modeling future demand remains challenging due to disclosure lags, reporting noise, and strong persistence in institutional behavior. We introduce the first benchmark for these tasks using temporal graph machine learning, framing holdings prediction as node affinity prediction – i.e., forecasting portfolio weights – on a discrete-time temporal bipartite graph of managers and securities extracted from preprocessed filings. On a sampled dataset comprising 99 managers and the S\P 500 index (503 securities, 209,351 temporal edges across 48 quarters from 2013–2025), Node Affinity prediction model using Virtual State (NAVIS) achieves a state-of-the-art test Normalized Discounted Cumulative Gain (NDCG) of 0.9127 with features (0.9121 without), outperforming all dynamic graph representation learning competitors by a substantial margin, and outperforming all heuristic methods. Remarkably, a simple Exponential Moving Average baseline achieves 0.8882, surpassing all dynamic graph models except NAVIS and all heuristics except Persistent Forecast (0.8891), highlighting the strong smoothness and persistence of institutional portfolios. Domain-specific node features provide only marginal gains (1.2%), indicating that temporal and structural signals in the 13F ownership graph already capture most of the predictable information. By benchmarking a suite of Temporal Graph Benchmark (TGB) models under the node affinity prediction setting, both with and without features, on real-world 13F data, this work provides a reproducible foundation for temporal graph machine learning in holdings prediction and portfolio allocation.
[LG-47] LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention
链接: https://arxiv.org/abs/2607.11976
作者: Ziqi Yin,Jianyang Gao,Peiqi Yin,Jiangneng Li,Gao Cong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Indexer-TopK, the operation to compute the scores and select the top-k candidates, is widely used by sparse attention kernels in large language models and vector retrieval in recommendation systems and vector databases. However, existing GPU-based Indexer-TopK kernels like DeepSeek Sparse Attention (DSA) remain inefficient due to excessive global memory traffic, costly synchronization, and prohibitive memory overhead. In this work, we exploit the curse of dimensionality in high-dimensional spaces, where distances between high-dimensional vectors tend to concentrate within a narrow range, to design LITETOPK, a novel and efficient fused Indexer-TopK kernel. LITETOPK first samples a small subset of data to estimate query-data score ranges, then uses these estimates to partition candidate results into bins online. This organization allows the LITETOPK kernel to maintain a tight approximate threshold, write back only promising candidates, reduce unnecessary I/O, substantially lower memory overhead, and still preserve exact Top-k correctness. Experimental results show that LITETOPK accelerates the prefill stage of GLM 5.2 by 1.2x in real-world deployment scenarios while incurring lower memory overhead.
[LG-48] LIDAR-AD: A Decoder-Free Latent-Interaction Dreamer with Action-Residual Chains for Autonomous Driving
链接: https://arxiv.org/abs/2607.11964
作者: Yongzhi Liu,Yang Xiao,Zhong Cao,Zeng Kang,Sunan Zhang,Zhaozhi Dong,Guojun Yu,Weichao Zhuang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Autonomous driving requires long-horizon closedloop decision making in dynamic traffic environments. Latent world models offer an effective framework for this problem by enabling imagination-based decision making in compact latent spaces. However, multi-source observations contain controlirrelevant redundancy, whereas reliable driving decisions rely on risk-relevant relations, future dynamics, and continuous action adjustments. This mismatch makes observation reconstruction and absolute action modeling suboptimal for learning decisionrelevant latent dynamics. We propose LIDAR-AD, a decoderfree Latent-Interaction Dreamer with Action-Residual Chains for autonomous driving. LIDAR-AD replaces observation reconstruction with redundancy-reduced latent alignment, encouraging compact representations of risk-relevant relations in multi-source driving inputs. It further models vehicle control as residual action updates and uses residual-action sequence contrastive learning to align multi-step residual-driven rollouts with future latent states. A deterministic analysis shows that the latent-tanh residual parameterization preserves interior action reachability while representing smooth long-horizon control as compact local updates. Together, these designs improve risk-aware state abstraction, continuous-control modeling, and long-horizon dynamics prediction. Extensive experiments across diverse simulated driving scenarios demonstrate that LIDAR-AD consistently outperforms world-model baselines, achieving the highest reward and the best success rate among learning-based methods. Evaluations on nuPlan-derived log-reconstructed scenarios further demonstrate the transferability of LIDAR-AD under real-world traffic layouts.
[LG-49] Constructed Reality Contested Priors: Decoupling and the Architecture of Cognitive Relapse Under the Free Energy Principle
链接: https://arxiv.org/abs/2607.11958
作者: MD Ibrahim Hossain Ridoy
类目: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
*备注: 12 pages, 1 figure
Abstract:Under the free energy principle, a predictive system does not observe reality directly; it maintains a generative model of the world and experiences that model’s best current hypothesis. Can a synthetic environment be made consistent enough that a predictive system’s own inference machinery adopts it as this default hypothesis, permanently displacing the environment that first shaped it? We call this state ontological inversion. Because inducing and monitoring such a transition in a nervous system is neither ethical nor technically feasible, we study the underlying computational problem through a controlled proxy: a convolutional variational autoencoder paired with a recurrent latent predictor, whose evidence lower bound objective is mathematically identical, up to sign, to variational free energy itself. The network is trained first on a baseline visual domain, then on a mixed stream in which a swept rehearsal ratio r controls how much baseline content persists during transition to a target domain. Representational capacity, what the latent space can discriminate, is tracked separately from default behavior, what the system generates when left unconstrained. Across a full sweep of 90 runs, the two diverge sharply: representational accuracy stays near ceiling, 0.97 to 0.998, regardless of r, while default behavior spans nearly the system’s entire range depending on r alone, a decoupling of learning from acceptance. More strikingly, at intermediate r the system’s default output rises toward the target domain, then partially reverts toward the baseline while training continues unchanged, a structural failure we term cognitive relapse. Resistance to reality-adoption is not reducible to learning speed; it is a structural property with its own distinct failure modes, established here as a computational existence proof and nothing further.
[LG-50] When Does Reward Teach State? A Hidden-Automaton Instrument and the Group-Language Boundary
链接: https://arxiv.org/abs/2607.11953
作者: Jim Allchin
类目: Machine Learning (cs.LG)
*备注: 14 pages, 2 figures, 5 tables (8-page main text; the remainder is references and appendices). Reproduction code and the complete per-run data behind every table and figure are attached as an arXiv ancillary file ( this http URL ). Includes a pre-registered control that overturned an initial over-strong claim
Abstract:Does a reinforcement-learning agent that earns high reward represent its task’s latent state, or only a reward-correlated shortcut? The question is usually unanswerable: the “true state” is undefined. We make it exactly answerable with a white-box instrument: express the task as a hidden deterministic finite automaton (DFA), let the agent observe a symbol stream and intermittently choose the next symbol under partial control, and grant one sparse terminal reward for acceptance. Knowing the automaton gives two things for free: the optimal return (so reward becomes an interpretable normalized score) and the exact latent state at every step (so we can probe the agent’s representation without ever showing it). Reward success and latent-state learning thus become separately measured quantities whose coupling is governed by three controllable axes. Optimizer strength: under weak on-policy RL the agent earns reward with the state probe at chance for every architecture, tempting the conclusion that sparse RL cannot install latent state; a pre-registered control overturns it – PPO+GAE recovers the state, but only partially and with high seed variance. Task structure: permutation (group-language) structure is a warning sign computable from the transition function before any training, and held out on 153 capacity-controlled fresh automata it flags perception gaps at precision 0.86 (89 of 103), in one direction only. Observation informativeness: a label-free auxiliary is vacuous when observations carry no state and recovers it in proportion to how much they reveal. The payoff is a distinction reward-only evaluation cannot make: a perception gap (latent state not linearly recoverable, though representable) versus a planning gap (state recoverable but unused). High reward is thus not evidence of task understanding; whether an agent recovers latent state is predictable in advance.
[LG-51] Scalable Optimal Transport Algorithm for Network Alignment FAST
链接: https://arxiv.org/abs/2607.11952
作者: Elaheh Hassani,Durga Mandarapu,Qi Yu,Hanghang Tong,Ariful Azad
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 10 pages, 7 figures. Code available at this https URL
Abstract:Network alignment identifies node correspondences across different networks and is a fundamental primitive in many data science applications, including social network analysis, fraud detection, and knowledge graph integration. However, state-of-the-art network alignment methods often achieve high accuracy by repeatedly constructing and updating dense matrices, sacrificing scalability in the process. To address this scalability limitation without compromising alignment accuracy, we present FastAlign, a scalable, sparsity-aware framework for optimal transport-based network alignment. Rather than introducing a new alignment model, FastAlign preserves the original OT formulation and reinterprets its computation as a set of recurring mixed sparse-dense operations. FastAlign combines sparsity-aware graph computation with domain-specific kernel fusion, including a custom SpMM kernel. Our results show that FastAlign achieves alignment quality comparable to state-of-the-art OT-based methods while substantially reducing end-to-end runtime up to 3.89x-9.45x on CPU and 2.24x-32.54x on GPU.
[LG-52] Mirror Horizon: Viable Path Entropy as a Measure of Bounded Reflection
链接: https://arxiv.org/abs/2607.11937
作者: Tiantian Zhang(Crystal)
类目: Machine Learning (cs.LG)
*备注:
Abstract:Mirror Theory proposes that an intelligent system should be studied not only by what it represents, but by what coherent continuations it can sustain under repeated reflection. We make this claim operational through \emphviable path entropy (VPE), a finite-budget measure of verified continuation capacity. Given a mirror state, a rollout protocol, a verifier, and a mode map, VPE decomposes bounded capability into two parts: the probability of reaching a viable continuation and the diversity of verified continuation modes reached among successful rollouts. This paper restores the full theoretical scaffold behind the measure: intuition as local underdetermining constraint, taste as invariant-selecting pressure, reflection as taste-guided resolution of underdetermination, and geometry as the learned structure that makes future reflection stable. We then instantiate the theory in language-model reasoning experiments on GSM8K. Across Qwen2.5-Instruct models, 32 sampled rollouts per problem, and two reflection horizons, increasing the token budget from 96 to 160 substantially expands verified reachability, reduces zero-reachability, increases verified-mode entropy, and improves smoothed VPE. At 160 tokens, Qwen2.5-1.5B realizes the strongest mirror horizon among the tested models, even though Qwen2.5-3B has more parameters. This shows that mirror horizon is not parameter count, but accessible verified continuation capacity under a bounded reflection protocol. The result supports Mirror Theory as a measure-level account: capability is the structure of viable continuations made reachable, not merely one-shot accuracy or pass@k.
[LG-53] Qubit-Efficient Quantum Search for Hyperdimensional Decomposition via Logarithmic Encoding
链接: https://arxiv.org/abs/2607.11936
作者: Sanggeon Yun,Hyunwoo Oh,Ryozo Masukawa,Raheeb Hassan,Mohsen Imani
类目: Machine Learning (cs.LG)
*备注: Accepted to ICCAD 2026
Abstract:Hyperdimensional Computing (HDC) represents symbols using high-dimensional hypervectors of dimension D . In hypervector decomposition, the objective is to recover F constituent hypervectors, each drawn from a codebook of size N , from a bound target hypervector. This requires searching over N^F candidate tuples, making the task computationally prohibitive at scale. Recent quantum approach provides a quadratic search advantage, but typically rely on qubit-inefficient O(D) -qubit hypervector representations. We propose a qubit-efficient quantum framework for HDC decomposition that reduces the representation cost to O(\log D) . The framework introduces logarithmic hypervector and binding encodings, together with a reversible hypervector lookup operator for circuit-level manipulation of dense hypervectors. Combined with a modified Dürr-Høyer search procedure, the method preserves O(\sqrtN^F) search complexity while substantially reducing qubit usage. Experimental results validate correct similarity computation, accurate decomposition in executable regimes, and significantly improved qubit scaling over baselines based on explicit D -qubit hypervector encodings, achieving up to 2,000\times fewer qubits.
[LG-54] Repairing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry
链接: https://arxiv.org/abs/2607.11928
作者: Adam Haroon,Cody Fleming,Beiwen Li
类目: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
*备注: 23 pages, 8 figures. Part 2 of a two-part study. Part 1 (diagnosis): arXiv:2606.17093
Abstract:Single-shot fringe projection profilometry (FPP) networks that regress depth directly can exploit a shape-prior shortcut, recovering depth from object boundaries rather than from fringe phase. On a photorealistic synthetic benchmark (15,600 fringe images, 50 objects at 1.5-2.1 m standoff), the best such UNet baseline plateaus at 14.54 mm object mean absolute error (MAE), and neither more data nor more capacity removes the shortcut, because neither changes the hypothesis space the optimizer searches. We introduce PhiCalNet, which outputs a wrapped-phase representation (\sin\phi, \cos\phi) and maps it to depth through a fixed differentiable calibration layer, removing the shape-prior solution architecturally rather than by a loss penalty. Because the single-shot mapping is non-injective without fringe order, PhiCalNet takes the fringe order as auxiliary input, an assumption a sensitivity analysis shows tolerates realistic decoding error; a physics-informed (PINN) baseline with the same physics as a soft penalty yields no gain, isolating the architectural choice as the operative factor. PhiCalNet reduces object MAE 3.3x to 4.46 mm, its residual confined to 0.103% of pixels at the \pm\pi wrap discontinuity, and a three-frame extension reaches 1.16 mm. Two checks agree: interpretability makes phase the most decodable internal feature, and pixel-wise conformal uncertainty quantification, to our knowledge the first for FPP, localizes error at the same discontinuity, where rejecting the top 5% of pixels by snapshot disagreement cuts root-mean-square error by 64% versus 3.5% for the baseline.
[LG-55] Near-Optimal Learning of Gaussian Sobolev Operators
链接: https://arxiv.org/abs/2607.11921
作者: Ben Adcock,Michael Griebel,Gregor Maier
类目: Numerical Analysis (math.NA); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注:
Abstract:A key question in operator learning is how to design surrogate operators with provable approximation guarantees in reasonable computational time. Whereas smooth operators can be approximated efficiently, i.e., with at least algebraic convergence in the amount of training data, learning finitely regular operators is known to be less efficient. The reason is an intrinsic curse of sample complexity, which allows only subalgebraic sample complexity rates. This fact makes it all the more important to develop algorithms which provably achieve these rates. In this work, we present a fully data-driven algorithm, termed Hermite-PCA approximation, for learning Gaussian Sobolev operators with near-optimal sample complexity. It employs principal component analysis and weighted least-squares methods and is therefore computationally efficient. Moreover, it is spectral, in the sense that it achieves faster (and near-optimal) convergence the higher the Sobolev regularity. We provide a full error analysis of this algorithm, taking into account all sources of error, along with numerical experiments that verify our theoretical results and empirically confirm the efficacy of Hermite-PCA approximation for learning Sobolev operators.
[LG-56] Semidirect Fourier Delta Attention: Phase-Controlled Delta Memory with Constructive Chunk-WY Kernels
链接: https://arxiv.org/abs/2607.11897
作者: Tiantian Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Linear attention replaces softmax attention’s growing KV cache with a fixed recurrent state, but this compression limits exact state tracking and long-context memory. We introduce \emphSemidirect Fourier Delta Attention (SFDA), a phase-controlled generalization of Kimi Delta Attention that replaces real diagonal decay with block-rotational Fourier control: [ S_t=(I-\beta_t k_tk_t^)\Lambda_tS_t-1+\beta_tk_tv_t^, \qquad \Lambda_t=\diag(\alpha_t\odot e^i\theta_t). ] Our main result is a constructive chunk-WY factorization for products (A_t=\Lambda_t-u_tr_t^), giving [ A_t\cdots A_1=\Gamma_t-Y_tM_tW_t^ ] with rank growth bounded inside fixed chunks. This yields an exact affine chunk transfer, formal stability and complexity bounds, and a compact characterization of phase-plus-low-rank memory. We verify the algebra numerically and show in toy state-tracking experiments that SFDA learns cyclic memory where the phase-disabled KDA baseline remains near chance. Fused kernels and large-scale language-model comparisons are left to future work.
[LG-57] A Shortcut to Statistically Steady-State Turbulence with Flow Matching
链接: https://arxiv.org/abs/2607.13022
作者: Gianluca Galletti,Gerald Gutenbrunner,William Hornsby,Lorenzo Zanisi,Naomi Carey,Stanislas Pamela,Johannes Brandstetter,Fabian Paischer
类目: Plasma Physics (physics.plasm-ph); Machine Learning (cs.LG)
*备注:
Abstract:Many nonlinear physical systems exhibit an initial transient phase in which perturbations grow before nonlinear interactions lead to a statistically steady state. While this saturated regime is of primary interest, direct numerical simulations must resolve the full transient dynamics before reaching it, incurring significant computational cost. In Computational Fluid Dynamics, reduced-order approaches such as Large Eddy Simulation mitigate computational cost by modeling small-scale dynamics, enabling tractable approximations of turbulent flows. In contrast, for systems such as gyrokinetics, comparably effective closures for the full dynamics are not generally available, and high-fidelity simulations remain necessary. Existing surrogate modeling approaches for these systems are autoregressive, hence they suffer from accumulating error. We instead propose to bypass explicit time evolution by directly modeling the distribution of saturated states under an ergodicity assumption, stating that ensemble averages over samples are equivalent to time averages of a single long simulation. We introduce GyroFlow, a latent generative model that directly estimates steady-state statistics of gyrokinetic turbulence in 5D phase space, without resolving the transient phase. GyroFlow generates saturated snapshots from noise, conditioned on dimensionless operating parameters and outperforms autoregressive, reduced-order, and other generative approaches, while providing substantial speedup. To evaluate generation quality we propose FGyD, a distributional metric computed in the latent space of a pretrained gyrokinetic model, and show that it correlates with downstream flux accuracy and solver convergence. Finally, GyroFlow can be used to warm-start the numerical code used to produce the data.
[LG-58] Ensemble Controlled-Flow Filtering for Implicit Data Assimilation
链接: https://arxiv.org/abs/2607.12975
作者: Zhuoyuan Li,Yue Zhao,Ming Li
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Numerical Analysis (math.NA); Optimization and Control (math.OC)
*备注: 26 pages
Abstract:Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations. Many observation mechanisms, however, are many-to-one, implicit, non-smooth, or accessible only through simulation, and need not provide the residual structures or likelihood guidance required by existing ensemble filters. We introduce implicit data assimilation, in which the analysis law is defined as an energy tilt of the forecast distribution. We then propose the Ensemble Controlled-flow Filter (EnCF), which realizes this update through a stochastic controlled flow and learns the observation-dependent control by adjoint matching from terminal energy gradients. For simulator-defined observations, EnCF-LF learns a surrogate conditional energy from samples and applies the same controlled-flow solver. We prove ideal exactness, derive a one-step error decomposition, and establish non-accumulation of local errors under filter stability. Numerical results show that Kalman-type filters remain preferable for smooth additive-Gaussian observations, while the proposed methods are better suited to non-Gaussian, many-to-one, multimodal, and implicit observation models.
[LG-59] Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis
链接: https://arxiv.org/abs/2607.12954
作者: Dandan Chen,Yan Zhao,Xuepeng Chen
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:
Abstract:Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally correlated, state dependent, and physically coupled across variables. Existing evaluations, however, often rely on perfect forecast assumptions or simplistic perturbations that do not reflect these characteristics. This study presents a physically constrained robustness evaluation framework based on simulation, using virtual PV power as a controlled response variable to isolate the propagation of input uncertainty from confounders at the plant level. Six representative machine learning and deep sequence models, including PatchTST, GRU, N-HITS, and LightGBM, are evaluated under dynamic NWP perturbations with heteroscedasticity modulated by clear-sky conditions and Erbs reconstruction that preserves radiation consistency. The results show that sequence models provide stronger noise filtering and temporal resilience than a strong tabular baseline under medium to high disturbance regimes. SHapley Additive exPlanations (SHAP) and Integrated Gradients (IG) further support a feature reallocation tendency at the case level, in which predictive reliance shifts from corrupted future forecasts toward more stable historical observations and deterministic physical priors. A Pareto analysis of accuracy under clean conditions, robustness, and computational latency then translates these findings into engineering implications for robustness assessment and model selection under forecast uncertainty.
[LG-60] LatentFlow: A General Framework for Conditioning Stochastic Processes
链接: https://arxiv.org/abs/2607.12922
作者: Louis Sharrock,Lachlan Astfalck,Henry Moss
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box information, and global constraints all induce intractable conditional laws, requiring bespoke, model-specific constructions. We introduce LatentFlow, a single framework for conditioning stochastic processes, with no learned neural approximations and no training. Our starting point is to write the stochastic process as the deterministic image of a tractable latent innovation, f_0 = T_\vartheta(\xi_0) , with \xi_0 sampled from a simple reference distribution. This reduces process-level conditioning to latent-space inference: pull the likelihood back through T_\vartheta , sample the resulting latent law with a tractable guided probability flow, and push the samples forward. This construction is provably exact at the level of the target law; in practice, approximation enters only through finite terminal noising, Monte Carlo guidance, and time discretisation of the continuous-time dynamics, each of which is explicit and systematically reducible. As LatentFlow is training-free, conditioning reduces to solving a single reverse-time SDE. This enables conditional sampling in seconds on a single desktop CPU across model classes that have never shared a scalable method: classical spatial priors, nonlinear stochastic dynamics, mechanistic models from the physical and life sciences, stochastic PDEs, heavy-tails and extremes, point and discrete-state processes, and neural or simulator-defined processes.
[LG-61] Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo
链接: https://arxiv.org/abs/2607.12902
作者: Siddharth Mitra,Vishwak Srinivasan,Xiuyuan Wang,Andre Wibisono
类目: Machine Learning (stat.ML); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST); Computation (stat.CO)
*备注: 74 pages
Abstract:We show the Randomized Hamiltonian Monte Carlo (RHMC) algorithm has accelerated mixing time guarantees for sampling from log-concave probability distributions. RHMC proceeds by repeatedly simulating the continuous-time Hamiltonian dynamics for some random integration times, and resetting the velocity to be an independent Gaussian random variable between each simulation. We show that when the target distribution is log-concave and satisfies an \alpha -Talagrand inequality (for example, if the target distribution is \alpha -strongly log-concave), if we use a random integration time from either the triangular or the exponential distribution with mean \Theta(\alpha^-1/2) , then RHMC converges exponentially fast in KL divergence, and the total integration time to reach error \varepsilon in KL divergence scales as O(\alpha^-1/2 \log(\varepsilon^-1)) . We also show that when the target distribution is log-concave, if we use a sequence of random integration times from the triangular distribution with exponentially increasing means, then the total integration time to reach error \varepsilon in KL divergence scales as O(\varepsilon^-1/2) . Our analysis relies on a bound on the average KL divergence along Hamiltonian dynamics, which is inspired by an analogous result on accelerated optimization methods based on Hamiltonian dynamics.
[LG-62] ANGLE: Angular Neural Generative Learning via Engression
链接: https://arxiv.org/abs/2607.12833
作者: Rajdeep Pathak,Archi Roy,Tanujit Chakraborty
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regression targets the conditional mean, which is often geometrically misleading for circular responses under multimodal, skewed, or asymmetric data structures. To address these limitations, a lightweight deep generative framework, namely ANGLE, is introduced for non-parametric distributional regression on the circle. The full conditional distribution of an angular response, given Euclidean and circular covariates, is learned through a generative map optimized via a generalized circular energy score (GCES) loss. Desirable theoretical properties, including the strict propriety of the loss and the rotational equivariance of the estimators, are established. Furthermore, both pre- and post-additive noise models are accommodated. A unified toolbox is provided for advancing previously underexplored challenges in circular statistics: extrapolation, sufficient dimension reduction, and conditional distribution equality testing. The framework’s efficacy is demonstrated through extensive simulations and real-world applications. Specifically, the proposal is utilized for object pose estimation from imagery and wind direction prediction, which are integral to surveillance, autonomous vehicles, and energy systems, respectively. Superior predictive performance and robust uncertainty quantification of the proposed method in these tasks are revealed.
[LG-63] Learning-enabled Acceleration of Scenario-based Model Predictive Control
链接: https://arxiv.org/abs/2607.12775
作者: Trinh Tran,Binh Nguyen,Truong X. Nghiem
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing control actions over multiple predicted scenarios. However, its computational complexity increases rapidly with the number of scenarios and prediction horizon, limiting is applicability to real-time planning and control. This paper presents a learning-accelerated Alternating Direction Method of Multipliers (ADMM) algorithm for efficiently solving SBMPC problems by leveraging parallel computing and Moreau envelope learning, while maintaining high solution accuracy. We reformulate the SBMPC problems into consensus forms that can be decomposed via ADMM, separating the scenario-dependent dynamics from non-anticipativity constraints and enabling parallel updates across scenarios and time steps. Building on this decomposition, we utilize existing learning-to-optimize schemes, which leverages Moreau envelope learning of the cost function to accelerate the primal update in ADMM, thereby reducing computation time. The proposed framework is evaluated on a microgrid energy management problem subject to load and renewable generation uncertainties. Comparisons with IPOPT and MadNLP, popular and modern nonlinear programming solvers, demonstrate substantial computational speedups while maintaining reliable closed-loop control performance.
[LG-64] Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
链接: https://arxiv.org/abs/2607.12726
作者: Jorge Alda,Jacobo Asorey,Alejandro Mir,Siannah Peñaranda
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG)
*备注: 30 pages, 14 figures, 4 tables. Submitted to Universe
Abstract:Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions. In this work, we present a Machine Learning framework designed to emulate complex, often non-Gaussian likelihood landscapes using gradient-boosted regression trees (XGBoost). We discuss the advantages of the Machine Learning approach in terms of computational efficiency and the resolution of confidence regions, particularly in scenarios with complex correlations or “curved” degeneracies. We validate this methodology by applying it to a recent analysis on flavour anomalies in semileptonic B meson decays and discussing the adaptability of this framework to other phenomenological systems, such as axion-like particles or cosmology global fits. Finally, we utilise SHAP (Shapley Additive exPlanations) values to provide a transparent analysis of feature importance, ensuring that the Machine Learning predictions remain physically interpretable and consistent with the underlying physics.
[LG-65] Gradient-free learning of a closed-loop wall controller for turbulent drag reduction
链接: https://arxiv.org/abs/2607.12626
作者: Giorgio Maria Cavallazzi,Miguel Pérez Cuadrado,Alfredo Pinelli
类目: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
*备注:
Abstract:Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain. We recently showed that such policies are also prone to saturated bang-bang actuations that collapse into standing streamwise waves whose scale is set by the computational box rather than by the near-wall cycle, and proposed architectural fixes that avoid these degeneracies. Here, we employ Evolution Strategy (ES) to optimise a recurrent closed-loop controller directly on a large turbulent channel at \mathitRe_\tau\simeq180 , evaluating policy performance over full flow episodes using an energy-aware criterion and processing candidate policies in parallel. To our knowledge, this is the first application of an evolution strategy to the control of a turbulent flow. The ES controller reduces the skin friction by about 26% , exceeding the gradient-based multi-agent controller of Cavallazzi et al. (2026), GRU-MARL, trained on a minimal box ( 17% ), and marginally exceeding classic opposition control (OC, 22% ). A wall-normal decomposition of the friction, Reynolds-stress profiles and anisotropy invariants show that the ES and opposition-controlled flows follow separate trajectories through the buffer layer, reaching comparable drag reduction by different reorganisations of the near-wall turbulence. In particular, the ES actuation correlates predominantly with the streamwise velocity fluctuations rather than with the wall-normal velocity that classical OC targets.
[LG-66] hompson Sampling Is 2-Competitive for Mistakes
链接: https://arxiv.org/abs/2607.12389
作者: Mark Sellke,Gregory Valiant
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 10 pages
Abstract:We consider Bayesian bandit models and prove that Thompson sampling makes at most twice the expected number of mistakes (selections of a suboptimal arm) as any other policy. Our analysis applies as long as the latent arm processes are independent and each arm evolves only when played. For stochastic bandits with best arm defined via mean reward, this confirms a conjecture of Guha and Munagala from 2014, where the factor 2 is already best possible. The result holds under any nonincreasing sequence of round weights, including fixed horizon and geometric discounting.
[LG-67] When Directional Accuracy Lies: A Base-Rate-Honest Benchmark for LoRA-Adapted TimesFM on Equity Forecasting
链接: https://arxiv.org/abs/2607.12248
作者: Taizhen Cheung,SA Kwon
类目: atistical Finance (q-fin.ST); Machine Learning (cs.LG)
*备注: 10 pages, 4 figures
Abstract:Large pretrained time-series models such as TimesFM are attractive for financial forecasting, but raw directional accuracy is a misleading scoreboard in equity markets. An early LoRA adapter in this project appeared to reach roughly 80% directional accuracy; we show this is not evidence of skill. Over a long horizon in a rising market, a trivial “always-up” rule attains comparably high accuracy without using the input at all. To separate genuine skill from this base-rate artifact, we build a reproducible, frozen-data benchmark with expanding walk-forward folds, a stratified held-out-ticker split, honest baselines (zero-shot TimesFM, always-up, random-walk, persistence, AR(1)), and paired significance tests (McNemar, Diebold-Mariano) under Benjamini-Hochberg FDR control. We apply the identical method to two universes – a tech-heavy NASDAQ-100 and a broad SP 500 – reporting excess accuracy over the always-up base rate. Three findings replicate. First, when the historical ~80% condition is recreated, the high number is a base rate of ~0.70 that the fine-tuned model scores below. Second, pooled LoRA shows no directional skill over the base rate at any horizon on either universe (negative at the six-month horizon). Third, per-sector specialization is significantly worse than a single pooled adapter (Diebold-Mariano p0.001 on held-out stocks at h=128). Fine-tuning’s only measurable benefit is a statistically significant reduction in point-forecast error relative to zero-shot TimesFM, which nonetheless does not beat naive baselines and confers no tradeable directional edge. The contribution is methodological: a defensible, fully seeded protocol that prevents the base-rate trap, together with the replicated negative result it produces.
[LG-68] Falsifying Causal Graphs With Outlier Events UAI2026
链接: https://arxiv.org/abs/2607.12145
作者: William Roy Orchard,Philipp M. Faller,Dominik Janzing
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Accepted at the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026)
Abstract:True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess whether a given causal graph is good in the absence of a ground truth. We propose falsifying candidate causal graphs based on whether they can explain the propagation of an outlier event. Our approach leverages a key principle: weak outliers rarely cause strong ones. While this principle has previously been used in root cause analysis to identify root causes without prior knowledge of the graph, we turn it on its head and use it to falsify candidate causal graphs whose implied outlier propagation is inconsistent with the data. To this end, we present the first statistical tests for the hypothesis that a candidate graph is the true causal graph, and show they have false positive control, power guarantees against incorrect causal graphs, and can operate with a single outlier sample.
[LG-69] Dynamic Online Processor-Native Inference for State Estimation
链接: https://arxiv.org/abs/2607.12095
作者: Orestis Kaparounakis
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models. Yet the computation of the likelihood remains an essential bottleneck for accurate posteriors and performant inference. This paper presents a Bayesian filtering technique that uses processor-native uncertainty tracking for both uncertainty propagation and inference. The technique implements deterministic hierarchical importance restructuring through a native operation, giving deterministic latency and bounded memory use for arbitrary models written as program code. Benchmarks across three nonlinear state-space systems compare the approach against particle filters and Monte-Carlo-based likelihood estimators. The technique enables deterministic approximate filtering with as high as 805 \times average speedup against direct Monte Carlo work at matched result quality for model evaluation, and Pareto-dominant accuracy-latency trade-offs for posterior inference while remaining competitive in RMSE with baseline particle filters.
[LG-70] Learning the Graphical Nature of Symmetries
链接: https://arxiv.org/abs/2607.12026
作者: Rashid Barket,Enrico Grimaldi,Yacoub Hendi,Edward Hirst,Adam Onus,Harmeet Singh
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Combinatorics (math.CO); Group Theory (math.GR)
*备注: 32 pages; 18 figures
Abstract:Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured, compared, and learned. In this paper, a dataset of 131,406 Cayley graphs is constructed, covering all groups of order at most 767 except order 512 , recording exact algebraic labels for group properties together with a broad collection of graph, cycle, distance, and spectral statistics. This census aims to provide novel benchmarks for studying how finite-group properties are reflected in Cayley graph observables. It also yields new enumerative contributions: alongside recovering known OEIS sequences for standard group classes, new sequences for monolithic groups and for groups generated by at most three, four, and five elements are contributed to the OEIS. The accompanying network analysis identifies several empirical regularities and formulates testable conjectures, including relationships involving square clustering, Cayley graph diameter, average graph disorder, and spectral eigengaps of nilpotent groups. Finally, a comparison between classical models, an MLP, and graph neural network architectures is performed for predicting algebraic group properties directly from Cayley graph data. The results show that engineered graph statistics are highly informative, while GNNs, especially GIN and in some fixed-order settings GCN, can recover substantial structural signal directly from the graph. Such that graph-aware architectures show phases of optimality on these group-theoretic graph representations.
[LG-71] VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
链接: https://arxiv.org/abs/2607.11985
作者: Anton Firc,Martin Perešíni,Vojtěch Mrázek,Kamil Malinka,Vojtěch Staněk,Zbyněk Lička,Nouhaila Innan,Walid El Maouaki,Alberto Marchisio,Muhammad Shafique
类目: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
*备注: Accepted at IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2026), San Jose, CA, USA. 9 pages
Abstract:Hybrid quantum-classical machine learning workflows repeatedly evaluate many small parametrized circuits during training and model exploration. In this regime, framework dispatch and orchestration overhead often dominate runtime. Prior simulators accelerate execution but leave open the question of when compile-once specialization is the right choice for static variational circuits. We answer this question with VQCSim, a compile-once, PyTorch-native statevector execution path with native autograd. In a systematic MQT Bench study, VQCSim compiles all tested static circuits and provides 87.7% end-to-end semantic validation. Across a five-GPU evaluation set, VQCSim delivers pooled median speedups of 4.49x for native inference and 26.78x for native training, while retaining a 3.31x advantage under matched finite-difference training. Ablation identifies native autograd as the dominant source of acceleration (27.6x), with compile-once caching and batch vectorization contributing additional gains. The speedup trades higher GPU memory (VQCSim is memory-limited at the high end) for lower runtime. We derive a hardware-aware regime map and release vqcsim-oracle, an open-source backend selector with 91.1%-97.7% top-1 agreement (including cross-GPU transfers), enabling automatic simulator selection in QML design loops.
[LG-72] Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection
链接: https://arxiv.org/abs/2607.11969
作者: Zongye Lyu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 14 pages, 4 figures. Code and OSF pre-registration links to be added in a later version
Abstract:Point-adjustment (PA), long the default scoring protocol in time-series anomaly detection (TSAD), was shown by Kim et al. (2022) to award near-perfect F1 to random scores. The field migrated to replacement metrics: PA%K, range-based precision/recall, affiliation precision/recall, and Volume-Under-the-Surface (VUS) ROC/PR. But did the fix work? Every robustness check to date is proposer-run or theoretical; no independent, adversarial, SOTA-relative audit on real benchmarks exists. We provide one. We stress-test 12 adopted metrics against trivial and adversarial no-skill score generators on the 250-series UCR Anomaly Archive (primary) plus five further benchmarks (SMD, SMAP, MSL, NAB, PSM), under a pre-registered SOTA-relative gameability criterion (a metric is gamed on a series if a no-skill generator reaches =90% of the best real detector’s score). The fix is only partial: affiliation-F1 (gamed on 99% of series) and every ROC-based metric tested (all roughly 62-64%) are gamed by no-skill detectors, genuinely so (real detectors score 0.97-1.00 on the very series they game), while the PR-based metrics and PA%K resist (14-18%). A decisive, one-directional McNemar split: VUS-ROC is gamed on 119 series where its sibling VUS-PR is not, and the reverse on none. The ROC-over-PR direction replicates across all five further benchmarks and never reverses, but the absolute gameability rate is benchmark-dependent and no single metric stays safe on all six (even VUS-PR is gamed on NAB). We release a pip-installable stress-test harness with all metric implementations vendored at frozen commits, plus a metric-selection decision protocol: prefer a PR-based metric or PA%K, treat affiliation-F1 and the ROC-AUC variants as gameable, and verify per benchmark rather than assuming robustness.
[LG-73] Benchmarking Sensor Robustness in Plasma Diagnostic Models: A Systematic Evaluation on TokaMark
链接: https://arxiv.org/abs/2607.11915
作者: Neerav Gupta
类目: Plasma Physics (physics.plasm-ph); Machine Learning (cs.LG)
*备注: 15 pages, 9 figures. Code and data available at this https URL
Abstract:Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics fail regularly: acquisition systems start late, individual sensors die, and signal dropouts cluster precisely when a plasma disruption is approaching. We present the first systematic robustness benchmark for plasma diagnostic ML using the TokaMark dataset of 11,573 MAST shots, evaluating XGBoost, LSTM, Transformer, and the TokaMark CNN baseline across six physically-grounded failure scenarios and three imputation strategies. We introduce the Robustness Score (RS) for standardized cross-architecture comparison. Our central finding is that disruption-proximate sensor failure (corruption injected in the final window timesteps) collapses sequence model performance (LSTM +212% NRMSE) while a statistical feature model remains comparatively stable (XGBoost +37%). Forward-fill imputation eliminates nearly all degradation from random dropout for sequence models (LSTM +57% to ~0%), but offers little help when the end of the window is corrupted. Shot-level alarm evaluation using ground-truth disruption timestamps reveals that LSTM alarm detection collapses to TPR=0.00 under proximate sensor failure, while mean-fill imputation recovers it to TPR=1.00, a reversal of the pattern observed in NRMSE. Plasma current emerges as the single most critical diagnostic across all architectures (+73% to +140% upon removal). Code, data, and trained checkpoints are available at this https URL.
[LG-74] Predictive Modeling of High-Altitude Clear Air Turbulence in the United States: A Machine Learning Approach
链接: https://arxiv.org/abs/2607.11899
作者: Kadir Gokdeniz,Irem Ulku
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:
Abstract:High-altitude Clear Air Turbulence (CAT) poses significant risks to aviation safety due to its unpredictability and challenges in detection. This study leverages machine learning models to improve CAT prediction within U.S. airspace at 200-350 hPa pressure levels, utilizing Pilot Reports (PIREPs), ERA5 reanalysis data, and aircraft aerodynamic parameters from the BADA database. Gradient boosting algorithms, particularly XGBoost, achieved the highest performance with an AUC of 0.904, demonstrating superior capability in capturing non-linear atmospheric dynamics. Key findings highlight the dominance of geographic coordinates (17.5% feature importance) and turbulence indices like TI3 in prediction, emphasizing the role of regional topography and upper-tropospheric instability. The integration of aerodynamic features such as drag force and wing loading improved the detection of moderate-to-severe perceived turbulence intensity (POD improved from 0.845 to 0.866), providing additional value to traditional aircraft-independent methods. Seasonal analysis revealed winter months as peak periods for CAT incidents, correlating with jet stream activity. While results align with global studies, limitations include geographic scope and aircraft-type diversity. This research underscores the potential of machine learning for operational CAT forecasting, with recommendations for future work focusing on global data integration and real-time telemetry to address climate-driven turbulence trends.
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