本篇博文主要内容为 2026-09-17 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 (2026-09-17)
今日共更新846篇论文,其中:
- 自然语言处理共129篇(Computation and Language (cs.CL))
- 人工智能共206篇(Artificial Intelligence (cs.AI))
- 计算机视觉共119篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共220篇(Machine Learning (cs.LG))
- 多智能体系统共10篇(Multiagent Systems (cs.MA))
- 信息检索共17篇(Information Retrieval (cs.IR))
- 人机交互共32篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Cognitive Extensions for Dual-Process Language Agents : Memory and Self-Reflection in Interactive Environments
【速读】：该论文旨在解决语言智能体在交互环境中表现脆弱的问题，核心挑战在于实现长时程状态追踪、有效动作执行以及对失败步骤的恢复能力。其解决方案的关键在于引入两个模块化认知扩展：自适应记忆模块（Adaptive Memory Module, AMM），通过显著性门控的情景记忆存储与触发式检索机制增强记忆利用效率；以及自我反思模块（Self-Reflection Module, SRM），通过有限执行时间内的验证与纠错干预实现运行时控制。二者均基于同一执行底座以功能开关形式集成，支持在ScienceWorld环境中的可控消融实验。实验结果表明，完整系统在平均最终得分（64.62）、成功率（43.17%）和成功步骤效率（19.33步）上表现最优，其中SRM作为独立组件贡献最大，揭示出在该任务设定下，运行时控制是主要瓶颈，而情景记忆的价值仅在运行时循环稳定后才得以充分显现。
链接: https://arxiv.org/abs/2609.19128
作者: João Meneses dos Santos,Arlindo L. Oliveira
机构: Instituto Superior Técnico, Universidade de Lisboa(里斯本理工学院，里斯本大学); INESC-ID(里斯本研究所-创新与开发)
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 13 pages, 1 figure
Abstract:Language agents remain brittle in interactive environments, where success requires long-horizon state tracking, valid action execution, and recovery from failed steps. We extend SwiftSage, a dual-process agent that combines a fast action proposer with a slower planner, using two modular cognitive extensions: an Adaptive Memory Module (AMM) for salience-gated episodic storage and trigger-driven retrieval, and a Self-Reflection Module (SRM) for bounded execution-time validation and corrective intervention. Both modules are implemented as feature-flagged extensions over the same execution substrate, enabling controlled ablations on ScienceWorld. Across four configurations—baseline, baseline+AMM, baseline+SRM, and the full system—the full system achieves the best mean final score (64.62), success rate (43.17%), and successful-step efficiency (19.33 steps), while SRM is the strongest standalone contributor. The results suggest that execution-time control is the dominant bottleneck in this setting, while episodic memory becomes most useful once the runtime loop is stabilized.
[MA-1] Flag Game: A Toy Model for Mechanistic Swarm Interpretability
【速读】：该论文旨在解决生成式AI代理在群体协作中因信念快速形成与传播而引发的集体行为失控问题，尤其是其潜在的安全风险。核心挑战在于缺乏对集体信念形成机制的机理理解，进而难以实现群体对齐（collective alignment）。为此，作者提出“旗帜游戏”（Flag Game）这一简化模型，模拟个体在仅能观测局部信息的情况下，通过社会性信息交换构建对全局真相（隐藏的国家旗帜）的认知。尽管模型结构简单，却能复现丰富的集体现象：如性能随种群规模呈现非单调变化、引入社会意识提示和团队多样性可提升准确率、组织结构具有显著影响等。研究发现，在小规模群体中，集体信念易发生“崩溃”；随着群体规模扩大，信念逐渐演化为“极化”，导致性能下降但产生信念多样性。为揭示背后机制，论文提出两种互补方法：一是“社会电路归因”（social circuit attribution），用于识别对集体动态最具影响力的个体及其观点，并通过因果干预验证；二是针对大规模群体发展统计力学理论，成功拟合了经验相图。两项成果共同推进了“群体可解释性科学”的建立，即从个体属性与通信模式出发，解析涌现的集体行为机制。
链接: https://arxiv.org/abs/2609.19124
作者: Elizabeth Pavlova,Hidenori Tanaka
机构: Harvard University (哈佛大学); NTT Research, Inc. (NTT 研究公司)
类目: Artificial Intelligence (cs.AI); Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Multiagent Systems (cs.MA); Physics and Society (physics.soc-ph)
备注: 21 pages, 10 figures
Abstract:Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formation and spread of beliefs about the world, and mechanistic understanding is crucial for collective alignment. To this end, we introduce the Flag Game, a toy model for studying the mechanisms of collective belief formation. Concretely, a hidden country flag defines the ground truth, and each bounded agent directly observes only a private crop but can exchange beliefs and weigh social evidence from peers. Despite its simplicity, the Flag Game reproduces rich collective phenomenology: non-monotonic scaling of performance with population size, accuracy gains from social-awareness prompting and team diversity, and strong effects of organizational structure. In particular, we identify that collective belief collapse at small population sizes turns into collective belief polarization as the population grows. This polarization causes the performance decline at large population sizes, but creates diversity in collective beliefs. Finally, we dissect the mechanisms underlying collective belief collapse and polarization with two complementary approaches. We first introduce social circuit attribution, a technique to predict which agent, and what view, matters most to collective dynamics, and verify its predictions by causal interventions on agents, tracing how agent patching changes collective outcomes. However, the efficacy of causal interventions on agents decreases as the population grows. We therefore develop a statistical mechanical theory for larger populations and verify that it matches the empirical phase diagram. Together, these results take a first step toward mechanistic swarm interpretability, a science of how the properties of individual agents and their communication give rise to emergent collective behavior.
[MA-2] One Axis No Brake: Self-Knowledge Limits the Filtering of Harmful Peer Conformity in LLM s
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent LLM systems）系统中因相互修正机制导致的“误纠”问题，即智能体在试图纠正彼此错误时，可能反而破坏原本正确的答案。其核心挑战在于：判断一次修正是否具有危害性，本质上等价于判断原始回答是否正确，这要求系统具备对自身输出正确性的自我认知能力。因此，论文指出，任何基于运行时信号设计的“制动器”（brake）本质上都是对模型自知能力（self-knowledge）的探测，而当前模型的自知能力有限（跨六类模型家族的AUROC约为0.64–0.89），形成了一个难以突破的“墙”（wall）。即使采用白盒方式直接引导模型的正确性方向，也无法突破这一限制，因为有害与有益的修正行为会同步变化。在群体规模下，“墙”演化为“悬崖”（cliff）——当多数智能体初始判断错误时，辩论机制会放大共性偏差，形成自信但错误的共识。研究进一步表明，增加智能体数量、提升模型多样性或增强个体能力均无法缓解此问题；真正有效的解决方案是引入外部信息以指导修订前的推理过程，而非事后过滤。由此得出关键结论：局部一致性不等于全局正确性。
链接: https://arxiv.org/abs/2609.18998
作者: Yibo Hu
机构: Illinois Institute of Technology (伊利诺伊理工学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:Multi-agent LLM systems are expected to be more reliable because agents can catch each other’s mistakes. But peer pressure cuts both ways: the same correction that fixes a wrong answer can overturn a right one. The tempting safeguard is a brake that keeps the beneficial revisions and blocks the harmful ones. We show this brake is hard to build, for a simple reason: a revision is harmful exactly when the original answer was right, so deciding whether to block it is the same as knowing whether the model was already correct. This turns the open-ended hunt for a brake into one measurable quantity, the model’s self-knowledge: any brake built from a deploy-time signal is a correctness probe in disguise, and self-knowledge is far from perfect (AUROC \approx 0.64 – 0.89 across six model families). We call this ceiling the wall. Even white-box steering of the model’s own correctness direction does not breach it: it changes how often the model revises, but harmful and beneficial revisions move together. At population scale the wall becomes the cliff: when most agents start wrong, debate amplifies the shared mistake into a confident, wrong consensus. In our multiple-choice societies, more agents, more model diversity, and a stronger member do not fix it. What helps is adding information before the revision, not filtering after it. Local agreement is not global correctness.
[MA-3] Social Laws for Multi-agent Coordination in Stochastic Environments ICAPS2026
【速读】：该论文旨在解决多智能体环境中智能体间协调以避免干扰并保障个体性能鲁棒性的关键问题，尤其针对传统社会规范（social laws）研究主要局限于确定性、目标驱动场景的局限性，提出将社会规范扩展至随机性、基于奖励的环境。其核心解决方案在于引入“α-鲁棒性”（α-robustness）这一度量指标，用于量化在所有智能体均遵守社会规范的前提下，每个智能体在追求自身最优单智能体策略时仍能保证的最低效用水平。为实现该鲁棒性验证，论文提出一种基于将问题转化为一系列马尔可夫决策过程（Markov Decision Processes, MDPs）求解的验证方法。实验在简化环境中的评估表明，该框架具备良好的可行性与潜力。
链接: https://arxiv.org/abs/2609.18929
作者: Rolando Fernandez,Caleb Probine,Tyler Lee,Jeffrey Chen,Erez Karpas,Muhammad Arrasy Rahman,Peter Stone,Ufuk Topcu
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Appeared at the RIPL Workshop as part of ICAPS 2026
Abstract:In multi-agent environments, coordinating agents to prevent interference and ensure robust individual performance is a critical challenge. Previous research on social laws for multi-agent systems has primarily focused on deterministic, goal-based settings. This paper extends the concept of social laws to stochastic, reward-based environments, proposing a formalism for defining and verifying their robustness under various conditions. We introduce the notion of \alpha -robustness, a measure of the guaranteed utility each agent retains while pursuing its optimal single agent policy, assuming all agents obey the social law. We then present an approach for robustness verification of social laws in stochastic settings, based on a reduction to solving a series of Markov decision processes. Empirical evaluations on toy environments illustrate the potential of our framework.
[MA-4] Compositional Policy Violations: When Step-Level Compliance Fails In Agent ic AI Workflows
【速读】：该论文旨在解决生成式智能体工作流（agentic workflows）在受监管场景中因治理机制与实际政策不匹配而引发的系统性失效问题。当前主流的治理方式为“步骤级”（step-scoped）监控，依赖输入-输出分类器、单步约束规则及片段级评估器，但组织实际持有的政策（如转介阈值、权限限制、审查要求等）是贯穿整个执行过程的全局属性，而非单一步骤可决定。这种设计导致一种新型故障模式——组合式策略违规（Compositional Policy Violation, CPV）：尽管每个独立步骤均通过其局部合规检查，但多个步骤组合后的整体执行仍违反全局策略。由于单个步骤的谓词无法评估其未决定的全局属性，因此提升步骤级监控精度无法识别此类问题。论文将CPV定义为“步骤级合规性无法正确组合”，并提出四类典型类型：权限蔓延（Authority Creep）、阈值洗白（Threshold Laundering）、累积和违规（Cumulative Sum Violation）以及上下文坍缩（Context Collapse）。研究指出，每类违规的修复关键在于确定受保护量（guarded quantity）发生突变的位置。为此，论文提出一种溯源感知的运行时架构，通过从原始溯源数据重计算受保护量，而非依赖流水线生成的衍生表示，从而实现对完整执行轨迹的策略评估，从根本上解决了由步骤级监控局限性引发的组合式策略违规问题。
链接: https://arxiv.org/abs/2609.18820
作者: Ashwini Kurady,Sri Sai Charith Grandhi,Rajesh Gupta,Sumit Mamoria
机构: RunCtrl(运行控制)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 11 Pages, 6 Figures, 2 Tables
Abstract:Agentic workflows now make consequential decisions in regulated settings, and the governance placed around them is almost entirely step-scoped: input-output classifiers, per turn rails, and span-level evaluators. The policies organizations actually hold, such as referral thresholds, authority limits, and review requirements, are properties of the whole execution rather than of any one step. This mismatch admits a failure mode we call a Compositional Policy Violation (CPV): every individual step passes its own check while the composed execution violates the governing policy. A predicate over a single step cannot evaluate a property that step does not determine, so no improvement in the accuracy of the step-scoped monitors detects this class. We define CPVs as the failure of step-level compliance to compose, and present a taxonomy of four types: Authority Creep, Threshold Laundering, Cumulative Sum Violation, and Context Collapse. We show that the correct repair for each class is dictated by where the guarded quantity mutates. We then introduce a provenance-aware runtime architecture that evaluates policies over complete execution traces, recomputing guarded quantities from raw provenance rather than the pipeline’s derived representation.
[MA-5] Where Should Agents Live? Energy-Memory Characterization of Agent ic AI for the Edge-Cloud Continuum
【速读】：该论文旨在解决在5G-Advanced与6G网络向自治化演进过程中，生成式AI（Generative AI）工作流在边缘-云连续体中分布式部署时面临的能效评估缺失问题。现有AI生命周期评估指标仅关注单一模型的孤立推理，或完全忽略多智能体执行图的复杂交互开销，导致网络运营商无法量化分布式智能体通信的实际能量成本，也无法科学决策智能体团队在边缘-云各层级的最优物理部署位置。为此，论文提出agentic-eCAL，将AI生命周期能耗（eCAL）度量扩展至有向多智能体工作流场景，通过耦合基于闭式解的双速率单次调用能耗模型（计算密集型预填充阶段与内存密集型解码阶段）与7层OSI数据传输模型，实现对跨层级通信与计算协同开销的精准建模。基于数百次NVIDIA A100/H100 GPU基准测试、16个开源模型及8种编排拓扑的实证研究，验证了该度量体系的有效性，并揭示：在5G无线接入网、城域网及光链路中，智能体间文本传输本身仅占整体工作流能耗的0.25%；因此，分布式部署的主要能耗来源并非通信本身，而是由通信触发的额外推理与上下文处理开销。这一发现为边缘-云智能体协同部署提供了关键能效优化依据。
链接: https://arxiv.org/abs/2609.18283
作者: Carolina Fortuna,Vid Hanžel,Tim Strnad,Blaž Bertalanič
机构: Jožef Stefan Institute (尤里·斯蒂芬研究所)
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:As telecommunication networks evolve toward autonomous 5G-Advanced and 6G operations, agentic artificial intelligence (AI) workflows, where large language models (LLMs) execute multi-step reasoning, invoke diagnostic tools, retrieve domain knowledge, and coordinate across agent teams, are increasingly embedded across the edge-cloud continuum. While the biological brain accomplishes complex cognition on an exceptionally modest metabolic power budget of approximately 20W contemporary LLMs are profoundly energy- and memory-intensive, making sustainable lifecycle orchestration a critical operational priority. However, existing AI lifecycle metrics evaluate only isolated, single-model inferences or overlook multi-agent execution graphs entirely. Consequently, network operators lack foundational models to determine whether distributed agent communication incurs meaningful energy costs and where across edge-cloud tiers agent teams should physically reside. To address this gap, we introduce agentic-eCAL, generalizing the Energy Cost of AI Lifecycle (eCAL) metric to directed multi-agent workflows by coupling a closed-form two-rate single-call energy model (compute-bound prefill and memory-bound decode) with 7-layer OSI data transport. Grounded in hundreds of GPU benchmark configurations on NVIDIA A100 and H100, 16 open-weight models and 8 orchestration topologies, we validate components of the metric and study workflow placement implications. Our findings demonstrate that inter-agent text transport incurs 0.25% of workflow energy across 5G RAN, metro, and optical links. Therefore in edge-cloud agent placement the dominant energy cost of distribution is often not the transmission of inter-agent text itself, but the additional inference and context processing induced by that communication.
[MA-6] ABM-SIRTEM: A Hybrid Agent -Based and Epidemiological Model for Pandemic Response
【速读】：该论文旨在解决在新冠疫情背景下，如何平衡公共卫生防控与经济稳定之间的矛盾问题，尤其关注低社会经济群体和依赖人际互动型工作的群体所面临的不平等影响。现有流行病学模型如SIR模型和基于个体的模型（Agent-Based Models, ABMs）在处理个体异质性与计算复杂度之间存在局限：宏观层面的模型往往忽略个体差异，而高精度的ABMs随着代理数量和交互规模增加会面临严重的计算负担。为此，本文提出了一种新型混合模型ABM-SIRTEM，其关键创新在于将职业类别、个体经济生产力及福利水平纳入模型，并动态模拟个体对政府干预措施的依从性行为。该模型通过美国四个州的历史阳性与阴性检测数据进行校准，揭示了不同人群在疫情中的合规行为演化规律，为制定兼顾疾病传播控制与经济社会可持续性的疫情防控策略提供了可量化的决策支持框架。
链接: https://arxiv.org/abs/2609.18223
作者: Sheryl Paul,Samuel Williams,Preetom K. Biswas,Giulia Pedrielli,Jyotirmoy V. Deshmukh
机构: University of Southern California, Los Angeles, USA; Arizona State University, Phoenix, USA
类目: Multiagent Systems (cs.MA); Computer Science and Game Theory (cs.GT)
备注:
Abstract:The COVID-19 pandemic has had profound impacts on global health, social structures, and economies. It disproportionately affected lower socioeconomic groups and those reliant on interaction-based jobs. Regulatory bodies faced the challenge of designing policies that preserve public health while limiting disruption to economic stability and productivity. Epidemiological models such as SIR and agent-based models (ABMs) have been used to study disease dynamics and the socioeconomic impacts of disease and interventions. Population-level models often simplify individual heterogeneity, while detailed ABMs can become computationally expensive as the numbers of agents and interactions increase. We propose ABM-SIRTEM, a hybrid model that incorporates occupation categories, economic productivity, and welfare at the individual level while dynamically modeling compliance with government interventions. We calibrate the model against historical positive and negative test counts from four U.S. states and examine the resulting compliance dynamics. This framework provides a basis for studying the interaction between disease spread and socioeconomic behavior in pandemic-response planning.
[MA-7] ERPBench: A State-Grounded Evaluation Paradigm for Computer-Use Agents in Enterprise Software ICASSP
【速读】：该论文旨在解决当前生成式AI在企业级应用中评估不足的问题，特别是针对企业资源规划（ERP）系统这一关键场景。现有计算机使用代理的评估仍局限于通用桌面与网页任务，而ERP系统具有界面密集、多步骤协同操作复杂以及错误将永久影响业务数据等独特挑战，导致通用代理性能无法有效迁移至企业环境。为此，论文提出ERPBench——一个基于真实可复现ERP系统的截图仅代理评估基准，通过与数据库中的真实值比对进行任务评分，确保评估结果的准确性与可靠性。其解决方案的关键在于构建了一个生产级部署框架，将代理行为置于人工审批机制之下以保障安全性，并实现自主化运行；同时揭示了尽管某些代理在界面操作上表现良好，但在实际数据写入环节存在严重偏差（如仅3%的正确率），暴露出企业工作流特有的失败模式，强调了从通用人机交互能力向企业级可靠性转化的必要性与复杂性。
链接: https://arxiv.org/abs/2609.17885
作者: Kratika Bhagtani,Kusha Sridhar,Maziyar Baran Pouyan,Yuying Zhao,Eugene Siow
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
备注: 8 pages, 3 figures, 5 tables, submitted for review to 2027 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Abstract:Computer-use agents that operate through screenshots and simulated actions are advancing rapidly, yet their evaluation remains anchored to general desktop and web tasks. Enterprise Resource Planning (ERP) systems run the finance, procurement, inventory, and customer operations of organizations worldwide, and pose distinct challenges for computer-use agents: dense interfaces, coordinated multi-step interactions, and errors that alter persistent business records rather than surfacing on screen. Existing enterprise benchmarks rely on proprietary platforms or on simulated approximations of such software. We introduce ERPBench, a benchmark that evaluates screenshot-only agents on a live and reproducible ERP system and scores each task against ground-truth values in its database. Beyond the benchmark, we present a production-grade harness that gates agent actions behind human approval for safe deployment, which ERPBench runs autonomously. Evaluating six closed and open-source agents, we demonstrate that strong general GUI performance does not transfer to enterprise reliability. Even when an agent reaches the right form and saves it, the stored record is often wrong: some agents save in up to 85% of runs but write the correct value in as few as 3%. We further characterize failure modes specific to enterprise workflows.
[MA-8] Investigating Adversarial Robustness of Heterogeneous Cooperative Perception
【速读】：该论文旨在解决异构协同感知（Heterogeneous Cooperative Perception, CP）系统中因传感器配置差异而被误认为具备天然抗攻击能力的安全隐患问题。尽管现有研究指出，在同质环境下恶意节点可通过构造特定特征干扰融合结果，导致真实物体被消除，但普遍认为异构性可提供防御优势——由于攻击者缺乏目标检测器与转换模块的先验知识，且对抗梯度在特征映射过程中被混淆。然而，本文通过构建统一目标的扰动评估框架（matched-objective harness），证明这种看似稳健的保护机制实为虚假安全：经过合理调参的迭代优化攻击能够有效弥合甚至逆转这一看似存在的鲁棒性差距。然而，此类基于优化的方法依赖真实标签和多轮反向传播，难以满足实时性要求，不具备实际战场威胁可行性。为此，本文提出HetPoison，一种基于学习的生成器，可在单次前向传播中无监督地生成高效删除型扰动，具备跨主流异构架构的迁移能力，且无需访问目标检测器，其攻击效果可媲美或超越昂贵的优化方法。鉴于异构性本身并非有效防御手段，作者进一步提出HetShield，一种轻量级信任层，通过验证特征在时空维度上的一致性来识别并抑制异常输入，成功恢复83%–95%因攻击损失的精度，显著优于现有防御方案。
链接: https://arxiv.org/abs/2609.17856
作者: Chenyi Wang,Yutong Liu,Qingzhao Zhang,Ming F. Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA); Robotics (cs.RO)
备注:
Abstract:Heterogeneous cooperative perception (CP) enables connected vehicles with diverse sensor setups to share spatial awareness via compact feature maps, where receivers reconcile these maps using learned translation modules for fusion and inference. Prior attacks against CP in a homogeneous setting reveal that the data exchange introduces a critical attack surface: a single malicious agent can transmit crafted features that erase real objects from a neighbor’s fused scene. Yet, it is widely hypothesized that heterogeneity naturally defends against these attacks, as the attacker lacks knowledge of the victim’s detector and the translation module scrambles adversarial gradients. We demonstrate that this protection is largely an illusion. Using a matched-objective harness to standardize the perturbation budget, objective, and forward path, we show that properly tuned iterative attacks close or reverse the apparent robustness gap. However, these optimization-based attacks require ground-truth labels and iterative backpropagation, meaning they do not represent a practical field threat running in real-time. To bridge this gap, we introduce HetPoison, a learned generator that crafts a removal perturbation in a single, label-free forward pass. HetPoison transfers across major heterogeneous designs without requiring access to the victim’s detector, matching or exceeding the effectiveness of expensive optimizer-based attacks. Since heterogeneity itself is not a defense, we propose HetShield, a lightweight trust layer that validates the spatiotemporal consistency across features, recovering 83–95% of the accuracy degraded by attacks, outperforming prior art.
[MA-9] Set-membership localization of intermittent RF sources using a fleet of collaborating UAVs
【速读】：该论文旨在解决由协同飞行的无人机（UAVs）网络观测到的射频（RF）信号源定位问题，尤其针对具有频率可分离性、间歇性及周期性发射模式且发射周期未知但有界的射频发射源。传统方法在处理此类非平稳、不确定性强的信号源时存在定位精度低与收敛速度慢的问题。本文提出的集合成员法（Set-Membership Approach, SMA）通过构建源位置的集合估计以及无源区域的集合表示，有效利用了信号周期的有界性与时空观测数据的一致性约束，在不依赖先验概率分布的前提下实现了更精确的定位结果与更快的收敛性能。其核心解决方案在于：基于观测数据与已知的周期有界性，采用集合迭代更新机制，动态缩小可能源位置的可行集，从而在保证鲁棒性的同时提升定位效率。
链接: https://arxiv.org/abs/2609.17797
作者: Jacques Bois,Raul de Lacerda,Michel Kieffer
机构: 未知
类目: ignal Processing (eess.SP); Multiagent Systems (cs.MA)
备注:
Abstract:This paper proposes a set-membership approach (SMA) to localize radio frequency (RF) sources observed by a collaborating fleet of Unmanned Aerial Vehicles (UAVs). Considering frequency-separable RF transmitters with intermittent and periodic emission patterns, %and unknown but bounded periods the SMA evaluates set estimates of the source locations and a set free of sources. Simulation results show that SMA outperforms a Bayesian baseline approach in terms of localization accuracy and convergence speed.
自然语言处理
[NLP-0] Objective vs. Search: Decomposing What Makes a Good Tokeniser EMNLP2026
【速读】： 该论文旨在解决现有分词算法（tokenisation algorithms）比较中因优化目标与搜索策略混杂而导致的因果混淆问题。具体而言，主流的字节对编码（Byte-Pair Encoding, BPE）与UnigramLM分词器分别在优化目标（压缩率 vs. 对数似然）和搜索过程（自底向上合并 vs. 自顶向下剪枝）两个正交维度上存在差异，而以往研究未能有效分离这两个因素的影响。为此，本文提出两种新分词算法——BottomUpLL（基于对数似然的自底向上分词器）与TopDownComp（基于压缩率的自顶向下分词器），从而完整覆盖2×2的设计空间。其关键解决方案在于通过控制变量法，独立考察优化目标与搜索过程对语言模型性能的影响。实验结果表明，在比特/字节（bits-per-byte）指标下，搜索过程是主导因素：自底向上分词器在多数设置中均表现更优；而在BLiMP任务上的表现则未显示出设计选择与性能之间的稳定关联。综上，该研究首次系统性地解耦了分词器设计中的核心要素，为构建更可解释、更高效的分词策略提供了实证依据与理论指导。
链接: https://arxiv.org/abs/2609.19145
作者: Ahmetcan Yavuz,Clara Meister,Tiago Pimentel
机构: ETH Zürich(苏黎世联邦理工学院); EPFL(洛桑联邦理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at EMNLP 2026. 20 pages, 4 figures, 10 tables. Code: this https URL
Abstract:Two dominant tokenisation algorithms are used by modern language models: byte-pair encoding (BPE) and UnigramLM. These differ along two orthogonal axes: their optimisation objective (compression vs. log-likelihood) and their search procedure (bottom-up merging vs. top-down pruning). Existing comparisons confound these axes, making it unclear whether their observed differences stem from what is being optimised vs. how it is being optimised. We disentangle the two by introducing two new tokenisation algorithms that complete this 2x2 design space: BottomUpLL, a bottom-up likelihood-based tokeniser, and TopDownComp, a top-down compression-based tokeniser. We train language models with tokenisers produced by each algorithm, varying: model size, vocabulary sizes, and domain (English-only vs. multilingual). Evaluating models on bits-per-byte, we find that the search procedure – not the objective – is the dominant factor: bottom-up tokenisers consistently achieve lower bits-per-byte in most settings. Evaluating models on the BLiMP task, however, shows no consistent relationship between design choice and performance. Overall, our results disentangle the effect of tokeniser design choices on language modelling performance, offering concrete guidance for their more principled construction.
[NLP-1] A Zeroth-Order Paradigm for LLM Preference Alignment
【速读】： 该论文旨在解决大语言模型（LLM）在直接偏好对齐（Direct Preference Alignment）过程中因似然偏移（likelihood displacement）导致的性能瓶颈问题。现有方法虽具备计算与内存效率优势，但在处理似然边际较小的偏好对时信息提取不足。为此，论文提出基于比较预言机（comparison oracle）的零阶对齐方法——比较式偏好优化（Comparison-based Preference Optimization, ComPO），其核心在于不直接优化可微分的偏好损失，而是通过偏好对提取方向性信息，实现对模型行为的渐进调整。该方法的关键创新在于：在离线场景下，利用平滑性、梯度稀疏性及预言机与潜在目标之间的兼容性条件，建立了收敛性保证；在在线场景中，保留离线比较机制的同时，引入未标注策略生成样本以实现相对于参考策略的反向KL控制。此外，从覆盖视角出发，论文还为一个基础约束方案建立了性能保障，前提为局部覆盖性和分布内成对奖励准确性。实验在Mistral、Llama、Gemma-2、Qwen3和Gemma-3等模型上验证了ComPO在长度可控胜率等指标上的优越性，且成对级诊断结果支持其有效缓解了似然偏移问题。
链接: https://arxiv.org/abs/2609.19144
作者: Peter Chen,Xi Chen,Wotao Yin,Tianyi Lin
机构: University of California, Berkeley (加州大学伯克利分校); New York University (纽约大学); DAMO Academy, Alibaba Group U.S. (阿里巴巴集团美国达摩院); Columbia University (哥伦比亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 39 pages
Abstract:Direct preference alignment methods are widely used to align large language models (LLMs) with human preferences because of their computational and memory efficiency. However, likelihood displacement motivates alternative ways to extract information from preference pairs with small likelihood margins. In this paper, we propose and analyze Comparison-based Preference Optimization (ComPO), a zeroth-order alignment method based on comparison oracles. ComPO extracts directional information from these pairs without directly optimizing a differentiable preference loss on them. We establish a convergence guarantee for its basic offline scheme under smoothness, gradient sparsity, and compatibility between the oracle and a latent objective. We further introduce online ComPO, which retains the offline comparison mechanism and uses unlabeled policy generations for reverse-KL control relative to a reference policy. Following the coverage perspective of preference fine-tuning, we establish a performance guarantee for a basic constrained scheme under local coverage and in-distribution pairwise reward accuracy. Experiments on Mistral, Llama, Gemma-2, Qwen3, and Gemma-3 models demonstrate improvements over existing direct alignment methods, including length-controlled win rates, with pair-level diagnostics providing evidence consistent with mitigating likelihood displacement.
[NLP-2] PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection
【速读】： 该论文旨在解决当前视觉-语言模型（Vision-Language Models, VLMs）在图像理解中难以实现精准像素级语义定位的问题，尤其针对复杂场景下对前景物体与背景区域的全面描述及对应短语的精确像素级锚定。现有方法在结合密集描述与像素级定位时，常面临描述不完整或分割掩码不准确的困境。为此，论文提出“全景式像素锚定描述”（panoptic grounded captioning）这一新任务，要求模型不仅生成涵盖所有图像实体的详细描述，还需为每个指代短语提供精确的像素级掩码。其核心解决方案在于：首先构建了PanoCaps数据集，该数据集基于全景分割数据集人工标注，具备近乎完整的像素覆盖率和实体级别的图文对齐，同时引入短语-掩码匹配协议与广义全景质量（gPQ）评估指标，实现对文本与掩码一致性的联合评价；其次，提出PANORAMA模型，将短语定位建模为在上下文感知的短语条件掩码提案池中进行选择，并利用预训练分割器根据上下文化的短语表示生成候选掩码，通过联合训练实现高质量掩码生成与一致的文本描述。实验表明，PANORAMA在PanoCaps上达到最优的全局定位性能，在多个像素级定位任务中表现优于或相当专业模型，能够生成精确的实体级分割结果并保持与掩码一致的详尽描述。
链接: https://arxiv.org/abs/2609.19143
作者: Sara Pieri,Evangelos Kazakos,Shizhe Chen,Josef Sivic,Cordelia Schmid
机构: Inria(法国国家信息与自动化研究所); École normale supérieure(巴黎高等师范学院); CNRS(法国国家科学研究中心); PSL Research University(巴黎萨克雷大学研究机构); Czech Institute of Informatics, Robotics and Cybernetics(捷克信息学、机器人学与控制论研究所); Czech Technical University in Prague(布拉格捷克技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language models (VLMs) can generate fluent and detailed image captions, but reliably associating them with image pixels remains challenging. Existing methods that combine dense captioning with pixel-level grounding often produce either incomplete descriptions or inaccurate segmentation masks. We study this problem through panoptic grounded captioning, a task that requires a VLM to describe both foreground objects and background regions while grounding each referring phrase with pixel-level masks. We make three contributions. First, we introduce PanoCaps, a human-annotated benchmark constructed from panoptic segmentation datasets. It provides dense captions with near-complete pixel coverage and image-text alignments at the entity level, supporting both training and evaluation. We further propose a phrase-mask matching protocol and a generalized Panoptic Quality (gPQ) metric that jointly evaluates textual and mask agreement. Second, we formulate phrase grounding as selection from a phrase-conditioned pool of mask proposals and introduce PANORAMA, a VLM that conditions a pretrained segmenter on contextualized phrase representations to obtain candidate masks and learns to select those corresponding to each phrase. Training this interface jointly with caption generation enables PANORAMA to produce high-quality masks while allowing each phrase to refer to a single region or multiple instances. Third, PANORAMA achieves the best overall grounding on PanoCaps and matches or exceeds specialized models across several pixel-level grounding tasks. Experiments show that our method produces precise entity-level segmentations while maintaining detailed, mask-consistent captions. Code, data and models are available at this https URL.
[NLP-3] ScienceIDE: Turning Worlds Scientific Codebase into Agent Learnable Environments
【速读】： 该论文旨在解决科学代码知识难以转化为可信赖学习体验的“科学经验瓶颈”（scientific experience bottleneck）问题，即尽管科学代码库蕴含数十年的人类知识，但因工具链碎片化、领域隐含规范以及专业化的正确性标准，导致这些知识难以有效用于训练智能体。其解决方案的关键在于提出ScienceIDE，一种将全球科学代码转化为可编程科学环境的基础设施。通过专家定义的科学案例与验收标准，科学智能体可将代码仓库转化为支持任务生成、执行与科学验证的可执行环境，从而为监督微调、强化学习及评估提供统一基础。基于经过验证的交互轨迹，研究团队训练出PhAI-IDE系列模型（PhAI-IDE-72B、PhAI-IDE-9B和PhAI-IDE-4B），实验表明这些模型在未见科学代码修复任务以及多个通用代码、推理与知识基准上均表现出性能提升，证明了科学经验向更广泛能力的正向迁移。ScienceIDE构建了一个集成化的智能体学习与科学实践工作空间，使人类的科学软件成为发展科学智能的共享基础。
链接: https://arxiv.org/abs/2609.19134
作者: Hejia Geng,Zesen Huang,Haoyang Li,Wenbin Li,Koutian Wu,Zihan Zhou,Yuanbo Pang,Weihao Liu,Zigong Xu,Zhiping Li,Zongzheng Zhang,Chuanfei Dong,Jiankai Sun,Tianzhe Zheng,Fengyu Xie,Yue Ma,Yueheng Shi,Tong Xie,Zonglin Di,Xianrong Liu,Qucheng Gao,Yimin Liu,Jiaming Pan,Sheng Huang,Xiao-Han Ma,Lanqing Yuan,Zhenlin Zhu,Ziang Liu,Ziyang Xu,Junkai Wang,Kangkai Liang,Jiayi Xian,Zehong Zhao,Liuwei Xu,Jingxu Xie,Peijin Zhang,Qiang Gao,Chengyi Xing,Zhe Zhao,Xi Wang,Yaopeng Xing,Xing Meng,Zhenfei Yin,Yingcheng Wu,Ling Yang
机构: University of Science and Technology of China (中国科学技术大学); Tsinghua University (清华大学); Peking University (北京大学); Shanghai Jiao Tong University (上海交通大学); Zhejiang University (浙江大学); Harbin Institute of Technology (哈尔滨工业大学); Fudan University (复旦大学); Nanjing University (南京大学); Beijing Institute of Technology (北京理工大学); Sun Yat-sen University (中山大学); Xi’an Jiaotong University (西安交通大学); Southeast University (东南大学); Shandong University (山东大学); Sichuan University (四川大学); Northeastern University (东北大学); Xiamen University (厦门大学); Wuhan University (武汉大学); Tianjin University (天津大学); Guangzhou University (广州大学); Chongqing University (重庆大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: Code: this https URL
Abstract:Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conventions, and specialized correctness criteria make this knowledge difficult to convert into reliable learning experience-a challenge we call the scientific experience bottleneck. We introduce ScienceIDE, infrastructure for turning the world’s scientific code into programmable environments for scientific agents. Guided by expert-defined scientific cases and acceptance criteria, agents transform repositories into executable environments that support task generation, execution, and scientific verification. These environments provide a shared foundation for supervised fine-tuning, reinforcement learning, and evaluation. Using verified interaction trajectories, we train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities. ScienceIDE lays the foundation for an integrated workspace for agent learning and scientific practice, making humanity’s scientific software a shared substrate for developing scientific intelligence. Code: this https URL
[NLP-4] Playing log(N)-Questions over Wikipedia Abstracts: Communication Efficiency Between Paired Frontier Models
【速读】： 该论文旨在解决生成式 AI（Generative AI）在存在信息不对称条件下的自我通信能力问题，具体通过“双代理对数提问游戏”（two-agent log(N)-Questions game）评估大语言模型（LLM）在有限问答轮次内通过精确的二元提问与回答实现目标识别的效率与可靠性。其核心挑战在于：一方提问者仅能访问一组文档的摘要（lead paragraphs），另一方回答者仅知目标实体及当前问题，需以单字回应，二者均由同一模型承担，从而测试模型在自身知识内部进行高效、一致推理与信息传递的能力。解决方案的关键在于模型能否在信息稀疏条件下维持高可靠性的逐轮判断能力，研究发现胜率可被单一每轮可靠性参数 $ p = 0.928 $ 拟合为 $ \text{win} = p^{\log_2 N} $，表明成功依赖于稳定且一致的推理质量；此外，仅有两个模型能实现每轮完整比特的信息提取，其共性是能够基于文档标题进行有效分区，这一策略在 $ N < 32 $ 时未被采用，但在 $ N \geq 32 $ 时出现在约四分之一的问题中，揭示了结构化提示与关键特征利用的重要性。同时，尽管推理令牌消耗差异达4.5倍，但与成功率关联极弱，说明计算开销并非决定因素，而是在候选集缩小过程中缺乏相应可靠性提升，反映出模型在后期推理中存在过拟合或信心膨胀问题。
链接: https://arxiv.org/abs/2609.19113
作者: Peter Potash
机构: 未知
类目: Computation and Language (cs.CL)
备注: 29 pages
Abstract:We evaluate six frontier language models on the two-agent \log(N) -Questions game. A questioner sees N Wikipedia lead paragraphs and must identify a secretly chosen target using exactly \log_2 N yes/no questions. An answerer sees only the target and the question, and replies with one word. Both roles run on the same provider, so the game measures how well a model communicates with itself across an information asymmetry. We run 408 games over document sets of 4 to 1024 paragraphs at a total API cost of \ 363. One model finishes well behind the others: Claude Opus 5 wins 28 of 68 games, against 45 to 56 for GLM-5.3, GPT-5.6 Sol, Grok 4.6, Gemini 3.8 Flash and Kimi K3. The leading five are only marginally separable. Pooling those five, win rate declines with set size at r=-0.973 and is fit by a single per-round reliability parameter. The form is \textwin=p^\log_2 N with p=0.928 . Losses divide into answer errors and discrimination failures in roughly equal measure, and models almost never name a document their own evidence excludes. Every unanimous answer error from the weakest model was inspected: 32 of 34 are No'' answers, on properties stated in the document's first sentence, under an instruction that explicitly warns against defaulting to No’'. Information per question, estimated from answer balance, correlates with win rate at r=+0.88 . The only two models to extract a full bit per question are the only two that partition on document titles, a strategy absent below N=32 and used in a quarter of questions above it. Reasoning-token expenditure varies 4.5\times across models with little relation to success, and the trace grows as the candidate set shrinks without a matching gain in reliability.
[NLP-5] Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations
【速读】： 该论文旨在解决大模型在规模扩大过程中日益普遍且复杂的奖励劫持（reward hacking）问题，探究其在模型内部表示中的可识别特征，并提出一种高效、低成本的检测与监控方法。其核心解决方案在于利用**简单差异均值向量（Difference of Means, DoM）**这一白盒方法，对前沿开源大语言模型（LLM）如Kimi K3、GLM 5.2和Qwen 3.8 Max的内部表征进行分析，发现这些向量能够一致且可解释地表征多种奖励劫持行为。尽管结构简单，DoM向量具备良好的泛化能力与可解释性，可在不依赖昂贵监督信号的情况下实现对奖励劫持的高精度检测。实验表明，DoM向量在DeepSWE与SWE-bench等基准测试中表现优于传统基于大模型的监测器，在保持相近误报率的前提下，能更有效地捕捉潜在的奖励劫持行为，甚至可在思维链（chain-of-thought）推理过程中提前预测后续的异常行为，实现在线实时监控。此外，通过分析未被现有监测器捕获的探针激活（probe-hits），还揭示了其他不良行为模式，并验证了该方法在非SWE评估场景下的迁移能力。研究结果表明，基于DoM向量的简单白盒方法可为大规模、可扩展地研究与监控前沿开源模型中的奖励劫持行为提供有效路径。
链接: https://arxiv.org/abs/2609.19101
作者: Leon Bergen,Usha Bhalla,Andrew Lee,Barak Widawsky,Linas Nasvytis,Connor Watts,Siddharth Boppana,Sidharth Baskaran,Dron Hazra,Michael Byun,Atticus Geiger,Owen Lewis,Matthew Kowal,Vasudev Shyam,Thomas Fel,Thomas McGrath,Ekdeep Singh Lubana,Jack Merullo
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:As models scale, reward hacking becomes more frequent, more sophisticated, and more consequential. Does it leave a telltale signature in model representations? This work analyzes how reward hacking is represented internally in frontier open source LLMs, and how those representations can be used to understand and discover the range of hacking behaviors a model displays. In particular, we find that simple difference of means vectors coherently represent reward hacking in Kimi K3, GLM 5.2, and Qwen 3.8 Max across a variety of behaviors in common evaluations. Despite their simplicity, these vectors are both generalizable and interpretable, and we can use them to reliably detect reward hacking. We first evaluate reward hacking in commonly reported benchmarks like DeepSWE and SWE-bench, finding that models reward hack excessively in these environments; GLM 5.2 hacks in 57.2% of rollouts on DeepSWE and in 73% of rollouts on SWE-bench. Catching these requires monitors; LLM monitors are effective, but expensive detectors. We show that DoM vectors are similarly effective but virtually free, catching 3.1% more hacks in Kimi K3 and 7.9% fewer hacks in GLM 5.2 on DeepSWE at a monitor matched false positive rate. DoM vectors run on the chain-of-thought also predict reward hacks in the model’s subsequent actions, meaning we can run them online and catch potential hacks before they occur. Finally, we analyze probe-hits that LLM monitors do not catch and discover other undesirable behaviors, as well as show transfer to finding hacks in non-SWE evaluations. Together, these results provide evidence that simple, white-box methods can be used to scalably study and monitor reward hacking behaviors in frontier open source models
[NLP-6] Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation
【速读】： 该论文旨在解决当前基于人工智能的放射科报告生成（Radiology Report Generation, RRG）模型评估中因放射科医生报告习惯异质性所导致的评价偏差问题。不同放射科医生在描述相同临床发现时，可能在术语使用、缩写方式、格式排布及详细程度等方面存在显著差异，这种报告实践上的多样性会显著影响现有评估指标对模型性能的判断，甚至改变模型间的相对排名。其解决方案的关键在于提出一种由放射科医生指导的报告变异分类体系，并开发了一种名为ReRef的方法，能够基于该分类体系对参考报告进行语义保持下的重构，从而在不改变临床含义的前提下模拟不同的报告风格。实验表明，通过调整参考报告的表述形式，可使多个主流RRG模型的性能排名发生显著变化，揭示了现有评估方法往往未能将临床准确性与报告格式一致性有效分离的问题。研究强调，选择能准确反映目标报告规范的参考报告对于实际评估至关重要。为支持后续研究，作者发布了经过放射科医生验证的MIMIC-CXR-Ext-ReRef数据集，包含120对源自MIMIC-CXR的原始与替代参考报告对。
链接: https://arxiv.org/abs/2609.19093
作者: Daniel P. Jeong,Charles Q. Li,Hossein Hosseiny,Nitya M. Bhalla,Fatma Uyar Morency,Pradeep Ravikumar,Zachary C. Lipton,Michael Oberst
机构: Carnegie Mellon University (卡内基梅隆大学); Allegheny Health Network (安莱吉尼健康网络); Highmark Health Enterprise Data Analytics (高马克健康企业数据分析); Johns Hopkins University (约翰霍普金斯大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Radiologists follow heterogeneous reporting practices. Two radiologists examining the same image and identifying the same clinical findings might nevertheless compose superficially distinct reports, varying in terminology, shorthand, formatting, and level of detail. These variations in reporting norms represent an under-appreciated obstacle in efforts to evaluate AI-based radiology report generation (RRG) models, where machine-generated reports are typically assessed based on their concordance with human-generated references. In this paper, we quantify the sensitivity of established evaluation metrics to variations in reporting practices, revealing impacts large enough to alter the rankings of models. We introduce a radiologist-informed taxonomy of variations in radiology reporting practice and a method (ReRef) that rewrites reference reports along the axes of our taxonomy while preserving clinical interpretation. For instance, when comparing the performance of nine RRG models on MIMIC-CXR using RadCliQ-v1, condensing the discussion of normal findings in the reference reports causes Libra to drop from first to second place while CheXOne rises from third to first. Our results suggest that many current metrics fail to decouple clinical interpretation from conformity to reporting practices and that choosing the ``right’’ references that accurately reflect the desired reporting practices can be important in practice. To support future research, we release MIMIC-CXR-Ext-ReRef, a radiologist-validated dataset of 120 (original, alternative) reference report pairs derived from MIMIC-CXR.
[NLP-7] MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education
【速读】： 该论文旨在解决当前大型视觉-语言模型（Large Vision-Language Models）在教育场景中对艺术类图像理解能力评估不足的问题。现有基准测试多聚焦于现实世界图像或特定领域的教育推理任务，缺乏对艺术性教育内容的充分覆盖，尤其忽视了跨文化语境下的视觉意义、情感与文化内涵的理解。为此，研究提出MUSE基准，专门用于评估模型在情境化教育应用中对艺术图像的综合理解能力。其核心解决方案在于将图像标注与问题生成解耦，实现任务多样性与难度可控性的同时降低标注成本；MUSE包含12项涵盖视觉感知、语义与情感解读、文化认知及组合推理的任务，精选了以新加坡及东南亚多元文化背景为主、兼顾西方艺术传统的多样化艺术图像，覆盖多重主题与难度层级。实验结果揭示了开源与专有模型在情感解读与组合推理等维度存在显著能力差距，并识别出模型在教育应用中的常见失效模式与关键挑战。研究期望MUSE能成为推动情境化教育应用中多模态理解发展的标准化评估基准。
链接: https://arxiv.org/abs/2609.19088
作者: Luyao Zhu,Xun Wei Yee,Wei Li,Mun Thye Mak,Wee Siong Ng
机构: AI Singapore(人工智能新加坡); National University of Singapore(新加坡国立大学); School of Computing(计算机学院); Institute of Advanced Intelligence and Computing(先进智能计算研究所); A*STAR(新加坡科技研究局)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision-language models have achieved remarkable progress in multi-modal understanding, yet their capabilities in educational settings remain insufficiently evaluated. In AI-assisted language learning, models must interpret artistic imagery, understand its semantic, affective, and cultural content, and reason about visual context to support meaningful interaction. However, existing benchmarks primarily focus on real-world images or domain-specific educational reasoning, providing limited coverage of artistic educational content. To address this gap, we introduce MUSE, a benchmark for evaluating large vision-language models on artistic image understanding in situated educational applications. MUSE decouples image annotation from question generation, enabling diverse tasks with controllable difficulty while reducing annotation effort. It comprises twelve tasks spanning visual perception, semantic and affective interpretation, culture understanding, and compositional reasoning, together with diverse artistic images deliberately curated to center Singaporean and Southeast Asian multicultural contexts alongside Western art traditions, covering multiple themes and difficulty levels. Evaluation of open-source and proprietary models reveals substantial disparities across capability dimensions, particularly in affective interpretation and compositional reasoning. Our analysis further identifies common failure modes and key challenges for developing trustworthy multi-modal models for education. We hope MUSE will serve as a standardized benchmark for advancing multi-modal understanding in situated educational applications.
[NLP-8] Safety-Flag: A Unified Benchmark for the Reliability and Calibration of LLM Content Moderators
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在内容审核中评估方法单一、缺乏多维度可靠性分析的问题。现有评估普遍依赖各基准测试的聚合准确率，无法揭示模型在错误方向上的系统性偏差、置信度校准问题以及置信度排序对人工审查的指导价值。为此，作者提出Safety-Flag，将七个广泛使用的内容安全评测基准（BeaverTails、XSTest、Ethics、WildGuard、Aegis、ToxiChat和ToxiGen）整合至一个统一的“标记/不标记”平衡协议中，并公开了六种通用大模型与四种专用内容审核模型在相同测试项上的逐项决策结果及置信度评分，辅以三类参考模型作为对比。其核心解决方案在于构建一个多维度的审核可靠性评估框架，涵盖错误方向（error direction）、概率校准（probability calibration）和基于置信度的错误排序（confidence-based error ranking）三个关键维度。研究发现，不同模型在错误类型上存在显著差异：某些模型误标85%的良性内容，而另一些则漏检54%的有害内容；所有通用模型均表现出过度自信，通过为每模型单独调整温度参数可使校准误差降低2.8至6.0倍，且不改变预测标签或置信度排序；基于置信度的主动回避策略能有效降低选择性风险，但其效果取决于置信度对真实错误的排序能力；专用审核模型虽误报更少且校准更优，但在其声明覆盖范围外仍存在更高的漏检率。该工作通过发布完整基准数据、固定测试项列表、评估代码、逐项输出及排行榜，推动内容审核评估向更透明、更全面的方向发展。
链接: https://arxiv.org/abs/2609.19072
作者: Yibo Hu
机构: Illinois Institute of Technology (伊利诺伊理工学院)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Large language models are increasingly used for content moderation, but most evaluations still report aggregate accuracy on individual benchmarks. We introduce Safety-Flag, which places seven widely used safety benchmarks (BeaverTails, XSTest, Ethics, WildGuard, Aegis, ToxiChat, and ToxiGen) into a single balanced flag / do-not-flag protocol. We release item-level decisions and confidence scores for six general-purpose LLMs and four dedicated guards, together with three reference models, evaluated on the same items. Safety-Flag measures three dimensions of moderator reliability: error direction, probability calibration, and confidence-based error ranking for human review. They often disagree. Aggregate accuracy does not reveal error direction: one model flags 85% of benign content, whereas another misses 54% of harmful content. All six general-purpose models are overconfident; fitting one temperature per model reduces calibration error by 2.8 – 6.0\times without changing predicted labels or confidence ordering. Confidence-based abstention lowers selective risk for every model, although the gains depend on how well confidence ranks errors. Dedicated guards produce fewer false alarms and are better calibrated, but several have higher miss rates outside their documented coverage. We release the benchmark, fixed item lists, evaluation code, per-item model outputs, and leaderboard at: this https URL.
[NLP-9] Benchmarking Large Language Models for Biomedical Relation Extraction
【速读】： 该论文旨在解决从生物医学文献中自动提取单核苷酸多态性-表型（SNP-phenotype）关联信息的挑战性问题，尤其针对三类任务：句子级、摘要级以及关联强度分类。其解决方案的关键在于评估多种自然语言处理（NLP）模型在SNPPhenA语料库上的表现，发现未经微调的少样本学习（few-shot learning）条件下，OpenAI O1在句子级分类任务中达到SOTA性能（F1=0.89），而微调后的Gemini 2.0 Pro在摘要级分类中也实现了新的SOTA（F1=0.82）。尽管关联强度分类任务仍具挑战性，但微调后的Gemini 2.0 Pro在首次针对该任务的大型语言模型（LLM）评估中表现最佳（F1=0.60）。研究结果表明，专有大型语言模型（如OpenAI O1和Gemini 2.0 Pro）在少样本或微调设置下显著优于其他模型，凸显了现代大型语言模型在基因组知识提取中的强大能力。
链接: https://arxiv.org/abs/2609.19071
作者: Claudiu Creanga,Teodor Marchitan,Liviu P. Dinu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Extracting SNP-phenotype associations from biomedical literature is vital but challenging. We benchmarked diverse NLP models, including MLMs, hybrid architectures, and state-of-the-art LLMs (Gemini 2.0, OpenAI O-series, Qwen, Mistral), on the SNPPhenA corpus across three tasks: sentence-level, abstract-level, and association strength classification. OpenAI O1 achieved state-of-the-art (SOTA) results using few-shot learning for non-finetuned sentence-level classification (F1 0.89) and established a new SOTA for abstract-level classification (F1 0.82). Association strength classification proved difficult, though fine-tuned Gemini 2.0 Pro performed best (F1 0.60) in the first LLM evaluation of this task. Proprietary LLMs, especially in few-shot (O1) or fine-tuned (Gemini 2.0 Pro) settings, significantly outperformed other models. These findings confirm the power of modern LLMs for genomic knowledge extraction.
[NLP-10] Reading Between the Lines: Can LLM s Discover the Question Behind the Text?
【速读】： 该论文旨在解决如何从完整文本中逆向推断其唯一且真实的“起源问题”（genesis question）这一挑战，即还原作者创作该文本时最初意图回答的核心问题。与侧重生成任意合理问题的“问题生成”（Question Generation）或建模话语层面语用行为的论述框架不同，本研究聚焦于模型对作者意图（authorial intent）的理解能力。其解决方案的关键在于构建了一个新的数据集，包含委托撰写文本与其原始研究问题及干扰项（plausible distractors）的配对，从而为评估模型在深层语义理解与意图推理方面的能力提供基准。实验结果表明，当前大语言模型（LLM）在该任务上已显著超越人类表现，尤其以Gemini Flash/Pro、Mistral和Qwen等模型为代表的新一代模型展现出更强的推理能力，而基于BERT的模型表现较差。这一发现揭示了现代生成式AI（Generative AI）在捕捉复杂人类交流中的隐含意图方面具备前所未有的潜力，为未来模型在需要精细语境理解的任务中应用提供了重要启示。
链接: https://arxiv.org/abs/2609.19070
作者: Claudiu Creanga,Liviu P. Dinu
机构: University of Bucharest (布加勒斯特大学); HLT Research Center (高级语言技术研究中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper introduces ``question archaeology’', a specific evaluation task focused on inferring the single, authentic “genesis question” that motivated the creation of a complete text. Distinct from question generation, which targets any plausible question, or discourse frameworks that model utterance-level acts, our task assesses a model’s grasp of authorial intent. We present a new dataset of commissioned texts paired with their original research questions and plausible distractors. Our evaluation of both proprietary models, like Gemini Flash and Pro, as well as open source models like Mistral and Qwen, reveals significant progress in this task, with the newer versions outperforming the earlier ones, while BERT-based models performed poorly. Notably, our findings indicate that current LLMs surpass human performance on this task, suggesting advanced understanding of authorial intent. This capability has important implications for AI’s role in tasks requiring nuanced interpretation of human communication. Our work thus provides a new framework and a challenging benchmark for future models.
[NLP-11] MIRAG E: How Conversation State Shapes Historical Evidence Use in Multimodal Personal Agents ACM-MM2026
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Model, MLLM）代理在执行长期任务时对历史证据使用能力评估不准确的问题。现有评估方法仅依赖结果正确性（outcome-only evaluation），无法有效识别模型是否真实依赖先前对话、文件或工作空间状态中的证据，导致高估其实际证据利用能力。为克服这一缺陷，论文提出MIRAGE（Multimodal Interaction Retrieval, Attribution, and Grounding Evaluation）——一种受控实验框架，通过固定证据对象、问题与评分标准，仅变化对话状态，系统评估代理在不同状态下的三项关键能力：判断问题可回答性、恢复正确信息源以及基于该源作答的能力。研究发现：1）模型在预压缩深度和后压缩延续阶段存在非单调的、分离的失效模式，而非单一退化曲线；2）开源模型高度依赖上下文连续性，在溯源失败时缺乏主动调用工具检索的能力；3）在深度预压缩状态下，检索压力有助于工具兼容模型提升源归因准确性，但在压缩后状态中则持续恶化，表明存储证据已发生不可逆退化。因此，该研究强调历史证据使用必须在对话状态变化条件下进行评估，而非仅依赖结果正确性推断。
链接: https://arxiv.org/abs/2609.19059
作者: Yu Liu,Wenxiao Zhang,Cheng Hu,Cong Cao,Fangfang Yuan,Xinyu Wang,Jin B. Hong,Yanbing Liu
机构: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China(中国科学院信息工程研究所, 北京, 中国); School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China(中国科学院大学网络空间安全学院, 北京, 中国); Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia(西澳大利亚大学计算机科学与软件工程系, 佩斯, 澳大利亚)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted by ACM MM 2026
Abstract:Multimodal large language model (MLLM) agents are increasingly used as personal assistants for long-running tasks. Their utility depends on continuity: agents must retrieve and use earlier evidence across dialogue, files, and workspace state. However, agents can generate plausible answers even when access to that history has degraded, causing outcome-only evaluation to overestimate true evidence use. We present MIRAGE (Multimodal Interaction Retrieval, Attribution, and Grounding Evaluation), a controlled study of historical evidence use under conversation-state variation in multimodal personal agents. MIRAGE holds evidence objects, questions, and scoring fixed while varying only conversation state, and evaluates whether an agent can determine answerability, recover the correct source, and answer from it. Across seven frontier and open-weight multimodal backbones, we find that: 1) pre-compaction depth and post-compaction continuation form distinct, non-monotonic failure regimes rather than a single degradation curve; 2) open-weight models rely heavily on context continuity and are reluctant to spontaneously switch to tool-mediated retrieval when provenance fails; and 3) retrieval pressure improves source attribution in deep pre-compaction states for tool-compliant models, but consistently regresses after compaction, where stored evidence has already degraded. These findings show that historical evidence use should be evaluated under state variation, rather than inferred from outcome-only correctness.
[NLP-12] alkMatrix: Generating Character Dialogue that is Both Consistent and Diverse
【速读】： 该论文旨在解决传统基于候选生成的解码方法在处理需要全局非可分解约束的应用场景时的局限性，即独立地为每个提示（prompt）选择补全（completion）无法保证输出集合在整体上满足一致性、多样性等结构性要求。其核心问题是：如何在给定每个提示的候选补全池的前提下，联合选择每条提示的补全，以优化一个集合级别的目标函数。解决方案的关键在于提出一种名为TalkMatrix的方法，该方法针对角色对话场景，为每个角色-情境组合生成多个候选回复，并通过四个基于嵌入的统一性与多样性目标，联合优化整个对话矩阵。为避免单一加权求和导致某些性能维度被牺牲，该方法采用双层极小极大（two-level minimax）优化框架，以最大化最差表现的目标，从而实现多目标间的均衡。通过多起点坐标上升法近似求解离散优化问题，实验表明，在50个合成角色扮演场景和25个精心设计的桌游交互场景中，所提方法在集合层面的生成质量显著优于随机选择和独立单元选择基线，验证了结构化联合选择对全局可控对话生成的有效性。
链接: https://arxiv.org/abs/2609.19022
作者: Ayuto Tsutsumi,Yuu Jinnai
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Candidate-based decoding typically selects a completion for each prompt independently, but many applications require a collection of outputs that satisfies global, non-decomposable requirements. We formulate this setting as structured multi-prompt, multi-completion selection: given a candidate pool for every prompt, select one completion per prompt to optimize a collection-level objective. We instantiate the problem in character dialogue, where each character should remain consistent across situations, each line should fit its situation, and characters and situations should remain distinguishable. Our method, TalkMatrix, generates multiple candidates for every character–situation pair and jointly selects a complete matrix using four embedding-based consistency and diversity objectives. Because a weighted sum can improve some dimensions by sacrificing another, TalkMatrix maximizes the worst-performing objective through a two-level minimax formulation. We approximately optimize the resulting discrete objective with multi-start coordinate ascent, and compare it with local, partial-matrix, and generic combinatorial search baselines. We run experiments on 50 synthetic role-playing scenarios and 25 curated board game scenarios where multiple characters interact in predefined situations. An LLM-as-a-judge rates matrix-level selection higher than random and independent cell-level selection baselines. These results show the value of structured selection for globally controlled dialogue generation, while our empirical validation remains specific to role-playing scenarios.
[NLP-13] WordPolo: Evaluating Language Models Through Iterative Semantic Feedback
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）与大推理模型（Large Reasoning Models, LRMs）评估中仅依赖数据集准确率而忽视推理过程质量与忠实度的问题。现有评估方法无法揭示模型在复杂任务中如何进行逐步推理、调整策略或理解反馈信息，导致对模型真实推理能力的误判。为此，作者提出一种名为WordPolo的词语发现任务，要求参与者通过语义相似性反馈逐步逼近一个未知目标词，每次猜测后获得距离评分（1表示正确，数值越高表示越远），从而迫使模型在无初始知识的情况下进行迭代式推理与自适应搜索。该设计使推理过程可直接观测且成为成功的关键。研究通过1,500个谜题对多个先进模型（如GPT-4.1、Llama 4、Claude 3.5 Haiku、Qwen 3、o4-mini、Deepseek-R1）、人类及一种新型启发式算法进行评估，不仅分析解题成功率（4%至62%），更引入基于进展的度量指标，揭示出仅凭准确率难以捕捉的深层推理动态——例如部分模型因“过度思考”或“思考不足”而表现受限，而表现优异的模型则展现出类人策略。因此，该研究的核心解决方案在于构建一个能够同时衡量推理过程与最终结果的综合性评估框架，强调需发展兼具过程可观测性与多维度评价能力的基准测试体系，以实现对模型能力的更全面、真实的刻画。
链接: https://arxiv.org/abs/2609.19006
作者: Tyler McDonald,Ali Emami
机构: Brock University (布罗克大学); Emory University (埃默里大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Large Language Models (LLMs) and Large Reasoning Models (LRMs) are typically evaluated on challenging benchmarks through dataset accuracy alone, providing no insight into the quality or faithfulness of their reasoning processes. We present WordPolo, a word-finding task where participants must discover an unknown target word using semantic similarity feedback. Players start with zero knowledge, make guesses, and receive distance scores (1 = correct, higher = further away). Success requires interpreting scores to navigate semantic space and systematically narrow the search. This design makes iterative reasoning and adaptive search strategies both directly observable and necessary for success. We evaluate recent LLMs (GPT-4.1, Llama 4, Claude 3.5 Haiku, Qwen 3), LRMs (o4-mini, Deepseek-R1), humans, and a novel heuristic on 1,500 puzzles. Beyond solve rates (which range from 4% to 62%), we introduce progression-based metrics that reveal models often make meaningful progress, insights that accuracy alone would miss. Our analysis shows how reasoning models can be hindered by overthinking and underthinking, while successful models exhibit human-like strategies. WordPolo demonstrates the need for benchmarks that test both reasoning process and outcomes, providing holistic measurements of model capabilities. Our code and dataset can be found at this https URL.
[NLP-14] CompileRover: Revolutionizing Virtual Machine Compiler Optimization with a Tri-Role LLM -Driven Framework
【速读】： 该论文旨在解决虚拟机编译器生成的汇编代码中存在的冗余计算、低效循环结构及次优函数实现等问题，这些问题显著降低了程序的执行效率。其解决方案的关键在于提出一种名为CompileRover的先进优化框架，该框架采用由裁判（referee）、顾问（advisor）和操作员（operator）构成的三角色协同机制，通过融合控制流分析、代码结构变换以及动态执行模式识别等新型优化算法与方法，系统性地克服性能瓶颈。实验结果表明，CompileRover在多个基准测试中均显著优于当前最先进的虚拟机编译器，有效降低了执行开销，提升了数据流一致性，并大幅增强了编译器的整体性能，验证了其在虚拟机编译优化中的有效性与可靠性。
链接: https://arxiv.org/abs/2609.19004
作者: Mingqiao Mo,Yunlong Tan,Hao Zhang
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 20 pages
Abstract:Code optimization plays a crucial role in the development of virtual machine compilers, with optimization frameworks significantly enhancing the performance of generated assembly code. However, existing virtual machine compiler outputs frequently exhibit redundant computations, inefficient loop structures, and suboptimal function implementations, which collectively impair execution efficiency. To address these shortcomings, we propose CompileRover, an advanced optimization framework specifically designed for virtual machine compilers. CompileRover employs a sophisticated three-role collaboration mechanism, comprising a referee, an advisor, and an operator, effectively overcoming performance bottlenecks by leveraging comprehensive optimization algorithms and novel methodologies, including control flow analysis, code structure transformations, and dynamic execution pattern recognition. Extensive evaluations demonstrate that CompileRover consistently surpasses state-of-the-art virtual machine compilers, achieving significant improvements in execution performance across various benchmarks. Furthermore, performance analyses validate that the introduced optimizations notably reduce execution overhead, improve dataflow consistency, and robustly enhance compiler performance, showcasing CompileRover as an effective and reliable approach to optimizing virtual machine compilers.
[NLP-15] Code Consistency Preference Optimization Verification for Language Model Alignment
【速读】： 该论文旨在解决大语言模型在科学任务中数学推理与逻辑一致性不足的问题，尤其针对现有基于Bradley-Terry奖励模型的偏好优化方法无法有效捕捉推理步骤间的逻辑依赖关系和执行一致性缺陷。其解决方案的关键在于构建基于依赖图（dependency graph）的执行一致性评估机制：通过构建包含推理步骤表达式、前提条件及可推导关系的依赖图，计算每一步的执行一致性得分，并将该得分作为附加信息融入训练数据中，实现对生成过程的精细化控制。该方法在MATH和GSM8K基准上分别带来+17.0%和+15.1%的性能提升，并进一步扩展为科学可行性控制框架（Scientific Feasibility Control），最终形成的CCPO模型家族在PhyX多模态物理推理任务中达到50.1%准确率，显著优于DeepSeek-R1与OpenAI o3-mini，同时在α=0.10下实现91.7%的科学有效性覆盖率并减少73%的科学定律违反问题。
链接: https://arxiv.org/abs/2609.19002
作者: Yunlong Tan,Mingqiao Mo,Hao Zhang
机构: 未知
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: 24 pages
Abstract:Execution-based verification enhances large language models’ mathematical reasoning through computational soundness and dependency-aware filtering. However, prior preference optimization methods relying on Bradley-Terry reward models fail to capture the logical dependencies and execution consistency needed for scientific tasks. We propose a method that generates computationally sound solutions with dependency graphs for execution-consistent preference optimization. We first build a scientific reasoning dataset using UltraFeedback prompts, model generations, verification, and consistency results. Then we extract reasoning step expressions, prerequisites, and derivability relationships to construct dependency graphs and compute execution consistency scores. These scores are appended to each step, creating paired training data. Fine-tuning Llama-3-8B and DeepSeekMath-7B yields significant gains: +17.0% on MATH and +15.1% on GSM8K. Extending our Scientific Feasibility Control framework achieves 50.1% accuracy on PhyX multimodal physics reasoning, surpassing DeepSeek-R1 (49.8%) and OpenAI o3-mini (48.2%), with 91.7% scientific validity coverage at alpha=0.10 and 73% fewer scientific law violations, resulting in the CCPO model family.
[NLP-16] Compiled Agency: Frontier General-Purpose Coding Agents Build Winning Game Players from Bare Interaction - from Flappy Bird to StarCraft II and Civilization
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在游戏环境中难以将知识转化为有效策略的问题，尤其是在缺乏外部架构支持的情况下实现自主游戏决策。传统方法虽通过引入感知模块、记忆机制、技能库或规划器等辅助组件，仍无法确保代理在复杂游戏中表现出色。本文提出的核心问题是：前沿编码代理是否能在无任何预先设计策略、算法或架构的前提下，完全自主地从零构建出可执行的游戏控制程序，并在真实游戏环境中取得成功？其解决方案的关键在于提出“Gauntlet”框架——一个无需开发、冻结结果、独立评估的系统流程。该框架仅提供通用编码代理以游戏描述、原始观测/动作接口及空策略文件作为输入，不赋予任何先验知识。在单次自主会话中，代理通过与实时游戏交互，自行探索并生成完整的、可运行的游戏控制器。随后冻结该程序，在未调用模型的情况下进行测试评估。实验表明，新一代代理在程序化肉鸽类游戏中实现了0%至86%的胜率，且展现出显著的代际性能跃迁；在《星际争霸II》中，其生成的原始API控制器击败所有公平内置AI及两个作弊变体；在《文明》（Freeciv）中，单次会话生成的程序可完成全面征服。这一成果首次实现了无需每回合模型调用、无手工战术层的完整策略游戏自主胜利，标志着前沿编码代理已具备捕捉长期战略的能力。所生成的代码为可检查的静态程序，体现“编译式智能”（compiled agency）特性——将开发过程中的经验固化为由模型自动生成的持久可执行代理。
链接: https://arxiv.org/abs/2609.18996
作者: Joey Xiao,Haonan Huang
机构: Princeton University (普林斯顿大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:LLM agents have repeatedly struggled to convert knowledge of a game into competent play, even when researchers build the agent around the model - supplying perception, memory, skill libraries, planners, or executable-policy scaffolds. Rapid progress in coding agents raises two sharper questions: can frontier models now win games at all, and can they win them unaided, building the entire player themselves? We introduce Gauntlet, a develop-freeze-evaluate framework that ports games from small arcades to full commercial-scale titles, behind one deliberately bare contract: a general-purpose coding agent receives a game description, a raw observation/action interface, and an empty policy file - no strategy, no algorithm, no architecture. In a single autonomous session the agent experiments with the live game and engineers a standalone controller; we freeze the result and score it on held-out instances with zero model calls during play. On an unpublished procedural roguelike, held-out success spans 0-86 percent and exposes a sharp generational threshold: every observed session of a newest-generation system outperforms the best session of its predecessor. At full-game scale, a compiled raw-API controller defeats every fair StarCraft II built-in AI and two cheating variants, and single-session programs win complete games of Civilization (Freeciv) by total conquest on held-out seeds. Though at modest rates against novice AI, this is a first: no prior language-agent system had won full games of this genre standalone, without per-turn model calls and a hand-crafted tactical layer. Frontier coding agents begin to track long-horizon strategy. The frozen programs are inspectable. We call this capability compiled agency: development experience compiled into a persistent executable agent whose architecture is built by the model.
[NLP-17] A Benchmark Suite and Ground-Truth Methodology for Formal Verification of IEC 61131-3 Ladder Diagram Programs
【速读】： 该论文旨在解决可编程逻辑控制器（PLC）程序形式化验证领域缺乏标准化评估基准的核心问题。现有数据集或忽略形式化属性，或未涵盖IEC 61131-3标准中的图形化编程语言（如梯形图，LD），且多数私有代码集无法支持可复现的性能评测。为此，研究提出首个融合可控真值（ground truth）与多语言覆盖的基准套件，包含50个程序在83种变体中跨越10个工业场景，以PLCopen扩展标记语言（XML）和结构化文本（ST）格式提供，并为每个程序配备形式化属性、机器可验证的预期结论及违反见证（violation witness），符合软件验证竞赛（SV-COMP）格式。其关键解决方案在于建立三重真值确立机制——通过构造、故障注入或经审计的跨工具共识来确定正确结论，以应对一种具体失效模式：传统安全属性因不可见的非终止行为，会错误地将两个公开的逻辑炸弹数据集中的所有攻击均判定为“安全”。通过基于高效SMT的上下文有界模型检查器（ESBMC）v8.4从源码生成参考真值，确认全部25个图形化基准均可执行，且45个接受变体中有43个与记录结论一致；在有限状态片段（21个基准）上，使用决策过程无关的nuXmv模型检查器验证，其对全部24个互锁变体达成一致，并解决了ESBMC-PLC未能判定的两个基准，从而证实了真值的工具中立性与判别能力。此外，移植过程揭示了前端对序列化格式的差异容忍度以及定时语义在不同工具间的不一致性，凸显了该基准套件在暴露格式与语义碎片化方面的设计意图。相关代码库、模式定义、验证器与重检工具均已作为开源资源发布。
链接: https://arxiv.org/abs/2609.18994
作者: Pierre Dantas,Lucas Cordeiro,Waldir Junior
机构: University of Manchester (曼彻斯特大学); Federal University of Amazonas (亚马逊联邦大学)
类目: Computation and Language (cs.CL); Hardware Architecture (cs.AR); Software Engineering (cs.SE)
备注: 11 pages
Abstract:We present the first benchmark suite for formal verification of Programmable Logic Controller (PLC) programs that combines controlled ground truth with coverage of both textual (Structured Text, ST) and graphical (Ladder Diagram, LD) IEC 61131-3 encodings. Despite growing support for tools, the field lacks standard evaluation benchmarks: existing corpora omit formal properties or graphical dialects, and private program sets preclude reproducible measurement of progress. Our suite comprises 50 programs in 83 variants across ten industrial domains, provided in PLCopen Extensible Markup Language (XML) and ST, each paired with a formal property, machine-checkable expected verdict, and violation witness in the Software Verification Competition (SV-COMP) format. The central methodological contribution is a tripartite ground-truth discipline - verdicts are established by construction, fault injection, or audited cross-tool consensus - motivated by a concrete failure mode where the obvious safety property misclassifies all attacks from two public logic-bomb corpora as safe due to invisible non-termination. Reference verdicts are obtained with the Efficient SMT-Based Context-Bounded Model Checker (ESBMC) v8.4 from source: all 25 graphical benchmarks execute, and 43 of 45 accepted variants match recorded verdicts. On the finite-state fragment (21 benchmarks), nuXmv - a model checker with unrelated decision procedures - agrees on all 24 interlock variants and resolves two benchmarks ESBMC-PLC leaves unknown, confirming tool-neutral ground truth and discriminative power. Porting exposes format and semantics fragmentation: front-ends accept different serializations, and timer semantics vary across tools - phenomena the suite is designed to reveal. The corpus, schema, validator, and recheck harness are released as open artifacts.
[NLP-18] MechSparse: Mechanism-Guided Sparse PEFT Selection Is Task-Shaped
【速读】： 该论文旨在解决在参数高效微调（PEFT）中如何更有效地分配有限的可训练参数预算的问题，尤其关注是否能够利用生成式 AI (Generative AI) 的机制可解释性所识别出的因果信号（causal signals），来指导微调位置的选择，从而超越当前广泛使用的启发式方法。其核心解决方案是提出一种基于归一化激活修补恢复（normalized activation-patching recovery）的评分机制，通过在干净与损坏输入样本上评估注意力头和MLP模块的贡献度，筛选出关键组件进行LoRA/QLoRA微调；进一步引入\methodc，在层内联合子集上施加有界信用分配以增强对协同作用的捕捉。实验表明，尽管因果选择器在主要指标上未优于现有方法，但在特定任务（如斯瓦希里语结构化信息抽取）中表现突出，尤其在跨种子标准差最小、且显著优于随机与梯度/Fisher基线的情况下，显示出良好的稳定性与可解释性优势。研究还揭示了输出结构主导任务（如JSON格式解析）应优先采用激活范数（activation-norm）策略，而内容主导任务则更适合使用因果选择器作为假设性指导。研究释放了完整的掩码、评分、预测及评估文件，支持结果复现与进一步分析。
链接: https://arxiv.org/abs/2609.18961
作者: Son Ha Xuan,Phat T. Tran-Truong,Xuan-Bach Le
机构: RMIT University (皇家墨尔本理工大学); Ho Chi Minh City University of Technology (胡志明市技术大学), VNU-HCM (越南国家大学胡志明市分校)
类目: Computation and Language (cs.CL)
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Abstract:Mechanistic interpretability identifies sparse subsets of heads and MLP blocks that carry specific behaviors. We ask whether such causal signals can guide where to place a small PEFT budget more effectively than the cheap heuristics practitioners already use. \method scores attention heads and MLP blocks by normalized activation-patching recovery on clean/corrupted probes and trains LoRA/QLoRA only on the selected sites; \methodc adds bounded credit for small within-layer joint subsets. We compare against random, magnitude, activation-norm, and gradient/Fisher on Ministral-8B/NF4 in three cells: Swahili span-JSON information extraction (IE) at b=0.25% and 1.0% , and English \to Swahili machine translation (MT) at b=1.0% . The causal selectors never win the primary metric. On the headline IE cell (3 seeds, paired-bootstrap CIs over 600 predictions), \methodc beats random by +0.079 span+type F1 and gradient/Fisher by +0.174 , but trails activation-norm by 0.028 , with the smallest cross-seed std ( \pm 0.003 ). On MT all four selectors lie within 0.30 BLEU and every paired CI includes zero. A schema-versus-span decomposition explains the IE gap: activation-norm captures the rigid JSON routine, while causal scores track content-sensitive sites. We distill a preliminary diagnostic – prefer activation-norm when output structure dominates, treat causal selectors as a hypothesis for content-dominated tasks – and release masks, scores, predictions, and evaluation files for direct replay. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.18961 [cs.CL] (or arXiv:2609.18961v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.18961 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-19] When Audit Quality Fails to Predict Downstream Utility: A Counterfactual Study of Synthetic-Data Selectors for Low-Resource African NLP
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在低资源非洲语言文本分类任务中，基于大语言模型（LLM）判断质量的合成数据选择方法的有效性问题。其核心问题是：当前广泛采用的“高质量合成数据”代理假设——即被LLM判为高质量的数据样本能够提升下游模型性能——在真实场景下可能失效。研究通过在四种非洲语言（阿姆哈拉语、豪萨语、斯瓦希里语、约鲁巴语）和两个文本分类任务（MasakhaNEWS、AfriSenti）上的受控重演实验发现，审计评分（如标签正确性）与下游模型性能（如宏平均F1分数）之间存在显著不一致，各组合条件下斯皮尔曼等级相关系数均值仅为ρ=0.04（中位数为0.00），表明这种偏差并非统计聚合所致。为此，作者提出一种反事实审计框架\method-V2，其关键创新在于同时优化三个审计维度：最高标签正确率（0.904，相比基线提升17.9%）、最低捷径得分以及更低的硬拒绝率（0.162 vs. 0.486）。然而，尽管\method-V2在审计指标上表现更优，其在下游任务中的宏平均F1仍低于另一种方法AlpaGasus，说明审计质量并不必然保证下游实用性。因此，论文的核心方法论启示是：合成数据的质量评估必须在同一保留数据集上同时报告审计指标与下游性能，以避免误导性结论。研究团队已公开审计表格、各选择器保留的数据池及可追溯的声明账本，确保结果可复现与透明验证。
链接: https://arxiv.org/abs/2609.18960
作者: Son Ha Xuan,Phat T. Tran-Truong,Xuan-Bach Le
机构: RMIT University (皇家墨尔本理工大学); Ho Chi Minh City University of Technology (HCMUT) (胡志明市技术大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Quality-aware synthetic-data selection rests on a proxy: examples that an LLM judge rates as good should also help a downstream model learn. In a controlled replay in low-resource African-language classification, we show that this proxy breaks. Across four languages (Amharic, Hausa, Swahili, Yoruba), two classification tasks (MasakhaNEWS, AfriSenti), and five matched-budget selectors, audit rankings and downstream rankings diverge. Within each cell, the Spearman between judged label correctness and Macro-F1 across selectors has mean \rho=0.04 (median 0.00 ), showing that the mismatch is not an aggregation artifact. \method-V2, our counterfactual audit framework, produces the cleanest selected pool on three audit channels at once: highest judged label correctness ( 0.904 vs.\ 0.767 for naive, a 17.9% relative gain), lowest shortcut score, and a hard-reject rate of 0.162 vs.\ 0.486 for naive. AlpaGasus nevertheless leads downstream Macro-F1 ( 0.202 vs.\ 0.163 for \method-V2), and the inversion persists on the five non-degenerate cells. The lesson is methodological: in this controlled setting, audit quality is a property of the selected pool, not a guarantee of downstream utility. Synthetic-data evaluation should therefore report audit and downstream metrics on the same retained sets. We release the audit tables, per-selector retained pools, and a claim ledger that links every reported number to its source row.
[NLP-20] LangSelect: Cost-Aware Target-Language Routing for LLM Code Generation
【速读】： 该论文旨在解决大语言模型（LLM）在代码生成过程中固定目标编程语言所带来的效率与成本问题，尤其是在语言灵活的编程任务中（即同一任务可由多种编程语言实现且通过相同测试验证）。现有方法通常在解码前选定一种目标语言并保持不变，然而研究表明，不同语言实现同一任务的生成词元（token）长度差异显著，导致资源消耗不均。为此，论文提出LangSelect——一种基于验证反馈的路由机制，在生成前动态选择最优目标语言，并在首次尝试失败时启用回退策略。为区分离线路由与端到端生成行为的影响，研究设计了“已验证解复现”（verified-solution replay）和“实时GPT-5生成”两种评估范式。在包含3,000个任务、8种语言的MultiLang-Bench数据集上，复现实验揭示了显著的语言路由优化空间；而在450个保留任务的实时评估中，基于训练集领域启发式的基线方法将包含包装器与入口点开销的总词元数降低50.3%，在92.9%的成功率下完成回退；而采用CodeBERT+元数据学习的选型器则达到最高的93.8%成功率，仅增加3.7%词元。结果表明，输出语言路由能够有效定义单位测试可验证代码生成中的实用成本-正确性权衡边界。
链接: https://arxiv.org/abs/2609.18959
作者: Son Ha Xuan,Phat T. Tran-Truong,Xuan-Bach Le,Nghia Duong-Trung
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:LLM code-generation systems usually choose a target programming language before decoding and treat that choice as fixed. We show that, for language-flexible programming tasks – tasks where several target languages are acceptable and checkable by the same tests – this choice is a measurable cost lever: verified implementations of the same task can differ substantially in generated-token length. We introduce LangSelect, a verification-aware router that selects the target language before generation and falls back when the first attempt fails. To separate offline routing opportunity from end-to-end behavior, we evaluate verified-solution replay, which chooses among already accepted corpus solutions, and live GPT-5 generation, which charges every generation attempt, including failures and fallbacks. On MultiLang-Bench, a 3,000-task, 8-language verified corpus, replay shows substantial language-routing headroom. In live evaluation on 450 held-out tasks, a train-split Domain heuristic baseline reduces harness-proxy tokens, which include wrapper and entrypoint overhead, by 50.3% at 92.9% pass after fallback, while a learned CodeBERT+metadata selector reaches the highest pass after fallback, 93.8%, with a 3.7% token increase. These results show that output-language routing can define a practical cost-correctness frontier for unit-test-verifiable code generation.
[NLP-21] Long-Lived Characters Local Inference: Incremental Memory Maintenance for Game NPCs
【速读】： 该论文旨在解决本地部署的生成式游戏角色（NPC）在长期对话中因记忆更新导致的前缀失效问题。传统方法中，少量记忆修改会破坏已缓存的长序列前缀，造成高昂的重新计算开销，进而影响对话流畅性与多角色协同维护效率。其核心解决方案在于提出一种增量式记忆维护机制，针对量化后的Qwen混合递归-注意力模型，在运行时动态移除过时的注意力键值（KV）条目，于真实序列尾部计算新记忆记录，并保留连续的递归状态及未变化的KV内容。实验表明，该方法可在八轮脚本化维护中保持当前状态与历史关联的完整性，优于固定位置插入或仅保留槽位的替代方案，且避免重复减法错误；注意力分布相似性无法解释语义差异，凸显了维持历史依赖推理状态的重要性。研究主张将角色的推理状态视为需持续维护的历史相关资源，而非仅基于最新记忆文本的临时编码。
链接: https://arxiv.org/abs/2609.18935
作者: Zimu Xu
机构: University of Bern(伯尔尼大学)
类目: Computation and Language (cs.CL)
备注: 18 pages, 6 figures. Supporting numerical snapshots included as ancillary files
Abstract:A game character should not have to reread its entire life before every conversation. For locally deployed language-model characters, however, revising a few memories can invalidate a long reusable prefix. The resulting preparation cost competes with both foreground dialogue and the maintenance of other characters. This matters especially when dialogue feeds game-defined actions and value judgments: a fluent but incorrect account of who owns an item, or whether a transfer has already happened, can corrupt the input to otherwise deterministic rules. We study incremental memory maintenance for long-lived game NPCs in a quantized Qwen hybrid recurrent-attention model. Our runtime removes superseded attention KV entries, computes replacement records at the true sequence tail, and preserves the continuing recurrent state and unchanged KV. Existing local experiments combine multi-update dialogue replays, fixed-input placement ablations, and attention diagnostics. Independent block composition weakens query-conditioned memory selection without a uniform chunk-initial attention collapse. True-tail updates preserve important current-state and historical bindings across eight scripted maintenance rounds; a placement case recovers the full-refill quantity in three reconstructions, while slot-preserving alternatives repeat a double-subtraction error. Attention-distribution proximity alone does not explain these semantic differences. The results motivate treating a character’s inference state as a maintained, history-dependent resource, rather than only a disposable encoding of its latest memory text.
[NLP-22] Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking
【速读】： 该论文旨在解决代理型大模型（agent）评估基准在实际应用中成本过高这一核心问题，其关键挑战在于如何在保持评估效度的前提下实现高效的基准压缩。现有方法主要依赖任务-模型最终得分分布中的冗余性进行压缩，但忽视了代理执行过程中的动态行为信息。为此，作者通过分析大规模代理轨迹，识别出六类与最终性能系统相关的互补性过程信号，提出一种名为DualViewEval的新型基准压缩方法。该方法的核心创新在于从结果（outcome）与过程（process）双视角协同建模，联合利用两者之间的关系，以学习一个精确规模的最小测试集，并精准预测全基准的评分表现。实验表明，在五个代理基准和五种代表性基线上的全面评估中，DualViewEval均取得最优性能；仅使用20个任务即可实现APEX-Agents与BFCL基准上24×至40×的压缩比，同时相比最强竞争方法将平均绝对误差（MAE）降低14.5%–28.2%，并在SWE-bench Verified数据集上使Kendall’s τ相关性相对EssenceBench提升最高达7.2%。所生成的最小测试集还揭示了不同代理间的潜在能力差异，为高效代理模型开发提供了紧凑且具有诊断意义的反馈机制。
链接: https://arxiv.org/abs/2609.18909
作者: Xinshuai Guo,Junjie Wu,Dolly Deng,Yinghui Li,Hai-Tao Zheng,Suncong Zheng,Maxm Pan
机构: Hunyuan Team, Tencent(腾讯混元团队); Tsinghua University(清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Agent benchmarks are substantially more costly to evaluate than conventional LLM benchmarks. Benchmark compression is therefore a natural solution, yet existing methods primarily model redundancy in task–model final-score distributions, which is important in agentic evaluation. To address this limitation, we analyze large-scale trajectories and identify six complementary process signals that are systematically associated with final agent performance. To disentangle agent performance redundancy from a complete perspective, we propose DualViewEval, an agent benchmark compression method that jointly exploits outcome and process relations to learn an exact-size miniset and predict the full-benchmark scores. Across five agent benchmarks and five representative baselines, DualViewEval achieves the best results in all datasets. With only 20 tasks, it achieves 24\times – 40\times compression on APEX-Agents and BFCL, reducing mean absolute error (MAE) by 14.5% – 28.2% over the strongest competitors while improving Kendall’s \tau by up to 7.2% relative to EssenceBench on SWE-bench Verified. The selected minisets further reveal capability differences among different agents, providing compact and diagnostic feedback for efficient agentic model development.
[NLP-23] How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards EMNLP2026
【速读】： 该论文旨在解决欧洲人权法院（ECtHR）在无法定计算公式或明确规则情况下，如何基于案件信息预测非金钱损害赔偿（Non-Pecuniary Damage, NPD）金额这一关键问题。现有法律评估基准涵盖多种任务，但持续性金钱赔偿的预测仍相对未被充分探索。为应对这一挑战，研究提出并构建了首个针对此场景的基准数据集ECtHR-NPD，包含14,575个案件，提供以名义欧元为单位的案件级赔偿金额、时间序列划分以及目标构建与模型输入分离的协议。解决方案的关键在于系统评估多种方法，包括常数预测器、梯度提升树、检索方法、微调编码器语言模型（LMs）、提示式解码器LMs及知识增强型智能体。结果表明，尽管采用了更复杂的生成式语言模型和智能体架构，其表现并未显著优于最优的基于特征的基线模型；所有模型家族均难以准确识别零赔偿案例，并在高赔偿预测上出现校准偏差，尤其在更具挑战性的测试子集上性能进一步下降，凸显了当前开源与专有语言模型在处理此类法律复杂性任务时的局限性，使ECtHR-NPD成为检验前沿模型能力的重要挑战基准。
链接: https://arxiv.org/abs/2609.18908
作者: Yanyi Pu,Damian A. Gonzalez-Salzberg,Zheng Yuan,Nikolaos Aletras
机构: University of Sheffield (谢菲尔德大学); University of Birmingham (伯明翰大学)
类目: Computation and Language (cs.CL)
备注: EMNLP 2026 main conference paper. 28 pages, 5 figures
Abstract:Existing legal benchmarks cover diverse tasks, while continuous monetary remedies remain comparatively underexplored. We introduce ECtHR-NPD, to the best of our knowledge, the first benchmark for predicting non-pecuniary damage (NPD) awards at the European Court of Human Rights (ECtHR) from case information when no statutory formula or explicit calculation rule determines the amount. ECtHR-NPD contains 14,575 cases with case-level awards in nominal euros, chronological splits, and a protocol separating target construction from model input. We evaluate a battery of methods, including constant predictors, gradient-boosted trees, retrieval methods, fine-tuned encoder language models (LMs), prompted decoder LMs, and knowledge-augmented agents. Our results show that more sophisticated LM and agentic approaches do not consistently outperform the strongest feature-based baseline. All model families struggle to identify zero awards and to calibrate high-award predictions, with further degradation on the Challenging test view, making ECtHR-NPD a challenging testbed for current state-of-the-art open-weight and proprietary LMs.
[NLP-24] Structured Claim-Level Discourse Representations for Dense Health Narratives
【速读】： 该论文旨在解决社交媒体健康视频中话语结构复杂、多主题、多立场及多语用功能的原子化主张（atomic claims）在短时对话片段内高度交织的问题。现有方法多依赖粗粒度的主题级、情感或立场表示，难以充分捕捉此类密集叙事中的细粒度语用结构。研究发现平均每分钟包含13.22个原子化主张，由此推动构建更精细的主张级话语表征。其解决方案的关键在于提出一种结构化框架，通过元组形式将原子主张与主题维度、立场态度以及多维语用属性（multidimensional pragmatic discourse attributes）进行关联建模。为支持该框架，研究构建了一个涵盖四个健康领域的基准数据集，包含从60段视频中人工标注的1,191个主张。实验表明，当前大语言模型（LLM）在主题分类和立场预测任务上表现良好，但在高维语用特征刻画方面仍存在显著挑战；同时发现不同话语分析任务对上下文推理形式的需求各异，提示未来系统需采用任务分解与专用推理策略以提升性能。
链接: https://arxiv.org/abs/2609.18905
作者: Farnoushsadat Nilizadeh,Elham Pourabbas Vafa,Shirin Nilizadeh,Eduard Dragut
机构: Temple University (天普大学); University of Texas at Arlington (德克萨斯大学阿灵顿分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Health discourse in social media videos often contains densely entangled claims spanning multiple thematic aspects, stances, evidential frames, and rhetorical functions within short conversational spans. Existing approaches largely rely on coarse topic-level, sentiment-based, or stance-oriented representations that do not adequately capture this structure. Our analysis identifies an average of 13.22 atomic claims per minute, motivating richer claim-level discourse representations. We introduce a structured framework for claim-level discourse analysis in dense health narratives. Our framework models discourse through tuples linking atomic claims with thematic aspects, stance, and multidimensional pragmatic discourse attributes. To support this setting, we construct a benchmark spanning four health domains with 1,191 manually annotated claims from 60 videos. Using this framework, we evaluate automated structured discourse analysis under different discourse context settings. Results show that current LLMs achieve strong performance on thematic categorization and stance prediction, but struggle with high-dimensional pragmatic profiling. We also find that different discourse tasks benefit from different forms of contextual reasoning, suggesting that future systems may require task decomposition and specialized inference strategies.
[NLP-25] PersonaPath: Towards Knowledge-Centric Personalized Learning Path Planning AACL
【速读】： 该论文旨在解决自适应学习系统在学习路径规划中过度依赖基于练习（Exercise-Centric, EC）的推荐机制所导致的个性化不足问题。传统方法仅依据个体对习题的交互记录进行下一步预测，难以有效支持以目标为导向的学习引导，尤其当学习者具有相似练习行为却存在不同学习目标时，其路径规划缺乏精准性。为此，论文提出面向知识中心（Knowledge-Centric, KC）的个性化学习路径规划范式，要求系统综合考虑学习者画像、知识掌握状态以及课程体系中的先决知识结构，从而决策下一阶段应学习的教材、单元及概念。为支持该研究方向，作者构建了PersonaPath基准数据集，包含2,000个细粒度学习者角色与覆盖77个学科的层次化知识图谱（含347本教材、1,751个单元和4,092个知识点）。实验表明，即使最强的大型语言模型（LLM）在基础教育场景下的最终通过率也仅为29.5%，且主要瓶颈在于适应性能力——所有模型在针对个体学习者定制路径方面均未超过44.7%的准确率，凸显当前生成式AI在复杂认知推理与个性化路径生成方面的显著局限。
链接: https://arxiv.org/abs/2609.18861
作者: Yu Liu,Zeming Liu,Tianle Zhang,Zihao Cheng,Yuhang Guo,Kehai Chen,Min Zhang,Yunhong Wang,Haifeng Wang
机构: Beihang University (北京航空航天大学); Beijing Institute of Technology (北京理工大学); Harbin Institute of Technology (Shenzhen) (哈尔滨工业大学（深圳）); Baidu Inc. (百度公司)
类目: Computation and Language (cs.CL)
备注: Accepted to AACL-IJCNLP 2026 Main Conference
Abstract:Adaptive learning systems commonly formulate learning path planning as Exercise-Centric (EC) recommendation, where the next step is inferred from item-level interaction logs. Evaluating goal-oriented guidance additionally requires explicit learner goals and curriculum-scale prerequisites: learners with similar exercise records may need different paths toward their targets. We therefore study Knowledge-Centric (KC) personalized learning path planning, where a planner must reason over learner profiles, mastery states, and prerequisite knowledge structures to decide which textbook, unit, and concept should be studied next. To support this setting, we introduce PersonaPath, a benchmark that pairs 2,000 fine-grained learner personas with a hierarchical knowledge graph of 347 textbooks, 1,751 units, and 4,092 concepts across 77 subjects. We evaluate representative LLMs on PersonaPath. Results show that even the strongest LLM reaches only a 29.5% final pass rate in Basic Education, and that the main bottleneck lies in adaptivity, where no model exceeds 44.7% in tailoring paths to individual learners.
[NLP-26] Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection
【速读】： 该论文旨在解决大视觉-语言模型在识别有害表情包时出现误分类的根本原因，即模型内部是否存在可被利用的证据（evidence），以及是否具备有效将这些证据路由至输出决策的能力。研究发现，模型的失败既可能源于缺乏内部表征的充分证据，也可能源于无法正确地将已有证据导向最终判断。其解决方案的关键在于通过稀疏自编码器（sparse autoencoders）、角色条件探针（role-conditioned probes）、因果干预（causal interventions）与恢复实验（recovery experiments），系统性地识别并验证模型中潜在可访问的中间表示信号。结果显示，在六个有害内容基准任务上，基于稀疏读出（sparse readouts）的监督式可访问性显著优于原生预测性能：Qwen3.5的宏平均F1从0.432提升至0.740，而Gemma-3则从0.532提升至0.714。进一步分析表明，这种性能提升反映的是后训练阶段对特定特征的可访问性增强，而非原始模型预设的决策规则。更重要的是，不同任务中最具影响力的文本角色存在差异，且通过仅校准路由路径即可恢复约93.3%的性能差距，说明“路由”机制本身是决定模型表现的关键瓶颈。此外，跨语言（西班牙语、印地语-英语混杂）评估和鲁棒性控制实验表明，该信号不仅存在于英语场景，且不完全由光学字符识别（OCR）解释，而是依赖于图像与文本的配对视觉证据。因此，研究揭示了在有害表情包分类中，路由机制（routing）而非单纯的表征能力，才是制约模型性能的核心瓶颈。
链接: https://arxiv.org/abs/2609.18860
作者: Girish A. Koushik,Diptesh Kanojia,Helen Treharne
机构: Nature-Inspired Computing Engineering; Department of Computer Science; University of Surrey (萨里大学); Guildford, United Kingdom
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 40 pages, 9 figures
Abstract:When a large vision-language model misclassifies a harmful meme, the failure may reflect missing internal evidence or an inability to route represented evidence to its output. We distinguish these cases in Gemma-3 and Qwen3.5 using sparse autoencoders, role-conditioned probes, causal interventions, and recovery experiments across six harmful content benchmarks, with additional Spanish and Hindi-English code-mixed evaluations. Sparse readouts outperform native prediction on all six primary binary tasks: Qwen averages 0.740 versus 0.432 native macro-F1, while residual reconstruction reaches 0.486 , whereas Gemma improves from 0.532 to 0.714 . These differences reflect supervised accessibility rather than a pre-existing, native decision rule, and the most influential token role depends on the task. Under the evaluated score scales, Qwen silent-feature ablation is 24-63 times more probe-sensitive, whereas routed-feature patching on literal yes/no tasks is 16-140 times more output-sensitive. Calibration-only routing recovers 93.3 % of the mean gap, and probe-distilled LoRA improves native predictions, although shared multi-task adaptation causes negative transfer. A case study of Gemma-3-12B on Facebook Hateful Memes finds a distributed rank-32 image-prompt interaction, reaching 0.756 versus 0.685 native macro-F1. Robustness controls show that the signal extends beyond English, is not explained solely by accompanying OCR, and depends on paired visual evidence. Thus, routing, rather than representation alone, is a recurring bottleneck in harmful meme classification.
[NLP-27] EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation EMNLP2026
【速读】： 该论文旨在解决在处理纵向电子健康记录（Longitudinal Electronic Health Records, EHRs）时，如何高效且可靠地生成可验证的临床推理依据的问题。现有方法中，基于大语言模型（LLM）的处理虽能利用丰富病历信息，但存在计算成本高、结果不可靠、易遗漏关键证据或产生幻觉等缺陷。为此，本文提出EviGen——一种三层可验证临床推理生成框架。其核心解决方案在于：第一层采用患者条件化的检索器，通过可学习查询（learnable queries）精准定位与临床结局具有预测性而非仅文本相关性的证据，并依据预测归因得分进行排序；第二层利用排序后的证据作为结构化支架，引导LLM生成基于真实证据片段的临床推理；第三层引入过程监督的验证器，在推理步骤层面检验生成内容的可靠性，识别并标记不可信陈述。该框架在三个医学预测数据集上均显著优于全上下文LLM和检索增强生成（RAG）基线模型，在预测性能与推理忠实度方面表现更优，且在临床评审的可用性评估中获得更高偏好。
链接: https://arxiv.org/abs/2609.18852
作者: Fengnan Li,Heman Burre,Liwen Sun,Roshni Varma,Matthew M. Engelhard
机构: Duke University (杜克大学)
类目: Computation and Language (cs.CL)
备注: Accepted to Findings of EMNLP 2026. 29 pages, 4 figures, 23 tables
Abstract:Longitudinal electronic health records (EHRs) capture years of patient history across notes, codes, labs, and procedures, and contain evidence needed to reason about likely clinical outcomes. However, comprehensive clinician review of these records is impractical, and LLM-based processing is costly and often unreliable, missing some relevant observations while hallucinating others. We therefore propose EviGen, a three-layer framework for verifiable clinical rationale generation that addresses these challenges. The first layer is a patient-conditioned retriever that uses learnable queries to find evidence predictive of, not just textually relevant to, a clinical outcome and ranks it by prediction attribution scores. The second layer is an LLM generator that consumes this ranked evidence as a scaffold to produce a clinical rationale grounded in the retrieved spans. The third layer is a process-supervised verifier that checks the generated rationale at the reasoning-step level, flagging unreliable claims. Across three medical prediction datasets, EviGen improves prediction performance and rationale faithfulness over full-context LLM and RAG baselines, and is preferred by clinical reviewers in a usability evaluation.
[NLP-28] ReFigBench: Benchmarking Scientific Figure Reconstruction as Editable PowerPoint Artifacts
【速读】： 该论文旨在解决多模态编码代理（Multimodal Coding Agents）在将视觉输入转化为可编辑文档（如可修改的PowerPoint幻灯片）时面临的实际应用挑战，特别是如何在保持原始图表文本、拓扑结构、布局及文档原生结构的同时实现高质量输出。现有评估方法仅关注工具调用片段、API调用痕迹或截图相似度等表面指标，无法区分模型是感知能力不足、规划策略不佳，还是其执行环境（即“ harness”——包括工具层、上下文管理与执行环境）导致失败。为此，作者提出ReFigBench基准框架，基于从arXiv论文中真实获取的1000张科学综述图及其完整溯源信息，系统评估来自四个模型家族的编码代理在两种工作流下的表现：直接代码生成与专用PPTX工作流。评估采用多维度综合方法，包括确定性产物校验、双模型家族重复自动化评分以及盲测人工对比。研究发现，感知能力仍是主要瓶颈，迭代渲染仅部分缓解；工作流设计对性能的影响高度依赖于模型与其所处的harness组合，同一模型在不同harness中可能因专用工作流获得增益或损失；即使最强模型也未能达到评价标准上限。最终揭示出保真度（fidelity）与可编辑性（editability）之间的根本矛盾，成为实用化多模态文档代理的核心挑战。
链接: https://arxiv.org/abs/2609.18844
作者: Liyang Fan,Chi Wei,Yitai Li,Xinping Bi,Guhong Chen,Chenghao Sun,Haoxiang Yang,Qingwen Li,Kai Yan,Hong Li,Bo Li
机构: 未知
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 31 pages, 7 figures, including appendices
Abstract:Multimodal coding agents are expected to turn visual inputs into usable artifacts, and they act through a harness, the layer of tools, context management, and execution environment around the model. Existing evaluations often isolate short tool calls, API traces, or screenshot resemblance, and a low score under these proxies cannot say whether the model saw poorly, planned poorly, or was failed by its harness. We study scientific overview figure reconstruction, an agent task in which a source image must become an editable PowerPoint slide that preserves text, topology, layout, and native document structure. We introduce ReFigBench, a benchmark and evaluation framework built on 1,000 real overview figures retrieved from arXiv papers with full provenance. Coding agents from four model families reconstruct every figure under two workflows, direct code generation and a specialized PPTX workflow, and the strongest model runs inside two commercial harnesses, yielding ten configurations. Evaluation combines deterministic artifact checks, repeated automated scoring by judges from two model families, and blinded human comparisons. Perception remains a bottleneck that iterative rendering only partly repays. Whether workflow effort converts into quality depends on the model together with its harness, since the same model gains from the specialized workflow inside one harness and loses inside the other, and the harness shifts scores even under an identical direct prompt. The specialized workflow erases native connectors in every configuration, human judges still prefer its renderings in most matchups, and even the strongest agent falls short of the rubric ceiling. These results expose the tension between fidelity and editability as the central challenge for practical multimodal document agents.
[NLP-29] Using OCR Heads to Verbalize Image Semantics
【速读】： 该论文旨在解决视觉语言模型（VLMs）如何从像素层面映射到语义层面这一核心问题，聚焦于光学字符识别（OCR）这一具体任务进行深入探究。其关键解决方案在于识别出在四款不同模型中对OCR任务具有因果必要性的注意力头，并发现这些注意力头实为通用型机制，能够对所有图像标记输出可解释的语义特征。例如，将这些注意力头作用于包含“bike”字样的图像标记时，Qwen3-VL-8B会输出“bike”；而作用于鸟翼图像标记时，则输出“feathers”。研究进一步将这些注意力头的权重压缩为一个统一的“语义化透镜”（verbalization lens）变换，从而在各层隐藏状态中揭示出可解释的语义特征。结合到词汇空间的投影后，从第0层即可获得可解释的标签，表明图像表征在早期层中已与语言对齐。此外，通过该变换的逆操作，可实现对非文字概念的编辑（如将自然图像中的拖拉机替换为左轮手枪），为该子空间不仅限于OCR、而是具备更广泛语义操控能力提供了因果证据。研究成果展示了通过解析特定机制，可为理解更广泛的模型可解释性问题提供洞见。
链接: https://arxiv.org/abs/2609.18823
作者: Sheridan Feucht,Benno Krojer,Sarah Wang,Henry Abrahamsen,Byron C. Wallace,David Bau
机构: Northeastern University (东北大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 21 pages, 22 figures
Abstract:How do VLMs map from pixels to semantics? To understand this general question, we focus on a narrow one: studying how VLMs perform optical character recognition (OCR). Across four models, we identify attention heads causally necessary for OCR, and discover that these are in fact general-purpose heads that output interpretable semantic features across all image tokens. For example, pointing these heads at an image token containing the word “bike” causes Qwen3-VL-8B to output “bike,” but pointing them at a bird wing causes the model to output the token “feathers.” We collapse these heads’ attention weights into a single verbalization lens transformation that reveals interpretable semantic features in hidden states across all layers. When combined with projection to vocabulary space, we can obtain interpretable labels starting from layer 0, showing that image representations are in fact aligned with language in early layers. We find that we can also use the inverse of this transformation to edit non-word concepts, e.g., replacing a tractor with a revolver in a naturalistic image, providing causal evidence that this subspace is useful for more than just OCR. Our results are an example of how the study of specific mechanisms can shed light on broader interpretability problems.
[NLP-30] Beyond frequency measures: Can contextual embeddings capture meaning change in scientific texts?
【速读】： 该论文旨在解决传统基于频次的科学计量方法在捕捉领域特定术语语义演变方面的局限性，尤其在识别技术趋势时难以有效反映语义层面的深层变化。其核心问题在于：如何更准确地识别和评估科技文献中术语随时间发生的实质性语义变迁。解决方案的关键在于引入上下文嵌入（contextual embeddings）作为补充信号，通过对比不同时期术语的嵌入“云”（embedding clouds）在多维空间中的分布差异，利用余弦距离、平均成对距离、Hotelling型T²统计量及最大均值差异（Maximum Mean Discrepancy, MMD）等指标量化语义漂移。研究发现，尽管频次方法在与专家判断的一致性上略优（以天体物理学为例，Precision@50为0.62 vs 0.60），但语义指标能识别出仅靠频次分析无法察觉的重要概念演进（如“原初黑洞”），表明语义信号与频次信号具有约0.6的相关性，且具备互补价值。因此，该研究强调将上下文嵌入纳入科学计量分析框架，以实现对技术趋势更全面、深入的动态追踪。
链接: https://arxiv.org/abs/2609.18804
作者: Jianying Liu(STL, BETA, CEIPI),Kim Gerdes(LISN, Qatent, STL),Jean-Marc Deltorn(CEIPI)
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Identifying technological trends is a core scientometric task, yet traditional frequency-based approaches struggle to capture substantial meaning shifts of domain-specific terms. We hypothesise that contextual embeddings can complement frequency dynamics to effectively track diachronic semantic change. We compare frequency and embedding-based approaches across Astrophysics and NLP corpora spanning from 2010 to 2024. Candidate terms are extracted using KeyBERT (utilizing SciBERT as its underlying language model) and filtered for significant frequency increases using Fisher’s exact test. These terms are then evaluated for genuine semantic shift by domain experts to establish ground-truth labels. To quantify semantic drift, each term’s contextual embedding ‘‘clouds’’ from the two discrete periods are compared using multiple metrics: cosine distance, average pairwise distance, Hotelling-type T 2 , and maximum mean discrepancy. Results indicate that frequency-based methods align slightly better with human judgments of ‘‘trend-related terms’’ than semantic metrics (Precision@50 of 0.62 vs 0.60 in Astrophysics). The two signals show a correlation of around 0.6. Several terms identified exclusively by embedding metrics (e.g., ‘‘primordial black holes’’) represent critical conceptual developments invisible to pure frequency analysis. These findings indicate that semantic metrics may capture complementary information, highlighting the value of integrating contextual embeddings into scientometric trend analysis.
[NLP-31] Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes EMNLP2026
【速读】： 该论文旨在解决低资源手语（如加泰罗尼亚手语，LSC）在缺乏音系标注数据的情况下，难以开展手形识别技术研究的问题。现有生成式手语处理方法高度依赖高资源语言（如美国手语，ASL）的详细音系标注，而全球多数手语因数据匮乏无法受益。其解决方案的关键在于提出首个零样本跨语言手形识别框架，通过将手形分解为五种共享的音系特征——涉及手指、屈曲、张开度、拇指位置与拇指接触状态——实现从ASL到LSC的跨语言迁移。该方法利用复合音系距离度量，基于预测的音系特征解码目标语言手形。实验表明，在统一录制格式后，该框架在无任何目标语言视频标注的前提下，实现了80.0%的音系特征准确率和54.5%的预期手形准确率，验证了音系分解作为桥梁的有效性，为低资源手语技术扩展提供了可行路径。
链接: https://arxiv.org/abs/2609.18772
作者: Marcel Granero-Moya,Carolina del Corral Farrarós,Gloria Haro,Coloma Ballester,Ricardo Marques
机构: Gloria Haro; Coloma Ballester; Ricardo Marques; Universitat Pompeu Fabra (庞培法布拉大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the Workshop on Sign Language Processing (WSLP), EMNLP 2026
Abstract:Sign language processing advances rapidly for high-resource languages such as American Sign Language (ASL), yet most of the world’s sign languages lack the phonological annotations new methods require. We present the first zero-shot cross-lingual framework for handshape recognition, transferring from ASL to Catalan Sign Language (LSC). Our approach leverages the decomposition of handshapes into five phonological features – selected fingers, flexion, spread, thumb position, and thumb contact – shared across both languages, to decode LSC handshapes from predicted features via a composite phonological distance metric. We evaluate three architectures (MLP, SL-GCN, SHuBERT) trained on two ASL corpora (PopSign, Sem-Lex) against a 37-handshape, single-signer LSC benchmark. Zero-shot transfer proves viable once recording-format disparities are harmonized, reaching 80.0% phonological feature accuracy and 54.5% expected handshape accuracy. Phonological decomposition thus offers a bridge for extending sign language technologies to low-resource languages without any target-language video training labels.
[NLP-32] FRAUDSkill: Structured Frozen-Weight Skill Optimization for Audio Anti-Fraud Detection
【速读】： 该论文旨在解决大规模音频-语言模型在反欺诈检测中面临的结构性决策约束问题，即如何在保持模型参数冻结的前提下，实现对服务场景识别、欺诈检测及条件化欺诈类型分类等多阶段决策流程的灵活适配。现有微调与提示工程方法通常将任务知识、规则约束嵌入模型参数或人工维护的提示中，难以随欺诈模式和标签策略的演变进行动态调整。其解决方案的关键在于提出FRAUDSkill框架，通过优化外部可训练的技能程序（skill programs）、路由特定策略（route-specific policies）与决策规则组成的独立层，在不修改底层音频-语言模型的前提下，实现结构化输出控制与验证引导的多路径推理，从而确保预测结果符合预定义协议要求。实验表明，该方法在TeleAntiFraud基准上达到73.50%的Macro-F1，较共享冻结模型基线提升31.96%，同时将无效输出降至1.94%，验证了其在保证准确性与合规性的同时具备良好的适应性与可扩展性。
链接: https://arxiv.org/abs/2609.18766
作者: Chengxian Hu,Zhiming Ma,Mingjun Pan,Yifan Wang,Shun Zhang,Qifan Wang,Zhilei Zhao,Yijin Zhou,Yuxi Zhao,Huiyuan Liu,Peidong Wang,Peng Chen
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: 10 pages, 4 figures, including supplementary material
Abstract:Large audio-language models have shown promise for anti-fraud detection by directly processing speech and reasoning over fraud-related evidence. Their deployment, however, requires predictions to follow a predefined label space and a structured decision protocol consisting of service-scenario identification, fraud detection, and conditional fraud-type classification. Existing fine-tuning and prompt-based approaches typically encode task knowledge, constraints, and decision rules into model parameters or manually maintained prompts, making them difficult to adapt as fraud patterns and labeling policies evolve. To this end, we propose FRAUDSkill, a structured frozen-weight adaptation framework that leaves the underlying audio-language model unchanged while optimizing an external layer of skill programs, route-specific policies, and decision rules. We further combine structured output control with validation-guided multi-path inference to ensure protocol-compliant predictions. On the TeleAntiFraud benchmark, FRAUDSkill achieves 73.50% Macro-F1, outperforming the shared frozen-model baseline by 31.96% while reducing invalid outputs to 1.94%. Extensive experiments demonstrate that external skill optimization provides an effective and adaptable solution for structured audio anti-fraud detection without modifying the underlying model. The source code is available at this https URL.
[NLP-33] AntiFraud 2.0: A Refreshable Profile-Grounded and Audio-Based Benchmark for Telecom Fraud Detection
【速读】： 该论文旨在解决音频驱动的电信诈骗检测模型在真实场景下评估时面临的两大核心挑战：一是如何动态纳入新出现的诈骗模式，同时保持已有测试集的稳定性；二是如何有效区分诈骗通话与语义相近的合法通话（near-domain non-fraud），而非依赖主题分离的负样本。其解决方案的关键在于提出TeleAntiFraud 2.0数据集及其配套的混合树反诈骗生成流水线（Mixed-Tree Anti-Fraud Generation Pipeline），该流水线通过将在线诈骗案例摘要转化为基于用户画像的情景化场景，采用混合树结构生成具有共享上下文的诈骗与非诈骗对话路径，并以角色匹配的方式合成验证后的语音，最终实现每月冻结（monthly frozen）的音频、标签、提示、元数据及溯源记录的完整封装。实验表明，当使用近域同类负样本时，多个分类器的宏平均F1（Macro-F1）显著下降至0.65–0.68，暴露出类别先验捷径（class-prior shortcuts）、预测坍缩（prediction collapse）和快照敏感性（snapshot sensitivity）等问题，由此确立了“近域构建”与“坍缩感知报告”作为真实混淆条件下评估音频反诈模型的核心要求。
链接: https://arxiv.org/abs/2609.18748
作者: Huiyuan Liu,Zhiming Ma,Yanxing Liu,Shun Zhang,Qifan Wang,Di Liu,Yifan Wang,Yuyang Deng,Haoyang Meng,Yijin Zhou,Yuxi Zhao,Chengxian Hu,Peidong Wang,Peng Chen
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: 12 pages, 4 figures, including supplementary material
Abstract:Telecom fraud scripts evolve rapidly and are often designed to resemble routine service conversations, creating two key requirements for audio-based telecom-fraud evaluation. First, benchmarks must incorporate newly observed scam patterns without overwriting previously established test sets. Second, they must distinguish fraud from lawful, near-domain calls rather than relying on topic-separated negative examples. We present TeleAntiFraud 2.0, constructed with our Mixed-Tree Anti-Fraud Generation Pipeline and evaluated under a monthly frozen evaluation protocol. The pipeline transforms online fraud-case abstracts into profile-grounded scenarios, expands them through mixed-tree generation, realizes fraud and non-fraud dialogue paths under shared contexts, renders validated dialogues as role-matched speech, and freezes the resulting audio, labels, prompts, manifests, and provenance records for each monthly evaluation set. Each frozen set contains 900 Chinese calls, comprising 600 fraud and 300 near-domain non-fraud cases. Controlled text experiments show that three classifiers achieve perfect macro-averaged F1 (Macro-F1) when evaluated against unrelated or ordinary negatives, but drop to 0.65-0.68 with near-domain sibling negatives. Full-set audio and automatic-speech-recognition plus large-language-model (ASR+LLM) evaluations further reveal class-prior shortcuts, prediction collapse, and snapshot sensitivity. Together, these findings establish near-domain construction and collapse-aware reporting as core requirements for evaluating audio-based telecom-fraud models under realistic confusable conditions. The accompanying research artifact includes the construction code, evaluation scripts, manifests, and documentation. Our dataset and code are available at this https URL.
[NLP-34] A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages EACL2026
【速读】： 该论文旨在解决命名实体识别（NER）任务中因标注质量差或噪声标注导致模型性能受限的问题。其核心挑战在于，低质量的标注数据会严重影响模型训练效果，尤其在资源匮乏的语言场景下更为突出。解决方案的关键在于提出一种基于频率的多步迭代框架，通过自训练（self-training）机制与双阈值（dual-threshold）策略协同提升模型推理置信度，从而逐步优化标注质量。该方法能够有效过滤噪声并修正错误标注，在多个NER数据集上均实现了相较于原始数据集的显著性能提升。此外，研究还探索了生成式大语言模型（Generative LLMs）在低资源语言NER任务中的潜力，为跨语言、少样本场景下的高质量标注生成提供了可行路径。
链接: https://arxiv.org/abs/2609.18739
作者: Toqeer Ehsan,Thamar Solorio
机构: VTT Technical Research Centre of Finland Ltd.(芬兰技术研究中心有限公司); Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)(穆罕默德·本·扎耶德人工智能大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to Findings of EACL 2026
Abstract:Poor quality or noisy annotations in Named Entity Recognition (NER), as in any other NLP task, make it challenging to achieve state-of-the-art performance. In this paper, we present a multi-step framework to enhance the annotation quality of NER datasets by employing automated techniques. We propose a frequency-based iterative approach that leverages self-training and a dual-threshold mechanism to enhance inference confidence. Experimental evaluations on different NER datasets demonstrate significant improvements in NER performance with respect to the original datasets. This work further explores the potential of generative Large Language Models (LLMs) to perform NER for low-resource languages.
[NLP-35] “If I Had to Buy Just ONE: Galaxy S26 Ultra”: Auditing AI-Generated Product Recommendations
【速读】： 该论文旨在解决生成式AI在商业推荐场景中潜在的偏见与非中立性问题，尤其是在广告商业化背景下，用户所获得的消费建议可能受到模型自身偏好或数据来源影响。其核心解决方案在于开展系统性的AI审计，通过构建包含2,528个真实消费者商业咨询问题的ConsumerQ数据集，评估主流聊天机器人（ChatGPT、Google Gemini及其API版本）和AI摘要（Google Search AI Overviews）在产品推荐中的表现。关键发现包括：ChatGPT在79%的产品推荐回复中表现出第一人称偏好，显著高于Gemini的7%和AI Overviews的2%；推荐结果在重复请求中存在显著波动；不同界面间来源域名重合度极低（平均仅5.4%，76.7%无共同域名），且API与前端界面在源信息呈现上存在差异。研究揭示，孤立的响应或仅通过API观察无法全面反映用户实际接触到的商业建议，因此提出独立审计应综合考虑重复响应、面向消费者的交互环境及信息源层级。
链接: https://arxiv.org/abs/2609.18729
作者: Lucas G. Uberti-Bona Marin,Thales Bertaglia,Giovanni Astante,Bram Rijsbosch,Gijs van Dijck,Anikó Hannák,Gerasimos Spanakis,Konrad Kollnig
机构: 未知
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注:
Abstract:Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this raises difficult questions about the bias and impartiality of such advice. In response, we conduct an AI audit of popular chatbots using real commercial-advice queries. First, we curate a dataset of 2,528 real commercial-advice queries (ConsumerQ). Then, we evaluate 1,536 responses to product queries from popular AI chatbots: ChatGPT (chatbot and API), Google Gemini (chatbot and API), and Google Search (AI Overviews). We find that ChatGPT expresses a first-person product preference in 79% of product-recommending responses, compared with 7% for Gemini and 2% for AI Overviews, while the products recommended often change across repeated requests. Displayed sources vary strongly: for the same query, the ChatGPT and Gemini interfaces share only 5.4% of domains on average, with no domain in common in 76.7% of comparisons. APIs provide a different view from their corresponding interfaces, with mean domain overlaps of 12.0% for ChatGPT and 14.8% for Gemini, and also differ in the types and layers of source information they expose. Our findings show that neither isolated responses nor API observations can be assumed to represent the commercial advice consumers encounter. Independent audits of AI-mediated commercial advice should therefore account for repeated responses, consumer-facing conditions, and the source layer being observed.
[NLP-36] LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leverag ing Post-Edits
【速读】： 该论文旨在解决生成式质量评估（QE）模型在未见领域中的性能下降问题，尤其是在实际本地化（localisation）场景下的适用性局限。现有模型如COMETKiwi虽在通用机器翻译评估中表现良好，但在面对本地化特有的关键因素（如数字翻译准确性、空格与标点符号的保留等）时缺乏敏感性，且难以有效对同一源句的不同译文进行精准排序，导致其在跨域迁移中的排名能力显著退化。为缓解这一问题，作者提出基于小规模后编辑数据的系统性微调策略，核心创新在于采用多任务微调框架与简单的分词器干预，以缩小领域差距。实验表明，该方法显著提升了模型在区分优选后编辑版本与被拒初始译文的能力；同时，研究发现人工偏好信号与连续评分之间具有相互稳定作用，强调在量化翻译质量时，必须同时兼顾绝对得分的校准与同源句间译文比较的可靠性，二者缺一不可。
链接: https://arxiv.org/abs/2609.18720
作者: Kathy Hämmerl,Gabriel Bretschner,Joern Wuebker
机构: Technical University of Munich(慕尼黑工业大学); Munich Center for Machine Learning; LILT
类目: Computation and Language (cs.CL)
备注:
Abstract:Learned quality estimation (QE) models such as COMETKiwi are widespread and work well for general machine translation evaluation. However, they are known to struggle on unseen domains, limiting their performance in a real-world localisation context. We show that they are insensitive to some important factors in localisation, such as whether numbers are translated accurately, or even whether the correct number of spaces and punctuation are preserved in a translation. Further, a key capability for optimisation of machine translation is the ability of QE models to accurately rank different translations of a single segment, which suffers significantly from the domain transfer. In the absence of large-scale direct assessment data, we propose principled fine-tuning approaches to reduce the domain gap with even small amounts of post-editing data. Using a multi-task fine-tuning approach and a simple tokeniser intervention, we create a QE model which proves markedly better at distinguishing preferred post-edits from rejected initial translations in a localisation context. We show that preferences and artificial continuous scores stabilise each other, and argue that to calibrate metrics both in terms of their absolute scores and comparisons between translation of the same source, both types of signal are needed.
[NLP-37] racing individual knowledge trajectories in a changing field: the case of general relativity and gravitation
【速读】： 该论文旨在解决历史学研究中如何将宏观领域演进（如广义相对论与引力理论，GRG）与微观个体学者学术轨迹相连接的问题。传统研究多聚焦于领域层面的演变或个别学者的职业生涯，但缺乏跨尺度比较的方法。为此，论文提出一种基于文献计量与语义分析的量化框架，通过将每位作者在特定时期内的出版物及其引用文献，与同一时期、前一时期及后一时期内GRG领域的文献进行对比，实现对学者学术实践与领域动态之间的系统性映射。其解决方案的关键在于构建四个互补的测量指标：自建词汇量（Own Vocabulary）、引用词汇量（Referenced Vocabulary）、嵌入密度估计（Embedding Density Estimation） 以及引文身份（Citation Identity），分别捕捉作者的写作语言特征、引用文献构成、语义邻近度及被引权威结构。研究表明，尽管作者的书写与引用词汇总体上趋向于后期GRG领域主流（即向天体物理与宇宙学方向演进），但其语义邻域常仍锚定在早期文献集中，而共引模式则无明确时间趋势，且两个基于引文的指标常对同一作者给出不同定位。这揭示出学者个体发展轨迹中存在“新词汇—旧结构”的非同步现象，提示需进一步探究其背后的历史机制。该方法具有可迁移性，适用于具有清晰文献边界、充分文本覆盖及作者身份消歧的其他学科领域。
链接: https://arxiv.org/abs/2609.18697
作者: Raphael Schlattmann,Malte Vogl
机构: 未知
类目: Computation and Language (cs.CL); History and Philosophy of Physics (physics.hist-ph)
备注: 43 pages including Supplementary Material (11+1 figures, 4+6 tables). Submitted to Frontiers in Complex Systems
Abstract:Historians have reconstructed the twentieth-century transformation of general relativity and gravitation (GRG) at the field level and through individual careers, but connecting these scales requires a way to compare researchers with the changing field over time. We develop such a comparison, setting a researcher’s publications and references against GRG field literature from the same, earlier, and later two-year periods. Building on Own Vocabulary and Embedding Density Estimation from our earlier two-case study (arXiv:2501.00391), we extend the analysis to the fifty most-published authors in a NASA/ADS corpus of about 180,000 GRG records (1911 to 2000) and add two citation-based measures, Referenced Vocabulary and Citation Identity. The four measures compare an author’s written language, cited literature, semantic neighbourhood, and cited-authority configuration with the surrounding field. The earlier cases suggested that closer field-vocabulary alignment accompanies a denser semantic neighbourhood. Across the fifty authors this holds only partially. Written and cited vocabularies tend to move together, usually resembling later GRG literature as the field turned towards astrophysical and cosmological research. Semantic neighbourhoods more often lie where the field’s publications were concentrated in earlier periods, while co-citation patterns follow no single temporal direction, and the two citation measures frequently place the same researcher differently despite drawing on identical reference lists. Individual trajectories can thus combine vocabulary tied to later field states with older semantic or citation structures, and these divergent cases mark patterns for closer historical investigation. The approach transfers to other fields with defensible corpus boundaries and adequate coverage of texts, references, and disambiguated author identities.
[NLP-38] HearInContext: A Benchmark for Implicit Context in Speech Recognition
【速读】： 该论文旨在解决语音识别中上下文感知能力不足的问题，特别是在处理同音词歧义时，如何有效利用语义线索或显式目标词提示来提升识别准确率。其核心挑战在于：在缺乏明确目标词提示的隐含上下文（implicit context）下，模型难以正确区分具有相同发音但不同语义的词汇；而在显式上下文（explicit context）中，如何最大化利用已知目标词以提高目标召回率。解决方案的关键在于构建一个针对中英双语的新型基准测试集HearInContext，该数据集通过合成语音与支持多种解释的助手回复配对，系统地评估模型在隐含与显式上下文下的表现。实验表明，通过微调Qwen3-ASR-1.7B模型，在隐含上下文中分别在中文和英文任务上将目标召回率提升11.0和11.5个百分点，而对标准语音识别指标（如CER/WER）影响极小（低于0.1个百分点），且性能增益可泛化至未参与微调的显式条件及真实录音中的热词识别场景，验证了上下文建模在提升生成式语音识别鲁棒性方面的有效性。
链接: https://arxiv.org/abs/2609.18680
作者: Yifan Gao,Yao Tian,Hongbin Suo
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注:
Abstract:Contextual ASR can benefit from semantic cues or from target words explicitly provided in the context. We introduce HearInContext, a Mandarin–English benchmark that pairs shared synthetic speech with assistant replies supporting different interpretations. The benchmark comprises 3,764 semantic test cases built around homophones. Implicit contexts exclude candidate words; explicit contexts name the target. No-context and unrelated-context controls measure the benefit of relevant history and sensitivity to irrelevant history. Context-capable models benefit from implicit cues but achieve higher target recall with explicit hints. Fine-tuning Qwen3-ASR-1.7B improves implicit-context target recall by 11.0 and 11.5 percentage points in Mandarin and English, respectively, while absolute CER/WER changes on AISHELL-1 and LibriSpeech remain below 0.1 percentage points. Gains extend to explicit conditions excluded from fine-tuning and to Mandarin hotword recognition on real recordings.
[NLP-39] Voice of Reason : Reinforcement Learning for Spoken Math
【速读】： 该论文旨在解决语音语言模型在数学推理任务上性能显著落后于文本模型的问题，尤其是在缺乏显式推理链（reasoning traces）的情况下，语音模型难以有效处理复杂逻辑推理。其核心解决方案是将强化学习（Reinforcement Learning, RL）引入语音模型GLM-4-Voice的训练流程中，通过可验证奖励机制（verifiable rewards）来优化模型的推理能力。关键创新在于：即使不依赖额外的推理标记或外部推理轨迹，仅通过基于合成语音问答数据的监督微调与强化学习联合优化，即可显著提升模型在GSM8K基准上的准确率；进一步结合流式推理（streaming reasoning）技术后，实现74.8%的自由形式答案准确率，首次达到语音原生模型在数学推理任务上的新基准，标志着语音模型在复杂认知任务上与文本模型差距的实质性缩小。
链接: https://arxiv.org/abs/2609.18677
作者: Timothée Weisselberger,Edouard Graves,Alexandre Défossez
机构: Kyutai; Gradium; Paris, France
类目: Computation and Language (cs.CL)
备注: Accepted at COLM 2026
Abstract:Speech language models enable richer spoken interactions between humans and machines than cascaded systems, allowing access to paralinguistic information and lower latency. However, their accuracy on mathematical reasoning benchmarks has lagged behind those of text models. Reinforcement learning (RL) with verifiable rewards has been instrumental in extending text models’ capabilities for solving complex problems and limiting hallucinations. In this work, we explore applying RL to the GLM-4-Voice speech model (Zeng et al., 2024) to bridge the gap between textual and spoken mathematical problem solving. We first adapt the model to the domain using supervised fine-tuning on synthesized spoken question-answering data. We then show that, even without extra reasoning tokens, RL improves the accuracy on GSM8K beyond levels previously achieved for speech models only with supplementary reasoning traces. When combined with existing streaming reasoning techniques, we show further gains to 74.8% free-form accuracy. This establishes a new state-of-the-art for mathematical spoken abilities with speech-native models.
[NLP-40] Selection Is Retrieval Abstention Is Not: On-Device Tool Routing over 70 Korean-English Actions
【速读】： 该论文旨在解决在无服务器设备上部署具备工具调用能力的AI助手时，因依赖大语言模型（Large Language Model, LLM）进行工具选择与是否调用决策所导致的高延迟与高内存开销问题。其核心挑战在于：传统设计中单一语言模型需同时完成“是否调用工具”和“调用哪个工具”两个决策，而这一过程在资源受限设备上成本过高。为降低开销，现有方案常以检索器（retriever）替代语言模型进行工具匹配，但该方法在处理“无有效工具可用”情形时存在缺陷——检索器仅返回最高得分候选，无法表达“无合适工具”的拒绝信号（abstention），从而导致误路由。本文通过实证分析600条韩语与英语请求，评估了两种决策的性能表现：结果显示，基于字符3-gram BM25的检索器可有效覆盖词汇重叠请求（162/164），但在语义泛化（即同义改写）任务中表现有限（85/166）；引入神经排序器（neural ranker）后，对改写请求的召回率显著提升至0.825（五次试验均值）。更重要的是，区分“在目录内”与“不在目录内”请求的能力依赖于神经组件，其中冻结的多语言编码器multilingual-e5-base在曲线下面积（AUC）达0.806，远超传统分类器（<0.697）。实验表明，工具选择（selection）可通过非神经方法实现，而“是否拒绝调用”（abstention）必须依赖神经组件，因此解决方案的关键在于：将神经组件聚焦于抽象决策（abstention），而非工具选择，从而在保证准确率的前提下，显著降低延迟与内存占用。
链接: https://arxiv.org/abs/2609.18672
作者: Janghoon Lee(Redrob)
机构: Redrob
类目: Computation and Language (cs.CL)
备注: 12 pages, 4 figures, 13 tables
Abstract:An AI assistant that calls tools makes two decisions on every request: which tool to invoke, and whether any available tool applies. In the usual design a single language model makes both, by emitting a call or by declining to emit one. On a device that has to answer without a server, the language model is what makes that design expensive, dominating both the latency and the memory of the router. The common alternative is to remove the model completely and rank the catalog of local actions with a retriever instead. That substitution is not symmetric across the two decisions. A retriever returns its highest-scoring candidate for every input and cannot signal that the catalog holds no valid action. Our earlier study found that constraining a decoder to a tool grammar repairs malformed output without improving the choice. What the substitution costs in each decision has not been measured. We evaluate the two decisions separately over 600 Korean and English requests and a catalog of 70 local actions. The router may also ask for a missing slot, reply, or delegate. Half the in-catalog requests reuse catalog vocabulary and half paraphrase it, separating lexical overlap from the action requested. Character 3-gram BM25 selects 162 of 164 lexically matched requests and 85 of 166 paraphrases. Restricting the candidate set to seven raises the paraphrase figure to a mean of 0.825 over five trials. No classifier over its score features separates in-catalog from out-of-catalog above 0.697 area under the curve, where the frozen encoder multilingual-e5-base reaches 0.806. Using that encoder for abstention alone keeps 376 of the requests local and misroutes 9 of the 150 needing delegation. Abstention, not selection, is where a neural component is required. A neural ranker improves every quality metric and is rejected on latency and memory rather than accuracy.
[NLP-41] DyMT-ESB: Dynamic Multi-Turn Evaluation of Social Bias in User-LLM Interactions EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多轮对话场景中社会偏见（social bias）动态演化问题，特别是现有评估方法因依赖预设或模板化用户输入、固定对话轮次而无法充分捕捉真实交互中偏见随对话进展演变的局限性。其解决方案的关键在于提出一种响应条件化的可控评估协议：通过基于对话历史动态生成后续用户提问，实现对可变轮次对话的灵活评估。实验结果表明，即使在连贯且响应适应性的多轮交互中，LLMs仍表现出显著的社会偏见，呈现出延迟出现、非单调变化及偏见重现等动态特征。这一发现强调了将社会偏见视为逐轮演化的动态现象进行分析的重要性，推动评估范式从静态、预设协议向响应驱动、自适应的动态评估转变。
链接: https://arxiv.org/abs/2609.18649
作者: Rem Hida,Masahiro Kaneko,Daisuke Oba,Danushka Bollegala,Naoaki Okazaki
机构: Institute of Science Tokyo (东京科学研究所); MBZUAI; Third Intelligence (第三智能); The University of Liverpool (利物浦大学)
类目: Computation and Language (cs.CL)
备注: Accepted to Findings of EMNLP 2026
Abstract:Warning: This paper contains examples of stereotypes and social bias. LLMs are increasingly used in interactive settings by the general public, making the evaluation of model behavior in multi-turn conversational scenarios important for safety, including stereotyping-related harms. However, existing multi-turn social bias evaluations often rely on pre-specified or template-based user inputs that do not adapt to model responses and typically assume a fixed dialogue length in advance. In this paper, we study social bias dynamics in response-conditioned multi-turn interactions using a controlled evaluation protocol that generates follow-up user queries from the evolving dialogue history and allows evaluation over variable numbers of turns. Experimental results show that LLMs exhibit social bias even in coherent, response-conditioned multi-turn interactions, revealing late-emerging bias, non-monotonic bias patterns, and bias re-emergence. These results motivate evaluations that extend beyond fixed-turn, pre-scripted protocols. Our findings highlight the importance of analyzing social bias as a turn-level dynamic phenomenon.
[NLP-42] Fallacy Benchmarks Measure Scheme Recognition Not Fallacy Detection
【速读】： 该论文旨在解决现有谬误检测基准测试中“有效论证”（valid argument）类别构建方式带来的误导性问题。当前基准普遍将未标注为谬误的所有样本归入单一“有效”或“无谬误”类，导致分类器可通过学习该类别中的非典型线索（如语言风格、句法特征）实现低假阳性率，而并非真正掌握谬误与正确论证之间的区分能力。其核心问题是：此类别构造掩盖了真实检测性能，使报告的低假阳性率成为人为构造的伪象，而非模型实际识别能力的体现。解决方案的关键在于引入“方案匹配负样本”（scheme-matched negatives），即使用与目标谬误相同论证结构但逻辑正确的反例作为负样本进行评估。实证结果显示，在采用此类更严格的负样本后，假阳性率显著上升（如CoCoLoFa从16.6%升至58.9%，Reddit从5.7%升至62.0%），且分类器对同方案负样本的误判倾向远高于异方案负样本（40.9分更高得分），表明模型仅学会了识别论证模式，而非判断其有效性。这一现象在三种零样本大语言模型（LLM）检测器中亦复现，且当使用经刻意设计的负样本类别时，测量结果明显更低。因此，论文主张必须对“有效”类别的方案覆盖度进行审计，否则报告的假阳性率不可信。为此，作者公开发布了这些构造的“方案干扰项”（Scheme Foils）以供后续研究验证。
链接: https://arxiv.org/abs/2609.18644
作者: Navyansh Singh,Animesh Pathak,Aarav Singh
机构: IIIT Naya Raipur(印度信息学院新赖布尔分校)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 13 pages
Abstract:Fallacy-detection benchmarks pair fallacy classes with a single “valid” or “none” class that takes everything data collection did not label as a fallacy. This construction is misleading: a classifier can learn cues that do well on this class without learning to tell a fallacy from a correct argument. We show that the low false-positive rates benchmarks report are an artifact of how the class is built, not evidence of detection ability. The most informative negative for a fallacy is a correct argument using the same argumentation scheme, and such arguments are at most a few percent of the valid class across the four benchmarks we examined. Evaluated on constructed scheme-matched negatives, false-positive rates rise from 16.6% to 58.9% on CoCoLoFa and from 5.7% to 62.0% on Reddit. That rate depends on how the negatives are written, so we also compare two conditions from the same pipeline that differ only in scheme identity. Classifiers label scheme-matched negatives as the source fallacy type 40.9 points more often than wrong-scheme negatives, which are instead identified as the scheme they actually use 85.9% of the time against 0.4% for the source type. The classifier has learned which scheme an argument uses, not whether it uses it correctly, and on the benchmarks’ own test sets the two are indistinguishable. The same dissociation appears in three zero-shot LLM detectors that never saw these benchmarks, and the measurement is far lower on a negative class that was built deliberately. We release the items as Scheme Foils. A reported false-positive rate should not be trusted as a measure of detection until the valid class has been audited for scheme-matched coverage.
[NLP-43] STRETCH the Boundaries: A Unified Self-Taught Framework for Progressive LLM Evolution
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在自提升训练过程中因固定难度水平无法随模型能力动态演化而导致的能力停滞问题。其核心解决方案是提出STRETCH（Self-Taught Reasoning Evolution via Targeted CHallenge）框架，该框架基于认知支架理论（cognitive scaffolding theory），引入动态“拉伸区”（Stretch Zone）机制，实现问题难度与模型求解能力的持续对齐。在统一参数空间内，模型通过双环协同进化：一个“支架生成器”（Scaffolder）生成具有挑战性且边界推进的适配题目，另一个“学习者”（Learner）则通过强化学习优化自身的求解轨迹。该机制有效稳定了训练过程，缓解了奖励滥用（reward hacking）问题，并促进了推理能力的渐进式增长。实验结果表明，STRETCH在谈判与运筹学基准任务上均显著优于强提示（strong prompting）及领域特定基线方法；进一步的支架配置分析证实，动态难度对齐是实现持续能力提升与推理能力同步演化的关键。
链接: https://arxiv.org/abs/2609.18642
作者: Yajie Yu,Mark Lee,Yue Feng
机构: University of Birmingham (伯明翰大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) often suffer from capability stagnation in self-improvement training because fixed difficulty levels fail to adapt to their evolving proficiency. To address this issue, we propose STRETCH (Self-Taught Reasoning Evolution via Targeted CHallenge), a unified framework inspired by cognitive scaffolding theory. STRETCH introduces a dynamic Stretch Zone mechanism that continuously aligns question difficulty with the model’s solving capability. Within a single parameter space, the model alternates between a Scaffolder that generates adaptive, boundary-pushing challenges and a Learner that that optimizes its solving trajectories through reinforcement learning. This dual-loop co-evolution effectively stabilizes training, mitigates reward hacking and promote progressive reasoning growth. Experiments on both negotiation and operation research benchmarks demonstrate that STRETCH consistently outperforms strong prompting and domain-specific baselines. Further scaffolder configuration analysis shows that dynamic difficulty alignment is critical for sustained capability improvement and synchronized reasoning evolution.
[NLP-44] Weakening Neurons: An Input-Output Functionality in Transformers with Outsize Influence EMNLP2026
【速读】： 该论文旨在揭示基于门控线性单元（GLU）的神经元在大型语言模型（LLM）中学习到的输入-输出行为机制，特别是其在残差流（residual stream）中的功能角色。核心问题在于：尽管现有研究关注神经元在激活时对信息流的增强作用，但对那些可能抑制或削弱特定方向信号的神经元缺乏系统分析。为此，作者提出一种简洁有效的分析方法——计算每个神经元输入（读取）与输出（写入）权重向量之间的余弦相似度；若余弦相似度显著为负，则表明该神经元会减弱其检测到的残差流方向，故称之为“削弱型神经元”（weakening neuron）。该方法的关键突破在于：首次系统识别出削弱型神经元在模型中的分布规律与动态特性——它们主要集中在深层网络，且虽数量稀少却具有高激活频率和强输出影响力；更令人意外的是，这些神经元在门控值（gate value）为负时仍能显著影响模型输出，而传统观点认为负门控值不承载功能性语义。这一发现挑战了对门控机制功能性的固有理解，并揭示了削弱型神经元在模型内部表征演化中的关键作用。
链接: https://arxiv.org/abs/2609.18612
作者: Sebastian Gerstner,Hilal AlQuabeh,Kentaro Inui,Hinrich Schütze
机构: LMU Munich (慕尼黑大学); Munich Center for Machine Learning (慕尼黑机器学习中心); MBZUAI (穆巴达拉人工智能研究所); Tohoku University (东北大学); RIKEN (理化学研究所)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026. Supersedes arXiv:2505.17936
Abstract:We analyze the learned input-output behavior of GLU-based neurons in large language models (LLMs). We propose a simple analysis method: For each neuron, we compute the cosine similarities between its input (reading) and output (writing) weight vectors. In this scheme, a strong negative cosine similarity indicates the neuron weakens the direction it detects in the residual stream, so we call this a weakening neuron. This allows us to gain a number of novel insights. First, we show that nine different LLMs have similar patterns: weakening neurons appear mostly in late layers whereas their counterparts, (conditional) strengthening neurons, are frequent in early-middle layers. Second, we find that weakening neurons display surprising behavior: even though there are few, they activate often and have a large influence on model behavior. Third, weakening neurons have a strong effect on model output when gate values are negative – which is surprising since negative gate values are not expected to encode functionality.
[NLP-45] PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?
【速读】： 该论文旨在解决企业在高敏感领域（如招聘、医疗、金融）中部署生成式AI代理时面临的合规性风险问题，尤其关注在用户施压、管理催促或违规行为具有诱惑性等现实压力情境下，大型语言模型（LLM）是否能够持续遵守规则。其解决方案的关键在于提出PACT（Pressure-Applied Compliance Testing）基准测试框架，通过构建涵盖十二个受监管企业领域、四十八种真实场景的多轮对话任务，系统性地评估AI代理在多重压力条件下的规则遵循能力。PACT通过将每项测试配对“合规规则”与“违规捷径”，并采用多种压力表述和系统提示模式进行测试，确保样本具备明确性、不可操纵性和现实性，避免诱发模型的评估感知行为。研究进一步设计六项互补指标，全面刻画模型在压力下、跨多轮交互中的鲁棒性、透明度及规则适用判断能力，并整合为加权合规率——PACTScore。实验结果表明，22个主流大模型在不同维度上表现出显著的合规差异，即使最强模型在6%至10%的测试项中仍存在误用规则的情况，且普通用户压力使违规率平均上升65%，揭示了当前大模型在实际应用中潜在的重大合规风险，从而推动建立更严格的防护机制和精细化的模型选型策略。
链接: https://arxiv.org/abs/2609.18605
作者: Mika Okamoto,Ansel Kaplan Erol
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 26 pages, 12 figures, 17 tables. Includes technical appendix; Dataset: this https URL Code: this https URL
Abstract:As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and finance. In these contexts, compliance with rules specified in an agent’s system context is a first-order legal concern. Currently, no evaluation framework systematically measures which LLM models tend to violate compliance rules, especially under pressure from a persistent user, a hurried manager, or circumstances where violation is convenient or attractive. We introduce PACT (Pressure-Applied Compliance Testing), a benchmark for rule-following under pressure in AI agents assisting employees in daily tasks across twelve regulated enterprise domains and forty-eight scenarios, each set in a realistic multi-turn conversation. Each benchmark item pairs a standing rule against a rule-violating shortcut, and applies a battery of pressures across different wordings and system-prompt modes. We construct PACT component by component under strict LLM-as-judge auditing to ensure samples are unambiguous, ungameable, and realistic enough to avoid eliciting evaluation-aware behavior. We use PACT to profile LLM compliance across six complementary metrics that create a holistic picture of an AI assistant’s robustness under pressure and throughout multi-turn conversations, its transparency, and ability to correctly discern where a rule applies. We aggregate this profile into PACTScore, a reliability-weighted compliance rate over all items and modes. Our results across 22 common LLM models spanning multiple providers and sizes show substantial variability in compliance across models and metric dimensions. Even the strongest assistants mis-apply a rule on 6 to 10% of items, and ordinary user pressure raises the violation rate by 65% on average. PACT highlights compliance risks in LLM assistants, motivating guardrails and careful model selection.
[NLP-46] A Probe Shift Is Not a Fairness Fix: The Limits of Representation Steering in Speech Models
【速读】： 该论文旨在解决自动语音识别（ASR）系统在不同说话人群体间存在不均衡的词错误率（Word Error Rate, WER）问题，探究预训练ASR编码器中可线性读取的与说话人相关的属性（如性别/性别、年龄、母语/口音）是否能提供有效方向以缩小群体间的WER差距。其解决方案的关键在于：通过在多个编码器层中探测这些属性的可读性，构建中心点（centroid）和基于探针（probe）的方向向量，并将其注入特定层级以观察下游任务中的探针轨迹与WER变化的关联性。研究发现，性别标签具有高度可读性（最高宏平均F1达0.941），母语/口音标签亦显著高于随机水平（0.544–0.696），而年龄则较弱（0.354–0.397）。尽管部分干预导致目标组的绝对WER下降（均低于0.7个百分点），且9个实验的95%配对自助法置信区间完全低于零，但多数情况下，即使局部目标类别的探针准确率从8.09%提升至99.87%，WER反而恶化。这表明，线性可读性并非因果使用的证据，也无法作为可靠的偏见缓解手段。因此，研究强调应从表征、传播及任务三个层面协同评估语音偏见干预措施的有效性。
链接: https://arxiv.org/abs/2609.18533
作者: Nicolas Bourrel,Abderrahmane Issam,Gerasimos Spanakis
机构: Maastricht University (马斯特里赫特大学)
类目: Computation and Language (cs.CL)
备注: Accepted at IMPACT-SPEECH 2026
Abstract:Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representations. We ask whether speaker-linked attributes that are linearly readable from pretrained ASR encoders yield useful directions for reducing group word-error-rate (WER) gaps. Across Whisper-medium, HuBERT-large, and Wav2Vec2-large on Common Voice and the Speech Accent Archive, we probe every encoder layer for metadata-derived sex/gender, age, and native/accent labels; construct centroid and probe-derived directions; inject them at selected layers; and compare downstream probe trajectories with matched WER changes. Sex labels are highly decodable (best macro-F1 0.924–0.941), native/accent labels are also above chance (0.544–0.696), and age is weaker (0.354–0.397). Of 22 post-selected reruns, nine have 95% paired-bootstrap intervals entirely below zero, yet every absolute source-group WER reduction is below 0.7 percentage points. Conversely, a local target-class probe rate can rise from 8.09% to 99.87% while WER worsens. Linear readability is therefore neither evidence of causal use nor a reliable mitigation method. Our results motivate evaluating speech-bias interventions jointly at representation, propagation, and task levels.
[NLP-47] Machine Translation between English and Syriac (East Syriac Dialect) using Statistical Machine Learning
【速读】： 该论文旨在解决亚述语（Syriac）作为濒危语言在自然语言处理（NLP）领域长期被忽视的问题，具体表现为缺乏公开可用的平行语料库以及其书写系统（尤其是马德尼凯亚体，Madnkhaya script）的正字法复杂性导致计算语言学研究进展缓慢。针对这一挑战，研究提出的关键解决方案是构建首个基于短语的统计机器翻译（SMT）模型，用于英-亚述语互译任务。核心创新在于：通过从完整英文圣经与亚述语圣经中提取并人工校对38,847句对句子，创建了首个系统化整理的英-亚述语平行语料库；同时采用去变音符号处理与字节对编码（Byte-Pair Encoding, BPE）进行分词，有效缓解了因正字法稀疏性带来的模型训练难题。实验采用Moses框架，通过多组配置对比优化语言模型阶数、扭曲限制、分割方案及操作序列模型的引入，最终在词级BLEU得分上达到23.54，并经11名母语者评估验证了翻译的可接受性（平均充分性与流畅性分别为3.42和3.34）。该工作不仅为亚述语提供了首个可复现的机器翻译基线，且公开了语料库、代码与训练模型，为未来该濒危语言的NLP研究奠定了基础。
链接: https://arxiv.org/abs/2609.18529
作者: Hadiana Sliwa,Hossein Hassani
机构: University of Kurdistan Hewlêr (库尔德斯坦大学赫莱尔分校)
类目: Computation and Language (cs.CL)
备注: 17 pages, 4 figures, 8 tables
Abstract:UNESCO considers the Assyrian (Syriac) language an endangered language. Although Assyrians speak the language worldwide, the speaking population is uncertain (ranging from 500,000 to 1,500,000). Syriac is also one of the least studied languages in Natural Language Processing (NLP). Despite advances in Machine Translation (MT) over the past decade, the lack of publicly available corpora and the orthographic complexity of the Syriac script, specifically the Madnkhaya script, have left this language entirely ignored in the computational linguistics literature. This study develops the first phrase-based Statistical MT (SMT) model for English-to-Assyrian MT using the Moses framework. We created a dataset of 38,847 sentence pairs from the complete English and Syriac Bible, merging a pre-existing New Testament dataset with an Old Testament built from scratch through PDF extraction, using custom segmentation scripts and manual alignment review by three bilingual annotators. The Syriac side of the corpus undergoes diacritic removal and Byte-Pair Encoding tokenization to reduce orthographic sparsity before training. We trained and evaluated six models using different configurations and splitting-scheme ratios, language model order, distortion limits, and the inclusion of an Operation Sequence Model. The best-performing configuration achieves a word-level BLEU score of 23.54. Human evaluation by 11 native Assyrian speakers resulted in mean adequacy and fluency scores of 3.42 and 3.34 out of 5, respectively. These results are consistent with comparable low-resource SMT models trained on Biblical corpora for morphologically rich Semitic languages. The corpora, scripts, and trained model are publicly available, providing the research community with the first systematically curated English-Syriac dataset and a reproducible baseline for future MT and broader NLP work on this endangered language.
[NLP-48] Align Integrate and Fire: Efficient Token-Level Alignment for Zero-Shot SpeechLLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理语音输入时的效率瓶颈问题，特别是如何高效地将语音理解能力扩展至语音-文本任务中。现有方法普遍存在两大缺陷：一是依赖计算开销巨大的全模型微调，二是采用参数高效的投影器时面临序列长度不匹配和全模型监督成本过高的问题。本文提出了一种名为“对齐连续整合与放电”（Aligned Continuous Integrate-and-Fire）的高效框架，其核心创新在于通过显式的动态时间规整（Dynamic Time Warping, DTW）对齐机制，将连续的声学帧动态压缩为与目标文本完全匹配的离散标记长度，从而在初始训练阶段仅需轻量级距离度量即可建立稳健的声学到语义映射，无需进行昂贵的大型语言模型前向传播。在后续微调阶段，提出一种内存高效的知识蒸馏目标，仅针对单个语言模型层进行优化，在显著降低计算成本的同时，性能可媲美全模型交叉熵训练。实验表明，该方法在自动语音识别（Automatic Speech Recognition）与语音翻译任务上均优于现有参数高效基线。
链接: https://arxiv.org/abs/2609.18516
作者: Abderrahmane Issam,Yusuf Can Semerci,Jan Scholtes,Gerasimos Spanakis
机构: Maastricht University (马斯特里赫特大学)
类目: Computation and Language (cs.CL)
备注: Accepted at WMT2026
Abstract:While Large Language Models excel in natural language processing, efficiently extending their capabilities to spoken input remains a significant challenge. Existing methods for building SpeechLLMs often rely on computationally expensive full-model fine-tuning, or employ parameter-efficient projectors that suffer from inefficient token sequence lengths and costly full-model supervision. In this paper, we introduce Aligned Continuous Integrate-and-Fire, a highly efficient framework for zero-shot speech processing. Our method dynamically compresses continuous acoustic frames into the exact discrete token length of the target text utilizing explicit Dynamic Time Warping alignments. This allows our initial training stage to establish a robust acoustic-to-semantic bridge using lightweight distance metrics, entirely bypassing the computationally expensive LLM forward pass. For subsequent fine-tuning, we propose a memory-efficient knowledge distillation objective that targets a single LLM layer, performing competitively with full-model cross-entropy training at a fraction of the computational cost. Through extensive evaluations on Automatic Speech Recognition and Speech Translation, we demonstrate that our method achieves superior performance compared to prior parameter-efficient baselines.
[NLP-49] Size Matters: Foundation Model for Czech HTML documents
【速读】： 该论文旨在解决在高流量工业环境下构建通用且高质量的网页文档表示所面临的挑战，核心问题包括现有方法依赖大型模型导致计算成本过高、忽视HTML固有的结构信息，以及受限于短上下文窗口而难以有效处理真实网页内容。其解决方案的关键在于提出一种参数量仅为1.54亿的轻量化基础模型——HTML-LM，采用面向HTML的训练策略与ModernBERT架构，通过多任务学习（包括掩码语言建模、词袋预测及来自大语言模型的对比性蒸馏）在1亿条网页文档上进行训练，充分挖掘网页的文本与结构双重特征。该模型在捷克互联网领域分类与回归任务中达到新的性能标杆，显著优于更大规模编码器和小型大语言模型，并已成功部署于生产环境，实现每秒处理数千篇网页文档的能力，同时开源共享以推动社区发展。
链接: https://arxiv.org/abs/2609.18494
作者: Martin Dvořák,Vít Tlustoš,Artyom Voronin,Martin Habrovec,Kateřina Podlesná,Barbora Rišová,Josef Vonášek
机构: Seznam.cz(捷克共和国布拉格)
类目: Computation and Language (cs.CL)
备注:
Abstract:Creating universal, high-quality representations of web documents in high-traffic industrial environments requires models that are both performant and economic. Existing approaches, however, often depend on large models, overlook the structural information inherent in HTML, or are constrained by short context windows, limiting their ability to process real-world web pages. We present HTML-LM, a compact foundation model with 154 million parameters that addresses these limitations through HTML-aware training and a ModernBERT-based architecture. It was trained on 100 million web documents using multiple objectives, including masked language modeling, bag-of-words prediction, and contrastive distillation from large language models. Consequently, HTML-LM sets a new state-of-the-art for classification and regression applications in the Czech Internet domain, surpassing both larger encoders and small-sized LLMs. The model is deployed in production, processing thousands of web documents per second, and released to the community under the CC BY-NC 4.0. this https URL.
[NLP-50] ActionPiece: Rethinking Action Tokenization for Autoregressive Vision-Language-Action Models
【速读】： 该论文旨在解决当前自回归视觉-语言-动作（VLA）模型中动作分词器（action tokenizers）在动作重构过程中对动作调整关系保真度不足的问题。现有方法多依赖点对点的重建误差指标（如均方误差，MSE），但此类指标无法充分反映不同演示中动作微调的上下文依赖性是否被准确保留；尤其在压缩后，相似动作可能趋同于某一代表性运动，导致不同情境下的细微调整被弱化、扭曲甚至反转。为此，论文提出物理秩一致性（Physical Rank Consistency, PRC），用于衡量分词后重构动作在局部物理距离排序上的保持能力，从而评估动作间相对关系的忠实性。其核心解决方案是提出ActionPiece框架，通过联合监督表征学习与量化过程中的物理关系：一方面在编码器与量化特征空间中约束近-远距离的排序一致性，另一方面通过对码字分配分布施加相同的排序正则化，实现对动作间相对关系的显式建模。该方法在保持高点对点重建精度的同时，显著提升了动作关系的保真度，在相同Qwen3-VL-4B策略训练设置下，于LIBERO任务上达到94.8%的成功率，并在未见的LIBERO-Plus、SimplerEnv及VLA-Arena L0-L2等基准上分别取得68.8%、71.9%和51.5%的表现。组件消融实验进一步验证了两项目标协同作用对提升PRC与策略成功率的关键价值，证明了对动作物理关系进行显式监督对于高质量动作分词的重要性。
链接: https://arxiv.org/abs/2609.18487
作者: Shijie Lian,Bin Yu,Zhaolong Shen,Xiaopeng Lin,Yichao Du,Zhirui Zhang,Laurence T. Yang,Kai Chen
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Action tokenizers play a central role in autoregressive vision-language-action (VLA) models, determining both the targets for policy training and the executable commands recovered from predicted tokens. Their fidelity is commonly evaluated using pointwise reconstruction metrics such as mean squared error (MSE), yet small individual errors do not fully characterize how faithfully action adjustments across demonstrations are preserved. After compression, similar actions may still cluster around a representative motion, while the adjustments needed for different contexts are diminished, distorted, or even reversed. We introduce physical rank consistency (PRC) to measure how well tokenization preserves local physical distance rankings after reconstruction. Evaluating decoded actions provides a common reference across token vocabularies and decoder architectures, complementing pointwise accuracy with a measure of relational fidelity. We further present ActionPiece, which preserves physical action relationships through joint supervision of representation learning and quantization. Physical rank preservation supervises near-far ordering in encoder and quantized feature distances, while quantization regularization applies the same ordering to codeword assignment distributions. Both objectives augment reconstruction, producing discrete action tokens for standard autoregressive policy learning and execution through a frozen decoder. Under the same Qwen3-VL-4B policy training setup, ActionPiece achieves 94.8% on LIBERO and 68.8% on unseen LIBERO-Plus, with additional evaluations reaching 71.9% on SimplerEnv and 51.5% across VLA-Arena L0-L2. Component ablations show that the two objectives jointly improve PRC and policy success, demonstrating the value of physical relationship supervision for action tokenization.
[NLP-51] Divide and Conquer: Mixture-of-Bottleneck Experts in Informative Ordinal Space for Video-based Multimodal Sentiment Analysis
【速读】： 该论文旨在解决视频多模态情感分析（Video-based Multimodal Sentiment Analysis, MSA）中模态融合缺乏任务感知性、忽视情感的序数特性以及难以捕捉模态间多样化独特与协同线索的问题。现有方法通常将情感预测视为单一任务，未能充分建模情感极性与强度之间的层次关系，且融合策略难以有效提取各模态中任务相关的紧凑表示。其解决方案的关键在于提出一种基于信息瓶颈理论的混合瓶颈（Mixture-of-Bottleneck, MoB）框架：首先将MSA重新建模为一个序数回归问题，并将其解耦为极性识别与强度预测两个子任务；随后，针对不同模态分别分配专用于极性或强度的专家网络，利用信息瓶颈学习机制使各专家生成去冗余、去噪声的紧凑且任务相关表征；最后通过引入硬挖掘策略的多模态瓶颈路由融合模块，动态聚合各专家潜变量，实现对序数情感空间的精准预测。该方法不仅显著提升了跨数据集与语言模型上的性能，还能够细致捕捉模态内与模态间的动态交互，增强了对细微情感信号的可解释性定位能力。
链接: https://arxiv.org/abs/2609.18470
作者: Ronghao Lin,Qiaolin He,Zefeng Lu,Yichu Liu,Li Huang,Sijie Mai,Haifeng Hu,Yap-peng Tan
机构: Shenzhen University (深圳大学); Sun Yat-sen University (中山大学); Guangzhou University (广州大学); South China Normal University (华南师范大学); VinUniversity (VinUniversity); Nanyang Technological University (南洋理工大学); Desay SV Automotive Co., Ltd (比亚迪汽车有限公司)
类目: Multimedia (cs.MM); Computation and Language (cs.CL)
备注:
Abstract:Video-based Multimodal sentiment analysis (MSA) must handle information from text, audio, and image sequence in human speaking videos, yet current methods often fail to integrate modalities with task awareness. Most models treat video sentiment prediction as a single task, overlooking its ordinal nature, and their fusion strategies struggle to capture diverse unique and synergic cues across modalities. To address these limitations, we adopt a divide-and-conquer perspective by reformulating MSA as an ordinal regression problem and decoupling it into polarity recognition and intensity prediction. Driven by information theory, we introduce a Mixture-of-Bottleneck (MoB) framework that assigns different latents to polarity- and intensity-specific experts for different modalities. With the learning of information bottleneck, each expert learns compact and task-relevant representations while filtering out redundancy and noise. A multimodal bottleneck routing fusion module then fuses these expert latents with hard mining strategy, guiding the prediction in the ordinal sentiment space. Extensive experiments on 4 MSA datasets and 4 language models show that MoB effectively leverages informative latents from diverse modalities and captures general sentiment structure. Beyond stronger performance, MoB comprehensively captures fine-grained intra- and inter-modal dynamics, enabling more trustworthy localization of nuanced video sentiment signals.
[NLP-52] Disentangling Long-Term Memory via Latent Neuro-Symbolic Reasoning
【速读】： 该论文旨在解决个性化智能体在处理长期交互历史时，难以有效建模显式偏好与隐式行为证据的难题。现有方法存在两大局限：早期的扁平化检索方法独立评分记忆片段，忽略信息的分布式特性；而当前的结构化记忆框架依赖于与查询无关的静态图结构，无法捕捉上下文相关的关联关系。此外，原始文本记忆本身具有高度纠缠和噪声特征，导致细粒度个性化与跨会话推理计算开销巨大。为此，论文提出LGM（Latent Graph Memory）这一新型神经符号框架，其核心创新在于将长期记忆的解耦过程从离散空间迁移至连续潜在空间。关键解决方案包括：(i) 采用稀疏自编码器设计定制化的潜在图构建机制，根据每个查询将历史交互映射为潜在记忆节点，并将记忆痕迹解耦为稀疏概念激活，动态生成与查询相关的边权重；(ii) 引入图编码器，以查询嵌入作为条件偏好，引导在任务特定潜在子图上进行非线性消息传递，从而生成高表达力的记忆表征。大量实验表明，LGM在长时个性化基准测试中显著优于现有最先进方法，在捕捉显式与隐式偏好方面均表现出更强能力，并支持生成个性化响应。
链接: https://arxiv.org/abs/2609.18461
作者: Cai Ke,Xinghao Chen,Xiaoyu Shen,Keyu Chen,Siyu An,Junnan Dong,Ruifeng Xu,Ruizhi Qiao,Xing Sun
机构: Tencent Youtu Lab(腾讯优图实验室); Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, EIT, Ningbo, China(浙江省工业智能与数字孪生重点实验室，宁波，中国); Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China(香港理工大学计算系，中国香港，中国); Shenzhen Loop Area Institute, Shenzhen, China(深圳环区研究院，中国深圳，中国)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Personalized agents are required to reason over long-term history interactions to infer both explicit preferences and implicit behavioral evidence. While early flat retrieval methods score memory fragments independently and neglect the distributed information, current structured memory frameworks rely on query-agnostic static graphs that fail to capture the context-dependent relations. Crucially, raw textual memories are inherently entangled and noisy, making fine-grained personalization and cross-session reasoning computationally prohibitive. To this end, we present LGM, a novel neuro-symbolic framework that shifts long-term memory disentanglement into a continuous latent space. Specifically, (i) instead of persisting fixed graphs, we design a tailored latent graph construction with a sparse autoencoder. Subject to each query, it maps historical interactions into latent memory nodes and disentangles the memory traces into sparse concept activations, dynamically synthesizing query-aware relational edge weights. (ii) A graph encoder then treats the query embedding as a conditioning preference to direct non-linear message passing across the task-specific latent subgraph. This yields a highly expressive memory representation for effective activations. Extensive experiments on long-term personalization benchmarks demonstrate that LGM significantly outperforms state-of-the-art baselines in capturing both explicit and implicit preferences while enabling personalized responses.
[NLP-53] M-SQE: Multilingual Skill Quality Estimation for Enhancing Language Equality in Agent ic Skill Use
【速读】： 该论文旨在解决多语言环境下生成式智能体（Agent）技能库中存在的语言不平等与文化偏差问题，即当前主流技能库高度依赖英语，导致低资源语言（如斯瓦希里语、印地语）缺乏本语言技能内容，致使查询与检索结果语言不匹配，严重降低检索准确率与召回率。其核心解决方案是提出M-SQE（Multilingual Skill Quality Estimation），一个后检索阶段的多语言技能质量评估框架，通过“理论视角”（Theory view）衡量技能的内在质量与“行动视角”（Action view）评估其任务导向实用性，二者融合为领域条件化的最终评分，以筛选出真正可用的技能。实验表明，M-SQE在通用、工具使用和文化任务三个领域均显著优于现有基线，平均提升至少3.5分，尤其在低资源语言上表现突出（印地语提升12.9个百分点，斯瓦希里语提升5.6个百分点），并覆盖六大文化区域，有效推动了智能体技能应用的多语言与跨文化公平性。
链接: https://arxiv.org/abs/2609.18445
作者: Yilun Liu,Shimin Tao,Minggui He,Chenxin Liu,Li Zhang,Chen Liu,Miao Zhang,Jiaxin Guo,Min Zhang,Liqun Deng,Xiaojun Meng,Daimeng Wei
机构: 未知
类目: Computation and Language (cs.CL)
备注: 17 pages, 6 figures
Abstract:Agent skills, reusable procedural documents that extend LLM agents beyond their parametric memory, have become an important interface for deploying agents on real-world tasks. Community-maintained skill libraries built around this interface are growing rapidly. However, this ecosystem remains deeply English-centric: our audit finds that low-resource languages such as Swahili and Hindi have no in-language skill content, so retrieval often returns a skill written in a different language than the query, degrading accuracy and recall. A practical solution is to synthesize in-language skills for retrieval but the quality can be unreliable, so relevance in this setting alone often surfaces a related but unusable candidate. To address this, we propose M-SQE, a post-retrieval Multilingual Skill Quality Estimation framework that scores candidates via a Theory view for intrinsic quality and an Action view for task-grounded utility, unified into a domain-conditioned final score. We evaluate M-SQE across three skill-use domains: general, tool-use, and cultural tasks. Empirically, we build three-layer candidate skill pools mirroring today’s ecosystem, where M-SQE’s task success exceeds existing baseline’s average by at least +3.5 points across three different retrievers. Particularly, M-SQE lifts the lowest-resource languages most (+12.9pp on Hindi and +5.6pp on Swahili) and achieves strong performance across all six culture regions, thereby moving agentic skill use toward linguistic and cultural equality.
[NLP-54] Planning or Improvisation? Stress-Testing the Poetry Planning Site on Open Models and Open Cross-Layer Transcoders ATC
【速读】： 该论文旨在检验Lindsey等（2025）提出的“Claude 3.5 Haiku在生成诗句前即规划押韵”这一结论的泛化能力，重点探究其背后的因果机制是否可在更广泛的模型与设置下复现。研究将原假设分解为三个关键条件：位置特异性（C1）、换行符位置身份识别（C2）以及换行符驻留的押韵规划（C3），并在七组实验单元中对四款参数量介于0.6B至2.6B之间的开源大模型及六种开源跨层转换器（Cross-Layer Transcoders, CLTs）进行测试，所有实验均在单张消费级GPU上完成。结果表明，仅条件C1具有普遍性：在全部7个实验单元中，且在444组提示-注入对中有247组可检测到效应，但有效干预位置始终为紧邻输出发射的最后一个提示词，且仅有两个模型达到行为上显著的概率水平。而条件C2与C3均未被任何探测器捕获——对所有活跃特征的普查显示，换行符处并无押韵预期特征的富集；进一步的实验证明，尽管干预强度强但仅持续一个词元，导致注入词成为生成句首词（703/720样本），而目标押韵词仍位于六词之后，无法被有效影响。最终消融实验移除转换器，直接对换行符残差进行全层修补，基于最小差异诗体的第三行结尾更换押韵，仅在11/1260条生成结果中成功转移押韵，相较于基线提升4%，设计分辨率仅为1.4%。综上，研究认为原结论的机制并未被复制，而是揭示了在当前模型规模与架构下，押韵规划的因果节点实际位于输出发射邻近位置，而非换行符本身。因此，该工作并非否定原结论，而是将其界定为特定边界条件下的现象，即仅能复现图13的表征形态，而非其内在机制。
链接: https://arxiv.org/abs/2609.18440
作者: Éric Jacopin
机构: Cosmic AI(宇宙AI); France(法国)
类目: Computation and Language (cs.CL)
备注: 17 pages, 3 figures, 8 tables. Code, data and analysis scripts: this https URL . An earlier version was submitted to the BlackboxNLP 2026 special track on reproducibility and reliability in interpretability analyses; this version adds a rerun composition-horizon experiment (36 runs, 8,640 sampled lines) and a transcoder-free activation-patching test
Abstract:Lindsey et al. (2025) report that Claude 3.5 Haiku plans rhymes: features for candidate rhyme words are active on the newline before a line is written, and a suppress-and-inject intervention redirects the line only when applied there (their Figure 13). We test how far this generalizes on seven cells crossing four open models (0.6B to 2.6B parameters) with six open cross-layer transcoders (CLTs), on one consumer GPU, decomposing the claim into position specificity (C1), newline site identity (C2), and a newline-resident plan (C3). This is a stress test rather than a faithful reproduction: attribution graphs are unavailable for these CLTs, so features are found bottom-up from decoder vectors. C1 generalizes, in every cell and in all 247 of 444 prompt-by-inject pairs with a detectable effect, but the effective position is the final prompt token, adjacent to emission, and only two cells reach behaviorally meaningful probabilities. C2 and C3 are not recovered by any probe: a census of every active feature finds no rhyme-anticipating enrichment at the newline, and steering the newline while the model composes the whole line, over 36 runs and 8,640 sampled lines, shows why. That intervention is strong but one token long, making the injected word the first word of the composed line in 703 of 720 samples and leaving the rhyme six words later untouched. A final test drops the transcoder entirely: patching the newline’s whole residual, at every layer, from a minimal-pair poem whose third line ends on a different rhyme moves the rhyme in 11 of 1,260 composed lines against 4 at baseline, with a design resolving 1.4%. We read this as a boundary condition rather than a refutation: at this scale and with these transcoders, the causal site is emission-adjacent. We reproduce Figure 13’s shape, not its mechanism. Code and data are public (code: this http URL).
[NLP-55] Dependency-Aware Trajectory Refinement for Efficient Multi-Turn Agent Fine-Tuning AACL2026
【速读】： 该论文旨在解决多轮智能体轨迹（multi-turn agent trajectories）中普遍存在冗余轮次（如失败的工具调用、并行子查询、仅验证步骤等）导致训练与推理成本过高的问题。其核心解决方案是将每条轨迹建模为轮级依赖有向无环图（round-level dependency DAG），通过该图结构识别出对最终答案全局关键的必要轮次，进而对原始轨迹进行精细化修剪与重构。关键在于：基于大语言模型（LLM）标注的DAG，轨迹编辑过程具有确定性与可解释性，并支持可选的语义重述以提升流畅性；在该优化轨迹上微调智能体，可在显著降低推理阶段消息数量（最多减少约40%）和生成令牌数（最多减少约48%）的同时，实现更高的下游任务准确率——在四个多模态问答基准上，相比标准监督微调（vanilla SFT）最高提升1.7个百分点，相比LLM删除基线提升5.7个百分点，有效降低了计算与服务成本。
链接: https://arxiv.org/abs/2609.18417
作者: Zhuo Chen,Zhen Zhang,Xinyu Wang,Kewei Tu
机构: ShanghaiTech University (上海科技大学); Shanghai Engineering Research Center of Intelligent Vision and Imaging (上海智能视觉与成像工程研究中心)
类目: Computation and Language (cs.CL)
备注: AACL 2026 Findings
Abstract:Multi-turn agent trajectories often contain redundant rounds (failed tool calls, parallel sub-queries, verification-only steps) that inflate both training and inference cost. We propose viewing each trajectory as a \emphround-level dependency DAG that exposes which rounds are globally load-bearing for the final answer, and fine-tune agents on trajectories refined through this DAG. Given an LLM-annotated DAG, these edits are deterministic and interpretable, with optional rephrasing. Models trained on these refined trajectories consistently outperform those trained on the original trajectories at lower inference cost. Specifically, across four multi-modal QA benchmarks, our refinements improve downstream accuracy by up to 1.7 ,pp over vanilla SFT (and 5.7 ,pp over an LLM-deletion baseline) while reducing per-sample inference messages by up to approximately 40% and inference tokens by up to approximately 48% , translating to substantial savings in compute and serving cost. Code is available.
[NLP-56] Emotion Experience Expression and Perception: Emotion Analysis on Multimodal Social Media Posts EMNLP2026
【速读】： 该论文旨在解决社交媒体情感分析中两个被忽视的关键问题：一是现有研究多仅关注文本模态而忽略了图像模态在情感表达中的作用，二是缺乏对触发情感的现实事件及其与内容关联性的考量。其解决方案的核心在于构建一个名为Mult2EMo的多模态多情感标注数据集，通过收集作者对其创作动机的事件体验以及读者对情感表达的重建反馈，系统性地探究作者情感意图与帖子内容之间的关系。研究发现，尽管人类读者和计算模型均具备一定的情感重构能力，但该任务仍具挑战性；尤其当帖子依赖图像传递情感时，重构难度显著上升。关键结论表明，理解触发事件是实现准确情感重构的决定性因素。
链接: https://arxiv.org/abs/2609.18385
作者: Christopher Bagdon,Carina Silberer,Roman Klinger
机构: University of Bamberg (巴伐利亚大学); University of Stuttgart (斯图加特大学)
类目: Computation and Language (cs.CL)
备注: Accepted for publication at EMNLP 2026 main conference
Abstract:Emotions are an essential aspect of human communication, particularly on social media, where authors frequently combine text and images to convey their emotions. Yet prior work on emotion analysis of social media posts has overlooked two important aspects in regard to measuring how well readers can reconstruct the authors’ intent: (1)~the image modality, with most work focusing solely on text, and (2)~the real-world events that trigger the expressed emotions, and their relationship to the post content. We therefore study the relation between (a) the author’s experience of the event that caused them to write a social media post and (b) the content of the post, with a focus on readers’ capability to reconstruct that emotion expression. To do that, we introduce the Multimodal Multi-Emotion-Model dataset Mult2EMo, created by collecting annotations from both authors and readers on the posts and their triggering events. We find that reconstruction is possible but challenging for both human readers and computational models. We show that understanding the triggering event is crucial for accurate reconstruction, and that reconstruction is particularly challenging when posts rely heavily on the image to express emotion.
[NLP-57] Market Signal Injection: Adversarial Context Manipulation of LLM Pricing Agents EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在定价决策中对市场信息呈现方式的敏感性问题，即即使数值数据保持不变，仅通过调整数字格式、竞争者排序或定性市场评论等非显式指令手段，亦可诱导模型产生显著行为偏差。其核心解决方案在于提出“市场信号注入”（Market Signal Injection, MSI）攻击范式，揭示了模型行为受框架效应（framing effect）驱动的本质。实验表明，基于情感倾向的攻击引发最大行为偏移，并通过多智能体交互传导至其他企业，显著影响利润与消费者剩余；不同模型家族的脆弱性存在差异，且模型规模并非决定鲁棒性的可靠指标。通过对照实验与规则代理验证，支持了“框架依赖”解释机制。进一步分析显示，所有十一组模型-条件对均可通过线性分类器（AUC=1.00）或MLP分类器（AUC 0.93–0.99）有效区分基线与受攻击状态，表明攻击影响具有可分离性，但该分离不直接等同于识别有害决策。研究还发现，输入规范化（input canonicalization）可完全消除测试中的情感攻击，而决策边界锚定（decision boundary anchoring）——结合提示约束与输出投影的策略——可在部分自适应攻击下提供有限缓解。综上，该研究将数据呈现方式确立为LLM定价代理的新型攻击面，强调需构建考虑智能体间交互的防御机制。
链接: https://arxiv.org/abs/2609.18357
作者: Dohun Lee,Hyunwoo Park
机构: Seoul National University (首尔国立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 30 pages, Accepted to FinNLP 2026 Workshop @ EMNLP 2026
Abstract:Large language model (LLM) pricing agents may respond to how market data is presented, even when its numerical values remain unchanged. We introduce market signal injection (MSI), an attack that manipulates numerical formatting, competitor ordering, or qualitative market commentary without issuing explicit instructions. We evaluate nine open-weight models in simulated Bertrand duopoly and triopoly markets and three proprietary models in duopoly markets. Sentiment-based attacks produce the largest behavioral shifts, which propagate to other firms and alter profits and consumer surplus. Susceptibility varies across model families, and larger models are not consistently more robust. Matched neutral-text controls and a rule-based agent support a framing-based account of these shifts under the fixed demand parameters of our simulation. Episode-held-out probes distinguish baseline from attacked activations in all eleven re-evaluated model–condition pairs: linear AUC is 1.00 and MLP AUC ranges from 0.93 to 0.99. This separability does not by itself identify harmful pricing decisions. Input canonicalization removes the tested sentiment attacks, while decision boundary anchoring, which combines prompt constraints with output projection, provides partial mitigation under the tested adaptive attacks. These results identify data presentation as an attack surface for LLM pricing agents and motivate defenses that account for interactions among agents.
[NLP-58] Faithful yet Collusive: Why Chain-of-Thought Monitoring Cannot Detect Collusion in LLM Pricing Agents under Oligopolistic Competition EMNLP2026
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在自主定价代理场景中可能通过隐性协调维持超竞争性价格的问题。其核心挑战在于如何有效识别和防范由大语言模型（LLM）引发的算法共谋行为，尤其是在伯特兰（Bertrand）竞争模型下的市场结构中。论文提出了一种因果图分歧（causal graph divergence）框架，从结构忠实性（structural faithfulness）与意图忠实性（intent faithfulness）两个维度分别度量LLM定价代理的行为特征。研究发现，在双寡头与三寡头市场条件下，九个不同LLM表现出协同行为与思维链（Chain-of-Thought, CoT）忠实性在两个维度上存在解耦现象：最具有共谋倾向的模型虽能准确表达合作意图，但其决策结构缺乏结构性忠实性；而最具结构忠实性的模型则在两种市场结构下均维持了高于纳什均衡的价格水平。这一发现表明，仅依赖对思维链内容的监控无法作为防范算法共谋的独立保障机制，必须结合对模型内在推理结构的深度分析才能实现有效的监管。
链接: https://arxiv.org/abs/2609.18346
作者: Dohun Lee,Hyunwoo Park
机构: Seoul National University (首尔国立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 20 pages, Accepted to Findings of EMNLP 2026
Abstract:Large language models (LLM) deployed as autonomous pricing agents may sustain supracompetitive prices through tacit coordination. We develop a causal graph divergence framework that separately measures structural faithfulness and intent faithfulness of LLM pricing agents in Bertrand competition. Across nine LLMs under duopoly and triopoly conditions, collusive behavior and chain-of-thought (CoT) faithfulness dissociate along both dimensions: the most collusive model accurately reports cooperative intent yet reasons structurally unfaithfully, while the most structurally faithful model sustains supra-Nash pricing under both market structures. These findings establish that CoT monitoring alone cannot serve as a standalone safeguard against algorithmic collusion.
[NLP-59] Attention Dispersion as a Diagnostic Signal for Hallucination in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在复杂推理任务中频繁出现幻觉（hallucination）的问题，这一现象严重制约了模型的可靠性。传统检测方法依赖于输出层面的置信度指标（如logits），但现代对齐技术常导致这些指标校准失准，从而降低检测有效性。本文提出一种基于内部注意力机制时间波动性的新型诊断信号，作为不依赖输出校准的替代方案。其关键创新在于引入一种无监督的注意力分散度量（attention dispersion metric），发现认知不确定性会在中间层产生可测量的痕迹——注意力熵的突增与推理失效密切相关。在数学推理基准测试（GSM8K和MATH-500）上使用Qwen2.5系列模型（1.5B和3B参数规模）进行评估，结果表明，该方法在所有测试条件下相较于基于输出的基线模型，均实现了高达+0.076的受试者工作特征曲线下面积（AUC）提升，且具有统计显著性。研究证实，注意力分散度量是传统幻觉检测方法的重要补充，具有在更广泛模型家族和任务领域中进一步探索的价值。
链接: https://arxiv.org/abs/2609.18320
作者: Shardul P. More,Tanuja S. Pawar
机构: Rajarambapu Institute of Technology (拉贾拉姆巴普技术学院); Ishwarpur, Maharashtra, India (伊什瓦尔布尔, 马哈拉施特拉邦, 印度)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 6 pages, 2 figures, 1 table
Abstract:Large Language Models (LLMs) frequently exhibit hallucinations, presenting a major barrier to reliability in complex reasoning tasks. While traditional detection methods rely on output-based confidence metrics, these logits are often miscalibrated by modern alignment techniques. In this paper, we investigate the temporal volatility of internal attention mechanisms as an alternative diagnostic signal for hallucination that does not depend on output calibration. By introducing an unsupervised metric for attention dispersion, we show that epistemic uncertainty leaves a measurable trace within intermediate layers, where spikes in attention entropy are associated with reasoning breakdowns. We evaluate our approach on mathematical reasoning benchmarks (GSM8K and MATH-500) using the Qwen2.5 model family (1.5B and 3B parameters), finding statistically significant AUC improvements of up to +0.076 over output-based baselines across all tested conditions. These findings suggest that attention dispersion is a promising complement to traditional hallucination detection methods, requiring further investigation across broader model families and task domains.
[NLP-60] Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理长尾文化知识时的缺陷，尤其是针对拉丁美洲等代表性不足地区特有的文化事实，由于其在预训练语料中出现频率过低，导致模型难以可靠记忆。为应对这一问题，论文提出基于知识图谱（Knowledge Graphs, KGs）的Graph-RAG框架，相较于传统的检索增强生成（Retrieval-Augmented Generation, RAG），该方案通过结构化知识表示实现对上下文内容更精确的控制，同时提升生成结果的可解释性与知识更新能力。其核心解决方案在于采用端到端方式利用KGGen（一种开放域知识抽取工具）从维基百科文章自动构建知识图谱，并结合经过训练的投影模块实现零样本跨语言迁移，无需目标语言微调即可在葡萄牙语任务上表现良好，验证了其多语言泛化能力。实验表明，在LatamQA数据集上，Graph-RAG在标准知识图谱下将基础模型错误率降低72%，在任务感知优化版本下降低78%，且随着图谱内容向任务相关性倾斜，与RAG的性能差距进一步缩小。
链接: https://arxiv.org/abs/2609.18317
作者: Pablo Poulenard,Yannis Karmim,Valentin Barrière
机构: Universidad de Chile (智利大学); École Polytechnique (巴黎综合理工学院); Inria (法国国家信息与自动化研究所); Inria Chile (法国国家信息与自动化研究所智利分部); CENIA (智利环境与气候研究中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) suffer from a long-tail deficit: culturally specific facts, particularly those concerning underrepresented regions such as Latin America, appear too rarely in pretraining corpora to be reliably memorized. Retrieval-Augmented Generation (RAG) addresses this by grounding generation in external text, but structured alternatives such as Knowledge Graphs (KGs) offer tighter control over what enters the context, along with potential gains in explainability and updatability. We benchmark Graph-RAG against standard RAG on LatamQA, a culturally grounded multiple-choice dataset spanning eight thematic categories. The graphs are built end-to-end from Wikipedia articles with KGGen, a recent open-domain extractor, without manual curation in our main setting. G-Retriever is competitive with RAG and reduces the error of the base LLM by 72% with a standard KG and 78% with a benchmark-aware variant, the gap to RAG narrowing further as the graph is oriented toward task-relevant content. The trained projection transfers zero-shot to Portuguese without target-language fine-tuning, indicating multilingual reach.
[NLP-61] SEA-LION-v4.8: A Technical Report
【速读】： 该论文旨在解决生成式AI在东南亚语言（Southeast Asian Languages, SEAL）场景下的性能不足问题，特别是针对多语言理解、指令遵循与自然语言推理能力的短板。其核心挑战在于现有大模型普遍缺乏对东南亚地区多种低资源语言的有效支持，导致在本地化任务中表现不佳。解决方案的关键在于构建一个统一的多语言神经网络架构——Nemotron-SEA-LION-v4.8，基于NVIDIA Nemotron 3框架，通过持续预训练（continued pretraining）引入东南亚语言、推理、代码及多语言平行语料，并结合监督微调（supervised fine-tuning）与在线策略蒸馏（online on-policy distillation）进行后训练优化。该方法显著提升了模型在七种东南亚语言上的综合表现，尤其在指令遵循、自然语言推理和理解任务上取得显著进步，其中30B-A3B与120B-A12B模型在SEA-HELM基准上的得分分别从46.06/49.30提升至51.57/63.44，验证了统一建模与针对性后训练策略的有效性。
链接: https://arxiv.org/abs/2609.18310
作者: Ahmed Mohammad Dabeer,Ahn Jeongmi,Anocha Sutaveephamochanon,Antonyrex Sajeban,Aulia Adila,Chan Hok Teng,Adwin,Cheng Zi Yi,Nicholas Zhuang Ziyi,Choa Hsueh Mei Esther,David Ong Tat-Wee(David Wang Dawei),Evelyn Tan Chor Phin,Heng Cheng Peng,Jonathan,Lee Chwan Ren(Li Chunren),Leong Wai Yi,Leong Wei Qi,Leslie Teo Eng Sipp,Liew Rachel,Limkonchotiwat Peerat,Montalan Jann Railey Estrada,Muhammad Ridzuan Bin Mokhtar,Nagarajan Karthik,Ng Boon Cheong,Raymond(Huang Wenzong, Raymond),Ngui Jian Gang,Nguyen Thanh Ngan,Tasawong Panuthep,Pereira Mark Gregory,Phang Shi Wei Benjamin,Poon Yip Hung,Joseph,Rengarajan Hamsawardhini,Siow Wei Kang Bryan,Tai Ngee Chia,Tan Choon Meng,Tan Le Min,Sheryl,Tan Siao Wei(Chen Xiaowei),Tan Yi Xian,Tee Jun Yun,Teng Kok Wai,Tjhi William Chandra,Tuchinda Pume,Wu Donghang,Yong Xianbin,Yosephine,Zhang Zhou
机构: AI Singapore(人工智能新加坡)
类目: Computation and Language (cs.CL)
备注: A technical report
Abstract:We introduce Nemotron-SEA-LION-v4.8, a family of Southeast Asian Languages in One Network (SEA-LION) built upon NVIDIA Nemotron 3. The family includes 30B-A3B and 120B-A12B models, with both continued-pretrained base checkpoints and post-trained variants. We adapt the models using Southeast Asian, reasoning, code, and multilingual parallel datasets, followed by post-training with supervised fine-tuning and online on-policy distillation. On SEA-HELM, the 30B-A3B model improves the overall SEA score from 46.06 to 51.57, while the 120B-A12B model improves from 49.30 to 63.44. The strongest gains are observed in instruction following, natural language reasoning, and natural language understanding across seven Southeast Asian languages.
[NLP-62] Rollback the World Keep the Reflection: Rollback-Induced Reflection for Long-Horizon LLM Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在执行长时程任务时，因单次错误动作导致状态与观测值偏离，进而引发错误累积的问题。现有方法要么仅修正上下文而未恢复被改变的环境状态，要么通过回滚至早期状态来修复但舍弃了有用的过往经验，难以兼顾消除失败条件与避免重复错误。为此，论文提出将可靠恢复视为一种“回滚边界控制”问题，其核心在于协同决策何时干预、从何处恢复以及哪些信息应保留在恢复过程中。基于此视角，作者提出统一的恢复框架——回滚诱导反思（Rollback-Induced Reflection, RIR），该框架能够在将执行过程恢复至选定的先前状态的同时，将废弃轨迹中提炼出的可复用知识保留下来，以指导后续决策。进一步地，RIR通过统一的操作符对回滚深度与保留记忆进行建模，提供了状态恢复与知识保留的通用范式。在三个长时程基准测试上的实验表明，RIR在多种LLM骨干模型上均显著提升任务性能，其结构化的反思记忆有效保留了有用经验，而选择性回滚则实现了高效恢复。
链接: https://arxiv.org/abs/2609.18304
作者: Yi Yu,Liuyi Yao,Yaliang Li,Enshu Wang,Libing Wu
机构: Wuhan University (武汉大学); Alibaba Group (阿里巴巴集团)
类目: Computation and Language (cs.CL); Robotics (cs.RO)
备注:
Abstract:Large language model (LLM) agents increasingly tackle long-horizon tasks through multi-step environment interaction, yet a single erroneous action can alter subsequent states and observations, causing errors to compound over time. Existing methods either correct the context without repairing altered environment states or restore earlier states while discarding useful experience, making it difficult to both eliminate failure conditions and avoid repeating past mistakes. We argue that reliable recovery should instead be treated as a rollback-boundary control problem that jointly determines when to intervene, where to resume, and what information should survive recovery. Based on this view, we propose Rollback-Induced Reflection (RIR), a unified recovery framework that restores execution to a selected prior state while carrying forward reusable knowledge distilled from the abandoned trajectory to guide subsequent decisions. We further characterize recovery through a unified operator over rollback depth and retained memory, providing a general view of state restoration and knowledge retention. Experiments on three long-horizon benchmarks demonstrate that RIR consistently improves task performance across multiple LLM backbones, with structured reflection memory preserving useful experience and selective rollback enabling efficient recovery.
[NLP-63] Relationally Guided Use Case Modeling with LLM s
【速读】： 该论文旨在解决使用用例流（Use Case Flow）在软件工程中下游活动（如需求分析、架构与详细设计、测试用例生成）中的构建难题，尤其是传统人工构建成本高、依赖专家经验，而现有自动化方法在保持语义一致性、控制流逻辑、数据流逻辑及系统边界完整性方面仍存在显著不足，尤其在分支点识别与备选流程生成上表现不佳。其解决方案的关键在于提出FlowGen框架，通过基于大语言模型（LLM）的语义信息处理（Semantic Information Processing, SIP）模块提取关键语义元素，构建由增强型关系图注意力网络（R-GAT）编码的语义关系图（Semantic Relational Graph, SRG），进而实现基础流程生成（BFGen）、分支点预测（BPP）和条件化备选流程生成（AFGen）的端到端自动化。实验结果表明，FlowGen在13个公开数据集和7个工业数据集上均显著优于现有基线，在精确率、召回率、F1值和AUC等指标上均有大幅提升，验证了其在语义理解、逻辑一致性与上下文建模方面的有效性。
链接: https://arxiv.org/abs/2609.18291
作者: Guangyu Wang,Bangqi Li,Ji Wu,Zhijun Shao
机构: Beihang University (北京航空航天大学); Xi’an Aeronautics Computing Technique Research Institute (航空计算技术研究所); Engineering Research Center of Integration and Application of Digital Learning Technology (数字学习技术集成与应用工程研究中心)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: 19 pages, 8 figures, 6 tables
Abstract:Use case flows are important elements of use case modeling because they support downstream software engineering activities, including requirements analysis, architectural and detailed design, and test case generation. However, constructing them manually is costly and expertise-intensive, while existing automated approaches still struggle to preserve semantic consistency, control-flow logic, data-flow logic, and the intended system boundary, especially when identifying branch points and generating alternative flows. To address this problem, we propose FlowGen for complete use case flow construction. FlowGen uses LLM-based Semantic Information Processing (SIP) to extract semantic elements, constructs a Semantic Relational Graph (SRG) encoded by an enhanced R-GAT for basic flow generation (BFGen), and further supports branch point prediction through BPP and branch-conditioned alternative flow generation through AFGen. Evaluations on 13 public and 7 industrial datasets show that FlowGen consistently outperforms competitive baselines in all three core components. In particular, BFGen improves over the best baseline by 14% in Precision, 7-25% in Recall, 11-30% in F1, and 10-19% in AUC; BPP improves Precision by 30-110%, Recall by 33-91%, and F1 by 32-117%; AFGen improves Precision by 8-23%, F1 by 5-18%, and AUC by 0.6-2.5%. Moreover, we validate the effectiveness of the LLM-based SIP module and the attention preservation factor in BFGen, analyze the impact of requirement completeness on BFGen, and examine how different scopes of branch-related context affect AFGen.
[NLP-64] Made in Hungary: Comments on the performance of generative language models
【速读】： 该论文旨在解决匈牙利语领域缺乏高性能生成式语言模型的问题，尤其针对现有以英语为中心的模型在匈牙利语任务上表现有限的现状。其核心挑战在于：当前三项致力于开发匈牙利语生成式语言模型的研究存在显著的方法学缺陷。解决方案的关键在于揭示并纠正这些缺陷——包括评估协议可靠性不足、训练数据污染、语料库构建与数据混合策略不规范，以及缺乏对模型遗忘（forgetting）或能力退化（capability loss）的有效评估。研究指出，仅通过简单的微调或适配无法保证性能稳定，且未经严格控制的消融实验导致难以验证各设计选择的实际影响。因此，该论文强调，未来工作必须采用更严谨的实验设计，确保在高计算与资金投入背景下实现可复现、可信的模型开发。
链接: https://arxiv.org/abs/2609.18284
作者: Mátyás Osváth,Enikő Héja,Noémi Ligeti-Nagy
机构: Institute for Language Technologies and Applied Linguistics (语言技术与应用语言学研究所); ELTE Research Centre for Linguistics (ELTE 语言学研究中心)
类目: Computation and Language (cs.CL)
备注: 14 pages, 1 figure
Abstract:In recent years, three initiatives have emerged to develop generative language models in Hungary. The motivation behind them is the same. For Hungarian, no model with the given capability existed, or existing English-centric models offered limited proficiency. A detailed examination of the corresponding studies, however, reveals several methodological limitations. First, the reliability of the evaluation protocols is questionable. Contrary to the findings of Csibi et al. [2026], evaluation under the recommended inference settings shows that Qwen3-4B achieves higher scores than Racka-4B, its Hungarian-adapted version. Data contamination is evident in the work of Yang et al. [2025d] and Szentmihályi et al. [2025], potentially biasing the reported results. Second, the training pipelines fall short of current best practices in corpus curation and data mixture, which risks wasting substantial compute on low-quality data. The lack of controlled ablations prevents reliable assessment of these choices. Third, none of the three papers assessed forgetting or capability loss. Testing the adapted models on a subset of the original benchmarks indicates performance decline in all three cases, especially Racka-4B. These observations emphasize the importance of rigorous experimental design in language model development, given the significant computational and financial costs involved.
[NLP-65] oo Good to Be Real? Diagnosing and Reducing the Gap Between AI Preference and Real User Engagement
【速读】： 该论文旨在解决生成式 AI（Generative AI）在生成在线内容时所表现出的偏好与真实用户实际互动行为之间存在的不一致性问题。具体而言，尽管大语言模型（LLM）倾向于通过增强逻辑结构来提升内容的“高参与度”感知，但真实用户的行为更受情感共鸣与表达鲜明性的影响。其核心发现为“逻辑过绑定”（logic overbinding）现象：随着目标参与度提升，LLMs过度强化逻辑显性化，反而削弱了内容的情感吸引力和表达张力。针对此问题，论文提出一种名为语义本体掩码推理自编码（Ontology-Masked Reasoning Autoencoding, OMRA）的可控干预方法，通过掩码并重构过度解释的文本片段，在保持立场、事实内容与连贯性的前提下，降低过度逻辑化倾向。实验表明，OMRA在四个主流大模型家族中平均减少了54.4%的AI偏好与真实用户参与度之间的差距；在人工评估中，其生成内容在62.4%的成对偏好判断中胜出，即便真实平台答案更常被识别为人类撰写。
链接: https://arxiv.org/abs/2609.18282
作者: Xinglang Zhang,Yuanmeng Xiang,Yunyao Zhang,Zeliang Chen,Junqing Yu,Zikai Song
机构: Huazhong University of Science and Technology (华中科技大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are increasingly used to generate and evaluate online content, yet it remains unclear whether the qualities they associate with higher engagement match what real users respond to. We study this question using 1.17 million answers to 25,978 questions from Zhihu, Quora, and Reddit, comparing real platform answers and AI-generated answers across four within-question engagement levels. We introduce Ontological Preference Measurement, which represents answers along three dimensions: logic, affect, and expression. We find a systematic gap between AI preference and real user engagement: as target engagement increases, LLMs add more explicit logical structure, while real user engagement is more strongly associated with affective and expressive salience. We call this tendency logic overbinding. Based on this diagnosis, we propose Ontology-Masked Reasoning Autoencoding (OMRA), a controlled intervention that masks and reconstructs over-explained spans while preserving stance, factual content, and coherence. Across four LLM families, OMRA reduces the measured gap by an average of 54.4%. In human evaluation, OMRA wins 62.4% of pairwise preference judgments against matched real platform answers, even though the real answers are more often judged to be human-written.
[NLP-66] I code or AI code: A comparative evaluation of AI-rated scores in classroom observations
【速读】： 该论文旨在解决传统课堂观察在教育质量评估中资源消耗大、依赖专业观察员的瓶颈问题，探索基于大语言模型（LLM, GPT-5模型）的自动化评分系统在早期儿童教育课堂中对师生互动进行量化评估的可行性。其解决方案的关键在于利用文本转录数据，将大语言模型配置为遵循完整的课堂评估评分系统（CLASS），实现对教师—儿童互动的自动化评分。研究发现，AI评分与人工评分在“情感支持”维度，尤其是“反馈质量”这一子维度上表现出较高的一致性，表明AI能够有效捕捉教学中促进学习发展的关键互动特征；然而，在更具程序性或情境依赖性的“课堂组织”和“教学支持”维度上，两者间存在显著差异。这表明当前基于文本的AI评分虽可识别部分相对变化，但尚无法在全框架内稳定复现人类专家的校准判断。因此，研究建议将AI辅助观察定位为初步筛查工具，而非替代专业观察员，其核心价值在于为教师提供反思性证据，而非用于高风险评估。未来研究应聚焦于领域特异性训练及引入上下文与视觉信息以提升AI评分与人工评分的一致性。
链接: https://arxiv.org/abs/2609.18274
作者: Y. Fong,J. Xiang,T.Y.D. Chan,K. Lee,E.Y.H. Lau
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Classroom observations are widely recognized as a key tool for establishing benchmarks of education quality and guiding pedagogical improvement, yet they remain resource-intensive and dependent on trained observers. This study evaluated the feasibility of using a LLM (GPT-5 model) to score teacher-child interactions in early childhood classrooms, benchmarked against human raters. The study analyzed 87 video-recorded observations from 38 classrooms across 30 kindergartens in Hong Kong. Using observation transcripts, the AI model was configured to apply the full Classroom Assessment Scoring System (CLASS) framework. AI-rated scores were then compared with human ratings by examining correlations and differences in mean scores of the CLASS domains and dimensions. The results showed greater convergence between AI and raters for the Emotional Support domain and, in particular, the Quality of Feedback dimension, which captures how teachers use feedback to extend children’s learning. Greater divergence emerged for interactions that were more procedural or context-dependent, particularly within the Classroom Organization and Instructional Support domains. These findings suggest that transcript-based AI scoring may capture some of the relative variation in teacher-child interactions but cannot yet reproduce calibrated human judgements consistently across the full CLASS framework. AI-assisted observation may therefore be more appropriate as a preliminary screening tool rather than as a replacement for trained observers, providing teachers with evidence for reflection rather than high-stakes evaluation. Future research should examine whether domain-specific training and incorporation of contextual and visual information can improve alignment between AI and human rated scores.
[NLP-67] M2Tok: Multi-head Multi-codebook Discrete Action Tokenization for Vision-Language-Action Models ECCV2026
【速读】： 该论文旨在解决多模态视觉-语言-动作（Vision-Language-Action, VLA）模型中动作信号离散化过程存在的“离散化瓶颈”问题。由于原始动作信号为连续高维数据，现有离散动作分词器常因重建误差过高而无法有效保留精细动作动态，从而限制了下游控制任务的性能上限。为此，本文提出一种多头多码本动作分词器（Multi-head Multi-codebook Action Tokenizer, M2Tok），其核心创新在于：首先将潜在动作特征分解为多个独立头（head），使不同头可隐式对齐特定动作维度；其次为每个头分配独立的码本进行量化。通过多码本间的组合表达能力，显著提升了分词器的表征灵活性与表达能力，大幅降低重建误差。实验在RoboTwin、Simpler-Env及三个零样本真实世界任务上验证了该方法在重建保真度和策略成功率方面的优越性，且消融实验证明多头与多码本机制均具有关键贡献。
链接: https://arxiv.org/abs/2609.18259
作者: Chunpu Xu,Zhixuan Liang,Yuhao Zhang,Chi-Min Chan,Jessie Wang,Yang Xiao,Mengkang Hu,Xiaokang Yang,Yao Mu
机构: The Hong Kong Polytechnic University (香港理工大学); Shanghai AI Laboratory (上海人工智能实验室); The University of Hong Kong (香港大学); Shanghai Jiao Tong University (上海交通大学); Hong Kong University of Science and Technology (香港科技大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Recent advancements have successfully adapted autoregressive language models to process multimodal signals, such as images and actions. Since raw action signals are continuous, effective tokenization is essential to map high-dimensional inputs into compact discrete tokens for autoregressive processing. However, existing discrete action tokenizers often suffer from high reconstruction loss, failing to preserve the fine-grained dynamics required for precise control. This ``discretization bottleneck’’ significantly limits the performance ceiling of downstream Vision-Language-Action (VLA) models. To address this, we propose \mathcalM^2 Tok, a Multi-head Multi-codebook Action Tokenizer designed to minimize reconstruction error and enhance policy performance. Our approach introduces two key structural innovations: (1) we decompose the latent action features into multiple heads, enabling the model to implicitly align specific heads with distinct action dimensions; (2) we assign independent codebooks to each head for quantization. By leveraging the combinatorial nature of multiple codebooks, we significantly expand the representational expressivity of the tokenizer, leading to substantially lower reconstruction loss compared to previous methods. We evaluate the \mathcalM^2 Tok-based VLA on the RoboTwin, Simpler-Env, and 3 zero-shot real-world tasks. Experimental results demonstrate our method not only achieves superior reconstruction fidelity but also significantly boosts the success rate of VLA models. Comprehensive ablation studies further confirm the effectiveness of the multi-head and multi-codebook mechanisms. Code is available at \hrefthis https URLthis https URL.
[NLP-68] Behavior2Value: Benchmarking and Empowering LLM s for Consumer Value Measurement from E-commerce Behaviors
【速读】： 该论文旨在解决电商行为数据中隐含的消费者价值观难以被有效识别与测量的问题。由于消费者价值观具有稳定性，能够更深层次地解释用户在购买决策前对产品的评估逻辑，但其往往隐含于复杂且碎片化的用户行为轨迹中，导致现有方法在从行为数据中推断价值观方面仍处于探索阶段。为此，论文提出了“行为到价值”（Behavior-to-Value, B2V）任务，以实现从电商行为序列中准确识别消费者价值观。其核心解决方案包括：构建首个系统性的电商消费价值观分类体系——电商消费价值分类体系（E-commerce Consumption Value Taxonomy, ECVT），并基于淘宝匿名行为日志创建了首个B2V数据集与基准测试平台B2V-Bench，涵盖25类真实购买决策场景及其对应的价值取向标注；进一步提出基于价值验证调优（Value Verification Tuning）的B2V-Verifier模型，通过学习判断行为证据是否足以支持特定价值观的推理，显著提升了多标签分类性能，相较强基线大模型提升达34%。该研究为理解用户深层动机提供了可量化的技术路径，并推动了生成式AI在价值感知领域的应用发展。
链接: https://arxiv.org/abs/2609.18203
作者: Peixuan Hou,Bin Chen,Li He,Jian Xu,Bo Zheng,Xiuli Ma,Guojie Song
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Human values are deep motivational orientations that shape human behaviors. In e-commerce, they reveal the stable drivers behind users’ purchase decisions. Compared with short-term interests, consumer values better explain how users evaluate products before purchase. However, consumer values are often implicit in complex and fragmented behavioral trajectories, leaving value measurement from e-commerce behaviors largely underexplored. To this end, we propose the Behavior-to-Value (B2V) task, which aims to identify consumer values from e-commerce behavioral trajectories. Centered on this task, we first construct the E-commerce Consumption Value Taxonomy (ECVT) and introduce B2V-Bench, the first B2V dataset and benchmark, based on anonymized Taobao behavioral logs. B2V-Bench consists of real-world purchase decision episodes, covering 25 types of purchase behaviors, along with corresponding consumer value orientations manifested in each episode. To improve consumer value measurement accuracy, we further present B2V-Verifier, a behavior-to-value measurement model based on Value Verification Tuning, which learns to assess whether behaviors provide sufficient evidence for each value inference. Experiments show that B2V-Verifier outperforms strong LLM baselines, improving multi-label classification by 34%. The dataset and code will be publicly released upon acceptance.
[NLP-69] -SANDHI: Tone Sandhi-aware Adaptive Network with Decoupled Hybrid Injection for Low-resource Taiwanese Hokkien Speech Recognition
【速读】： 该论文旨在解决台湾闽南语自动语音识别（ASR）中因声调变调（tone sandhi）导致的音调映射混淆问题。尽管先前研究普遍认为声调变调是模型难以处理的主要挑战，但实验表明，语音基础模型实际上能够有效捕捉变调的声学特征，真正的性能瓶颈在于变调形式与保留的本调（citation tone）之间在局部产生的混淆。为此，论文提出T-SANDHI框架，在冻结的Whisper骨干网络基础上，通过显式解耦表面声学特征与底层词义意图，以缓解此混淆问题。其核心解决方案在于设计一种轻量级混合注入模块，采用由文本生成伪标签驱动的词典引导多任务学习结构，利用动态门控机制独立融合本调与变调的语音流，实现对声调语义的精确分离。在TAT-MOE语料库及两个盲测集上的大量评估显示，该方法在保持严格参数效率的前提下，显著提升了识别性能，优于现有基线模型。
链接: https://arxiv.org/abs/2609.18194
作者: Hung-Yang Sung,Chien-Chun Wang,Tien-Hong Lo,Yu-Sheng Tsao,Yung-Chang Hsu,Berlin Chen
机构: National Taiwan Normal University (国立台湾师范大学); E.SUN Financial Holding Co., Ltd. (元大金融控股公司); EZAI (EZAI)
类目: Computation and Language (cs.CL)
备注: Accepted to IEEE SLT 2026
Abstract:In Taiwanese Hokkien automatic speech recognition (ASR), prior studies often treat tone sandhi as a major challenge under the assumption that models fail to process implicit phonological variations. However, our experiments on Taiwanese Hokkien reveal that speech foundation models actually handle tone sandhi variations effectively, and the real performance bottleneck stems from a localized confusion between these variations and retained citation tones. To address this, we propose T-SANDHI to explicitly decouple surface acoustics from underlying lexical intent on top of a frozen Whisper backbone. Using a lexicon-guided multi-task learning structure driven by text-derived pseudo labels, our lightweight hybrid injection module integrates independent citation and sandhi phonetic streams via dynamic gating. Extensive evaluation on the TAT-MOE corpus and two blind test sets demonstrates that this explicit disentanglement effectively resolves tonal mapping confusion, outperforming baselines with strict parameter efficiency.
[NLP-70] ochewBench: A Human-Reviewed Benchmark for Teochew Hanzi Translation
【速读】： 该论文旨在解决潮州话（Teochew）在大规模语言模型评估中缺乏高质量文本资源的问题。尽管潮州话拥有庞大的使用者群体，并具有独特的词汇、句法与语用特征，但现有针对该语言的评测数据集严重不足，限制了对多语言生成模型在非主流语言上的表现进行系统性评估。为此，作者提出了TeochewBench，一个经过人工审校的基准测试数据集，包含300个潮州汉字表达，用于评估从潮州汉字到普通话和英语的翻译性能。其关键解决方案在于构建了一个涵盖五大类别的高保真度标注数据集：基础词汇、日常句子、潮州特有表达、语气、礼貌及语境相关表达，以及习语、歧义及文化特定表达，并由母语者主导审核与修订，确保语言准确性与文化适切性。此外，研究引入汉字原样复制控制组（Hanzi-copy control），以揭示共享汉字对中文翻译自动评分的影响。实验结果表明，Qwen3.5-27B在整体chrF-style得分上表现最佳（60.63），而高语义特异性表达项的平均得分显著下降至27.52，且各模型间差异缩小，说明此类表达构成模型性能的“共低分区”，凸显当前模型在处理文化专有与语境敏感内容时的普遍局限。
链接: https://arxiv.org/abs/2609.18156
作者: Jianan Wu
机构: 未知
类目: Computation and Language (cs.CL)
备注: 11 pages
Abstract:Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present TeochewBench, a human-reviewed benchmark comprising 300 Teochew Hanzi expressions for evaluating translation from Teochew Hanzi into Mandarin Chinese and English. The dataset covers five categories: basic vocabulary; everyday sentences; Teochew-specific expressions; tone, politeness, and context; and idiomatic, ambiguous, and culturally specific expressions. A primary Teochew-speaking reviewer examined all entries individually and revised them as needed, while two additional Teochew speakers verified selected items. Our main evaluation covers 11 official general-purpose post-trained models on the reviewed dataset in both translation directions, yielding 6,600 predictions. Two official base checkpoints provide 1,200 predictions for supplementary diagnostics, bringing the total to 13 models and 7,800 predictions. We additionally include a Hanzi-copy control, which returns the source input unchanged, to assess how shared Hanzi affect automatic scores for translation into Mandarin Chinese. Qwen3.5-27B achieved the highest overall chrF-style score among the evaluated checkpoints, at 60.63, followed by Qwen2.5-72B-Instruct at 56.61, Gemma-3-27B-IT at 56.36, and GLM-4-32B-0414 at 55.82. Across the 11 main-evaluation models, the mean chrF-style score decreased from 69.25 for low-specificity items to 27.52 for high-specificity items. High-specificity expressions received lower scores and exhibited smaller cross-model differences, suggesting that they constitute a shared low-scoring region across the model families evaluated here. The Hanzi-copy control further indicates that surface overlap in low-specificity items can substantially affect automatic scores for translation into Mandarin Chinese. Comments: 11 pages Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.18156 [cs.CL] (or arXiv:2609.18156v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.18156 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-71] DualSQL: Text-to-SQL with Multi-Agent Reinforcement Learning
【速读】： 该论文旨在解决现有文本到SQL（Text-to-SQL）系统中任务解耦导致的性能瓶颈问题，即传统多代理流水线分别独立训练模式在语义链接（schema linking）与SQL生成之间的协同效应未能被有效利用。其解决方案的关键在于提出DualSQL，一种基于单一模型主干（model backbone）驱动的双代理架构，两个代理共享相同的模型权重和代理框架，通过稳健的多代理强化学习（multi-agent reinforcement learning, MARL）框架实现两任务的联合优化。为支持多步推理，设计了三种数据库访问工具以增强与数据库的交互能力；同时引入滚动回放防护机制（rollout guardrail mechanisms），有效防止训练过程中的模型坍塌，提升训练稳定性。此外，提出新的SQL正确性评估指标——鲁棒执行匹配（robust execution match, REX），更精准地判断SQL语句的正确性并提供有效的奖励信号。实验表明，仅用3755个训练样本，DualSQL-4B在BIRD开发集上达到68.0%的执行准确率，媲美此前70亿参数模型；而DualSQL-8B进一步提升至71.1%，超越参数量达320亿的先前最优单模型方案，充分验证了联合多代理强化学习在构建高性能Text-to-SQL系统中的有效性。
链接: https://arxiv.org/abs/2609.18135
作者: Shijie Chen,Yu Gan,Yeounoh Chung,Jiani Zhang,Quannan Li,Sravan Babu Bodapati,Cody J. Greer,Yu Su,Fatma Ozcan
机构: Google LLC(谷歌公司); The Ohio State University(俄亥俄州立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注:
Abstract:State-of-the-art Text-to-SQL systems are typically multi-agent pipelines centered around two fundamental tasks: schema linking and SQL generation. However, existing work trains separate models for each task, failing to leverage the synergy between these interrelated tasks. In this work, we propose DualSQL, a new Text-to-SQL system consisting of two agents powered by a single model backbone. The agents share the same model weights and agentic scaffold, enabling joint optimization through a robust multi-agent reinforcement learning (RL) framework. We design three database access tools to facilitate effective multi-step reasoning grounded to interactions with the databases. To improve training and avoid model collapse, we introduce a set of rollout guardrail mechanisms that stabilizes multi-agent RL training, supporting DualSQL to keep improving during training. We also introduce a new SQL correctness metric, robust execution match (REX), to more accurately judge SQL correctness and assign reward signals. Being trained on only 3755 examples, DualSQL-4B achieves an impressive 68.0% execution accuracy on the BIRD development set, matching previous 7B models. DualSQL-8B further improves to 71.1%, outperforming previous state-of-the-art single-model solutions with 32B parameters. These results demonstrate the strength of joint multi-agent reinforcement learning for building high performance Text-to-SQL pipelines.
[NLP-72] Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing EMNLP
【速读】： 该论文旨在解决量化（Quantization）导致的混合专家模型（Mixture-of-Experts, MoE）性能显著下降的问题。由于MoE中各专家参数量少且对低比特表示敏感，量化过程中个别专家性能退化会严重影响整体模型表现。为此，论文提出Colla-Q，一种基于激活熵（activation entropy）的位宽分配框架，其核心在于通过动态分配不同专家的量化位宽，实现专家间性能的均衡。该方法使各专家在量化后仍能协同工作，从而在提升整体MoE模型性能的同时，降低对校准数据集的依赖性，并增强模型在不同校准数据下的泛化能力与稳定性。
链接: https://arxiv.org/abs/2609.18131
作者: Eunju Shin,Jongbin Ryu
机构: Ajou University (亚洲大学), South Korea (韩国)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted by the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2026
Abstract:In this paper, we present a Mixture-of-Experts (MoE) quantization method based on activation entropy. Although quantization reduces memory and computational costs, it can substantially degrade performance. In particular, performance decline is pronounced in quantized MoE models, where individual experts have a small number of parameters that are sensitive to low-bit representation. Considering that MoE operates as an ensemble model with collaborative contributions from routed experts, a significant performance decline of a particular expert due to quantization can harm model performance. Therefore, we propose Colla-Q, a bit-allocation framework to maintain balanced performance across experts through an activation-entropy-based bit-width allocation algorithm. This approach encourages each expert to operate collaboratively in the quantized model, thereby 1) improving the overall MoE performance and 2) reducing the dependence on the calibration dataset. Since uniformly adjusting each expert’s performance facilitates robustness and stability of the MoE model, the proposed MoE quantization method can generalize more consistently across different calibration datasets. Our code is available at: this https URL
[NLP-73] A Comprehensive Review of Generative Physical Artificial Intelligence
【速读】： 该论文旨在解决生成式物理人工智能（Generative Physical Artificial Intelligence, GPAI）系统在复杂真实场景中实现自主感知、推理与行动时所面临的架构设计、跨平台泛化能力不足及行为自然性欠缺等核心挑战。其解决方案的关键在于提出一个五维分类体系：机器人基础模型（Robot Foundation Models, RFMs）实现跨平台技能迁移；视觉-语言-动作模型（Vision-Language Action, VLA）支持端到端多模态感知与控制；大行为模型（Large Behavior Models, LBMs）生成类人运动轨迹；扩散策略模型（Diffusion Policy Models, DPMs）基于扩散机制实现时间上连贯的动作生成；世界基础模型（World Foundation Models, WFMs）则用于构建符合物理规律的仿真环境并生成训练数据。这五类方法相互协同，形成“数据生成—策略学习—行为优化—跨平台部署”的闭环，显著提升系统在自动驾驶、工业自动化、医疗机器人及类人机器人等场景中的性能，推动了面向物联网互联环境的智能体在传感器、执行器与边缘设备间高效协同的发展。
链接: https://arxiv.org/abs/2609.18111
作者: Satyam Gaba,Krutiksinh Rana,Siva Sai,Vinay Chamola,Dusit Niyato
机构: Qualcomm Research (高通研究); University of the Cumberlands (肯伯兰大学); BITS-Pilani, Goa Campus (比萨学院果阿校区); National University of Singapore (新加坡国立大学); BITS-Pilani, Pilani Campus (比萨学院皮拉尼校区); Nanyang Technological University (南洋理工大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 25 pages, 8 figures
Abstract:The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics known as Generative Physical Artificial Intelligence (GPAI). These agentic AI systems autonomously perceive, reason, and act in complex real-world situations. This survey comprehensively analyzes GPAI systems, focusing on their architectural foundations, current applications, and key limitations. We introduce a taxonomy of five distinct approaches: Robot Foundation Models (RFMs) for cross-platform skill transfer; Vision-Language Action (VLA) models for end-to-end multi-modal perception and control; Large Behavior Models (LBMs) for human-like movement generation; Diffusion Policy Models (DPMs) for diffusion model-based temporally coherent action generation; and World Foundation Models (WFMs) for physics-compliant simulation and data generation. We examine how these approaches complement each other: WFMs generate training data for VLAs and DPMs, RFMs enable cross-platform deployment of learned policies, while LBMs provide motion priors for natural behavior. Through examples across autonomous vehicles, industrial automation, healthcare robotics, and humanoid systems, we identify significant performance improvements and summarize promising research directions in data-efficient learning, sim-to-real transfer, edge-compatible architectures, and safety frameworks. These insights advance embodied AI for IoT-connected environments where intelligent agents interact with networked sensors, actuators, and edge devices.
[NLP-74] Linguistic Triggers of Gender and Racial Bias in Open-Weight LLM s Applied to Recruitment AAAI
【速读】： 该论文旨在解决开放权重大语言模型（open-weight large language models, LLMs）在招聘流程中潜在的系统性歧视问题，尤其是其在欧盟《人工智能法案》（EU AI Act）高风险分类（附录III）及美国平等就业机会委员会（EEOC）不利影响分析框架下所引发的合规风险。当前，尽管开源大模型正快速融入招聘环节，但其在岗位描述语言中的隐含偏见及其对不同性别与种族群体的差异化影响仍缺乏系统性评估。本研究提出首个针对多模型的系统性审计方案，以岗位发布语言为核心实验变量，对六种主流开源模型（Llama 3.2、Mistral、Gemma 3、Qwen 3、Phi 3、DeepSeek-R1）开展四项受控实验，涵盖招聘方模拟与求职者模拟任务。研究发现：（1）具有代理型（agentic）特征的岗位描述显著降低女性候选人的推荐得分（r_rb = 0.309, p_Bonf = 7×10⁻⁵；模型固定效应下 r_rb = 0.448），而共情型（communal）语言可部分抵消此惩罚效应；（2）编码排斥性语言在大规模效应下抑制非白人候选人的评分（r_rb = 0.646–0.758），且在求职者侧引发选择性回避行为，形成可量化的“寒蝉效应”机制。通过标签消融实验确认显式人口统计学身份标签为关键因果驱动因素，并借助词嵌入关联测试（Word Embedding Association Tests）在表征层面验证了上述偏差（基于Caliskan等人多词性别属性列表，d = 1.01–1.45）。最终，研究将实证发现转化为可操作的部署前审计协议，包括岗位词汇评分、基于身份条件的模型探测以及基于四分之五法则的不利影响预警，从而有效落实欧盟AI法案附录III对高风险招聘类AI系统的文档记录与风险管理义务。
链接: https://arxiv.org/abs/2609.18106
作者: Kosuke Kitahara,Nobuhiro Yamaguchi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Accepted at the 9th AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026). Extended version with Appendices A-B (prompt templates and full stimulus set)
Abstract:Open-weight large language models are rapidly entering hiring pipelines, yet their discriminatory failure modes – and the regulatory exposure these create under the EU AI Act high-risk classification (Annex III) and U.S. EEOC adverse-impact analysis – remain poorly understood. We present the first systematic, multi-model audit of open-weight LLMs that treats job-posting language as the primary experimental variable, evaluating six models (Llama 3.2, Mistral, Gemma 3, Qwen 3, Phi 3, DeepSeek-R1) across four controlled experiments that jointly probe recruiter-simulation and job-seeker-simulation tasks. We find that (1) agentic posting language depresses recruiter recommendation scores for female candidates (r_rb = 0.309, p_Bonf = 7x10^-5; model-fixed-effects r_rb = 0.448), while communal language partially reverses the penalty; and (2) coded-exclusion language suppresses non-White recruiter scores at large effect sizes (r_rb = 0.646-0.758) and, on the job-seeker side, selectively deters non-White personas from expressing interest – operationalizing a chilling-effect mechanism at scale. A label-ablation experiment isolates the explicit demographic persona label as the primary causal driver, and Word Embedding Association Tests corroborate these findings at the representational level (d = 1.01-1.45 under Caliskan et al.'s multi-word gender attribute lists). We translate these results into a concrete pre-deployment audit protocol – posting-vocabulary scoring, persona-conditioned LLM probing, and adverse-impact flagging against the four-fifths threshold – that operationalizes the documentation and risk-management obligations Annex III imposes on high-risk AI in recruitment.
[NLP-75] Agora: Git as Shared Memory for Collective AutoResearch
【速读】： 该论文旨在解决多智能体自主研究循环中因缺乏共享记忆而导致的重复搜索问题，即当多个编码智能体独立运行时，各自从零开始探索，容易造成大量冗余工作而非有效发现。其解决方案的关键在于构建一个名为Agora的共享记忆系统：将研究过程以追加式有向无环图（DAG）形式记录在Git中，每个研究结论、洞察、假设、验证和报告均作为不可变提交（commit）存在，且通过父节点关系明确其依赖关系；系统还引入一个衍生索引以可视化研究前沿、被忽视的研究分支及各主张的验证状态，并采用多样性感知的选择策略防止群体陷入单一主导路径的“同质化”困境。在实际应用中，13个无任务指派、无中央协调的语言模型工作者在12天内协作完成了一项权重迁移任务，面对141个预训练捐赠模型与一个参数量为119.6M但结构不匹配的冻结注意力-状态空间混合模型，仅通过静态初始化即实现目标网络性能从3.39比特/字节提升至1.899比特/字节，缩小了与训练过的GPT-2 124M模型之间62%的差距。最终胜出方案通过压缩捐赠模型的下一个词统计信息至目标模型的嵌入层与输出头，并结合对注意力、前馈与状态空间模块的稀疏编辑引入短程上下文信号。整个过程共产生1703项贡献，拥有145次提交的完整溯源链，且165次独立复现全部成功。研究揭示了单次中段人工干预如何打破研究社区的同质化困局，并指出当前轨迹所能证明与无法证明的内容，同时提出需通过受控对比实验来验证共享研究状态是否能真正提升单位算力下的发现效率。
链接: https://arxiv.org/abs/2609.18094
作者: Yifan Zhang,Yunheng Zou,Shaokun Zhang,Jian Hu,Hao Zhang,Binfeng Xu,Jan Kautz,Yi Dong
机构: NVIDIA(英伟达)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Autonomous research loops such as AutoResearch show that one coding agent can improve a training setup unattended. Run several of them and each session starts from scratch, so more agents tend to mean more duplicated search rather than more discovery. Agora is a shared memory for such agents: research is recorded as an append-only directed acyclic graph (DAG) stored in Git, so that every claim is a commit anyone can check out and rerun. Each result, insight, hypothesis, verification, and report is an immutable commit whose parent edges say what it builds on; a derived index exposes the frontier, the neglected branches, and the verification status of each claim, and a diversity-aware selection rule keeps the community from collapsing onto one leader. We describe the system and report its first sustained use: a run of nearly 12 days in which 13 language-model workers, with no assigned tasks and no central planner, worked on a weight-transfer problem. Given 141 pretrained donor models and a frozen 119.6M-parameter attention-SSM hybrid whose dimensions match no donor, the workers had to initialize the target without training data or gradient updates. They published 1,703 contributions and drove the evaluator from 3.39 to 1.899 bits per byte, closing 62% of the gap to a trained GPT-2 124M. The winning recipe compresses donor next-token statistics into the target’s embedding and output head, then adds a short-range context signal through sparse edits to attention, feed-forward, and state-space blocks. Its 145-commit ancestry spans 15 accounts, and 165 independent reproductions were posted, none of which failed. We describe the single mid-run human intervention that pulled the community out of a monoculture, what the trace does and does not establish, and the controlled comparison that would settle whether shared research state improves discovery per unit of compute.
[NLP-76] From a River in Gilead to the Inference Distributions of Large Language Models : Covert Dialect Bias and Linguistic Profiling at Scale AAAI
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在高风险场景（如住房筛选）中隐性方言偏见（covert dialect bias）的传播问题，尤其关注那些未被现有对齐技术有效缓解的、存在于模型内部概率分布中的隐蔽态度关联。其解决方案的关键在于采用匹配身份（matched-guise）社会语言学范式，系统评估四种英语变体——标准美式英语（Standard American English, SAE）、非裔美国人方言英语（African American Vernacular English, AAVE）、尼日利亚标准英语（Nigerian Standard English, NSE）和尼日利亚皮钦语（Nigerian Pidgin, NP）——在住房相关社会判断任务中的表现差异。研究通过260组语义匹配的句子四元组与基于住房相关形容词的对数概率评分，在租户筛选、邻里接纳和室友选择三种社会接近度不同的情境下，测试十种开源大语言模型。结果表明，AAVE与NP普遍被赋予更负面的形容词关联，且惩罚机制由不同刻板印象集群驱动，而非单一“非标准”标签；而具有制度声望的NSE则呈现情境依赖性变化：在正式租户筛选中优于SAE，但在社会接近度增加时逐渐受到贬低。这一发现揭示了大语言模型不仅继承了以种族身份为维度的隐性偏见，还延续了以语言地位为维度的后殖民英语变体间的歧视模式，反映出人类住房歧视现象在生成式人工智能中的跨文化迁移。
链接: https://arxiv.org/abs/2609.18068
作者: Chowdhury Mohammad Abdullah,Rita Orji
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures. Accepted to the 9th AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026)
Abstract:Large language models (LLMs) are increasingly deployed in high-stakes domains such as housing screening. While alignment techniques mitigate explicit racial bias in generated text, they often leave covert attitudinal associations in internal probability distributions untouched. Adapting the matched-guise sociolinguistic paradigm, we examine covert dialect bias in housing-related social judgments across four varieties: Standard American English (SAE), African American Vernacular English (AAVE), Nigerian Standard English (NSE), and Nigerian Pidgin (NP). AAVE reflects the racialized dialect studied in prior covert-bias evaluations, whereas NSE and NP represent Black African, postcolonial varieties absent from this literature. Using 260 meaning-matched sentence quadruples and log-probability scoring over housing-relevant adjectives, we probe ten open-weight LLMs across three contexts varying in social proximity: tenant screening, neighbor acceptance, and roommate selection. Across all ten models, AAVE and NP are consistently associated with more negative adjectives than SAE, with NP penalized most severely. Crucially, each dialect is penalized via distinct stereotype clusters rather than a generic non-standard category. NSE, which carries institutional prestige, displays a context-dependent shift: favored over SAE in formal tenant screening but increasingly penalized as social proximity grows. Our findings reveal that LLMs inherit covert dialect bias along both racial identity and prestige dimensions, echoing documented human housing discrimination and demonstrating its reach across postcolonial English varieties.
[NLP-77] Exact semantic readout from compressed vector representations
【速读】： 该论文旨在解决预训练词向量表示中是否存在精确线性或仿射读出（readout）有限词汇表真值条件的问题，即能否通过一个固定的映射（每个谓词对应一个映射），将实体向量准确映射到对应的真值向量。其核心解决方案的关键在于提出一个必要且充分的行空间条件：当真值矩阵的增广矩阵秩为 $ r $ 时，线性情形下的最小维度为 $ r $，而仿射情形下为 $ r-1 $。精确读出要求输出在共享的真值基底下进行，布尔连接词在此基底上保持不变；而仅具备可分性（separability）则需依赖阈值机制。对于二元关系，实现身份或严格全序的精确双线性读出要求实体向量线性无关。实验基于GloVe和word2vec模型表明，大多数谓词具有严格可分性，但无一能从预训练嵌入中实现精确仿射读出。通过监督式归纳训练（transductive training），可在满足理论边界的所有测试维度上达到数值精度下的精确仿射恢复。在原始嵌入维度下，约束为精确线性恢复的几何结构保留了98–99%的预训练特征范数方差及80–83%的WordNet词典方差，表明该方法在保持语义信息方面具有高度有效性。
链接: https://arxiv.org/abs/2609.18047
作者: Daniel Quigley
机构: Center for Possible Minds; Indiana University Bloomington (印第安纳大学布卢明顿分校)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
备注: 24 pages; 31 references; 13 figures; 2 tables
Abstract:We characterize when compressed vector representations admit exact linear or affine readouts of a finite lexicon’s truth conditions: one fixed map per predicate, sending each entity vector to the corresponding truth vector. A necessary and sufficient row-space condition determines existence; the augmented truth matrix has rank r, giving minimum dimension r in the linear case, and r-1 in the affine. Exact readouts return values in a shared truth basis on which Boolean connectives act unchanged; separability alone requires an intervening threshold. For binary relations, exact bilinear readout of identity or strict total order requires linearly independent entity vectors. Experiments with GloVe and word2vec distinguish exact affine recovery, linear separability, and held-out prediction: most predicates are strictly separable, but none admits an exact affine readout from the pretrained embeddings. Supervised transductive training attains exact affine recovery to numerical precision at every tested dimension meeting the bound. At the embeddings’ original dimension, geometries constrained to exact linear recovery retain 98-99 percent of the pretrained variance on the feature norms, and 80-83 percent on the WordNet lexicon.
[NLP-78] A Calibrated Instrument for Measuring How Inference Optimizations Affect Output Quality
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）优化技术中输出质量评估缺乏统一、严谨标准的问题。现有方法如量化（quantization）、提前退出（early-exit）和推测解码（speculative decoding）各自采用非标准化的评测指标，难以实现跨系统、跨技术的公平比较。其解决方案的关键在于提出一种经过严格校准的输出质量测量方法：使用一个大型语言模型作为评判者（LLM-as-judge），并通过形式化校准确保其评分一致性——即在相同提示下对同一模型两次运行的结果进行评分，验证评判者无系统性偏好，并量化其单样本噪声水平；同时引入“零条件”（null condition）作为对照，该条件在分布上与原始模型完全一致，其测量差异必须为零，从而保证评估框架的可靠性。利用这一统一工具，研究可在相同输入下对多种加速技术进行质量成本对比，揭示出感知质量高度依赖于语域（domain of discourse）。例如，4比特量化在中英文散文中仅造成±0.3点质量损失，而3比特量化则分别导致英语散文损失0.5分、中文损失0.9分、多步数学推理损失1.1分；早期退出在散文中仅损失0.7分，但在数学任务中却使正确解题数从27道降至6道（损失2.5分）。不同厂商模型（如阿里与Meta）对相同量化器的敏感度存在显著差异，表明质量代价具有模型特异性。此外，模型对当前词元的置信度虽能预测其是否偏离完整模型输出，但无法预示该偏差对最终质量评价的影响，因此基于置信度的接受规则无法有效区分关键错误与无关误差。
链接: https://arxiv.org/abs/2609.18005
作者: Jerry Kaplan
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 23 Pages. 6 tables in main text,5 tables in appendices. Code, prompts, and result files at this https URL
Abstract:Large language model optimization is an active research area, spanning quantization of model weights, early-exit methods for skipping layers, and speculative decoding. Each track uses its own quality measures, typically an idiosyncratic benchmark score. Few approach the measurement precision required by other scientific disciplines. We propose a rigorous methodology for measuring output quality, suitable for cross-system and cross-technique comparison. We score outputs with an LLM as a judge, but calibrate the judge formally: we compare its scores on two ordinary runs of a model given the same prompts, verifying that it shows no systematic preference between statistically equivalent outputs and measuring its per-sample noise. Each design also includes a ‘null’ condition, provably identical in distribution to the unmodified model, whose measured difference must be zero. With this one instrument we measure several acceleration techniques on the same prompts, so their quality costs can be compared. Perceived quality proves highly dependent on the domain of discourse. A 4-bit model was indistinguishable from its 16-bit original down to our design’s +/-0.3-point resolution, in English prose and Chinese alike. At 3-bit precision the same prompts lost 0.5 points in English prose, 0.9 in Chinese, and 1.1 on multi-step math; early exit that cost 0.7 points on prose cost 2.5 on math, cutting correctly solved problems from 19 of 27 to 6. The pattern held for models from Alibaba and from Meta, but not its magnitude: the same quantizer cost Meta’s model 1.8 points where it cost Alibaba’s 0.7. A model’s certainty about a token predicts how likely it is to differ from the full model’s choice, but not how much that difference affects judged quality, so acceptance rules relying on certainty cannot distinguish errors that matter from errors that don’t. Comments: 23 Pages. 6 tables in main text,5 tables in appendices. Code, prompts, and result files at this https URL Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) ACMclasses: I.2.7; I.2.6 Cite as: arXiv:2609.18005 [cs.CL] (or arXiv:2609.18005v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.18005 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jerry Kaplan [view email] [v1] Wed, 16 Sep 2026 01:49:12 UTC (788 KB)
[NLP-79] Modeling the Developmental Shift in Telicity Acquisition
【速读】： 该论文旨在解决第一语言（L1）习得者如何从表面语言形式与语义线索中推断事件的可终结性（telicity，即事件是否具有明确边界，如“吃了一个苹果”为有界，“吃了苹果”为无界）的问题。其核心挑战在于理解这一映射过程背后的计算轨迹。解决方案的关键在于提出一种基于生成式模型的“差异突变率”（Difference in Surprisal）方法，利用GPT-2对成对时间状语诊断句（如“in an hour” vs. “for an hour”）的词元突变率（token surprisal）自动标注英语儿童语言数据集（CHILDES）中的可终结性标签，并通过专家语言学家判断进行验证。在此基础上，研究构建了两个逻辑回归分类器，分别分析儿童言语（child speech）与成人导向言语（child-directed speech）中12种句法和词汇语义特征对可终结性的编码方式。结果发现两者模型存在显著差异：儿童模型通过单一确定性句法线索——动词后置限定词（post-verbal determiner）——即可达到近乎完美的分类准确率；而成人模型则更依赖动词类别及其他词汇语义特征，且限定词线索被中和。这一发现支持句法搭桥假说（Syntactic Bootstrapping）：学习者最初依赖高频句法结构作为认知支架来初步建立事件结构表征，随后逐步发展出以动词为核心的、更为组合式的事件语义表征。
链接: https://arxiv.org/abs/2609.17996
作者: Ellie Xia,Parisa Kordjamshidi,Alan Hezao Ke
机构: 未知
类目: Computation and Language (cs.CL)
备注: 12 pages
Abstract:Acquiring telicity, which is the distinction between bounded (e.g., ate an apple) and unbounded (e.g., ate apples) events, requires first language (L1) learners to map surface-level and semantic cues to abstract event structures, but the computational trajectory of this mapping is not well understood. We introduce a Difference in Surprisal method that uses GPT2 token surprisal over paired temporal adverbial diagnostics (in an hour versus for an hour) to automatically label telicity across English CHILDES corpora, validated against expert linguist judgments. Using these labels, we train diagnostic logistic regression classifiers on 12 syntactic and lexical semantic features to compare how child speech and child-directed speech encode telicity. The two models diverge: the child model reaches near perfect accuracy through a single deterministic cue, the presence of a post-verbal determiner, while the adult model relies more heavily on verb class and other lexical semantic features, with the determiner cue neutralized. This trajectory supports Syntactic Bootstrapping: learners first exploit high-frequency structural cues as a scaffold to bootstrap, before developing fully compositional, verb-based event structures.
[NLP-80] ACTICS: Taxonomy-Aware Intelligent Corpus Sampling for Machine Translation
【速读】： 该论文旨在解决大规模机器翻译（Machine Translation, MT）系统评估中因随机采样导致的覆盖率不足问题。传统评估方法依赖于语料库的随机样本，而这些样本的分布特征受限于语料构建过程，无法全面覆盖系统实际需处理的多样化语言现象，包括规则性惯例（如术语、标点、货币格式）和上下文依赖现象（如语气、敬语、文档级连贯性），因而缺乏对系统鲁棒性的有效评估保障。为此，论文提出TACTICS（Taxonomy-Aware Coverage-opTimized Intelligent Corpus Sampling），其核心创新在于将“覆盖率”显式建模为优化目标。TACTICS通过从本地化风格指南中构建层次化分类体系，对文本片段进行分类，并在固定预算下联合优化稀有类别覆盖率、文档级连贯性以及与全语料分布的一致性，从而实现更均衡、更具代表性的样本选择。实验表明，相较于基于词法或嵌入的采样方法，TACTICS显著提升了对罕见语言现象的覆盖能力；更重要的是，其通过聚焦区分不同系统的关键语言现象，能够在更少的评估样本下准确恢复真实系统排名，且在无实质差异时不会产生误判，有效提升了评估效率与可靠性。
链接: https://arxiv.org/abs/2609.17956
作者: Prasanth Bathala,Anubhav Shrimal,Sukhdeep Singh Kharbhanda,Pradyumna Lanka,Rohit Dhaipule
机构: Amazon(亚马逊)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at The Eleventh Conference in Machine Translation 2026 (WMT2026)
Abstract:Large-scale machine-translation (MT) systems are typically evaluated on random samples from a corpus whose distributional composition is an artifact of how it was assembled. Such a sample inherits the phenomena the collection happens to contain rather than the full space a system must handle, spanning rule-governed conventions (terminology, punctuation, currency formatting) and context-dependent phenomena (tone, honorifics, document-level coherence), and thus provides no coverage guarantee for assessing robustness. We propose TACTICS (Taxonomy-Aware Coverage-opTimized Intelligent Corpus Sampling), which recasts coverage as an explicit objective. TACTICS induces a hierarchical taxonomy from a locale style guide, classifies segments against it, and selects a fixed-budget subset jointly optimizing coverage of rare categories, document-level coherence, and distributional fidelity to the full corpus. Applied to MT evaluation across four translation directions, TACTICS improves coverage of rare categories over lexical and embedding-based selection. By targeting the phenomena that separate systems, TACTICS makes a fixed evaluation budget go further, recovering the true system ranking from far fewer segments than random sampling wherever a real quality gap exists and never signaling a difference where none exists.
[NLP-81] ASPIRE: Asynchronous Batched Self-Speculative Decoding for Long-Context LLM Inference
【速读】： 该论文旨在解决长上下文大语言模型（Long-context LLM）推理中因注意力机制导致的性能瓶颈问题，尤其是解码阶段由重复的键值缓存（KV-cache）读取引发的内存带宽限制。现有自推测解码（self-speculative decoding）方法虽通过稀疏注意力生成候选词并用全注意力验证来缓解此问题，但其批处理策略仍采用同步调度机制，即同一批次内所有请求共享单一的生成-验证时序，无法适应不同请求间及单个请求内部动态变化的最优生成长度需求。为突破这一局限，论文提出ASPIRE框架，其核心创新在于三个关键组件：首先，统一混合前向传播（unified mixed forward）使生成与验证请求可在同一前向计算中并行执行，消除了全局生成-验证阶段的强制同步；其次，轻量级在线推测调度器（lightweight online speculation scheduler）基于每请求的接受率估计和批处理感知的成本模型，实现各请求独立决策验证时机；最后，内部生成刷新层（intra-draft refresh layer）在生成阶段指定某一层执行全注意力操作，以在每步生成时更新稀疏上下文，降低生成过程中的上下文过时（staleness）问题。实验表明，ASPIRE在三个模型及五个长文本推理与复杂推理基准上，相较自回归基线实现1.70–4.58倍的解码吞吐量提升，并较最强的先前自推测基线平均提速27%。
链接: https://arxiv.org/abs/2609.17943
作者: Amir Ziashahabi,Hossein Entezari Zarch,Lei Gao,Murali Annavaram,Salman Avestimehr
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: Accepted to COLM 2026
Abstract:Long-context LLM inference is bottlenecked by attention, whose repeated KV-cache reads make decoding memory-bound. Self-speculative decoding alleviates this by drafting tokens with sparse attention and verifying them with full attention, but existing batched methods remain synchronized: all requests in a batch share a single draft-verify schedule, even though the optimal draft length varies widely across requests and changes dynamically within each request. We propose ASPIRE, a non-synchronized batched self-speculative decoding framework built on three components. First, a unified mixed forward allows drafting and verifying requests to coexist in the same batched forward pass, removing the need for global draft-verify phases. Second, a lightweight online speculation scheduler uses per-request acceptance-rate estimates and a batch-aware cost model to let each request independently choose when to verify. Third, an intra-draft refresh layer performs full attention at a single designated layer during drafting, updating the sparse context at every draft step to reduce staleness during drafting. Across three models and five reasoning and long-context benchmarks, ASPIRE achieves 1.70 - 4.58\times speedup in decoding throughput over autoregressive baselines and improves average speedup by approximately 27% over the strongest prior self-speculative baselines.
[NLP-82] Long-Context Demonstration Selection Using State Space Models
【速读】： 该论文旨在解决长上下文场景下提示示例选择（demonstration selection）的效率瓶颈问题。在基于大语言模型的上下文学习中，为查询前缀添加相关示例可显著提升性能，但随着序列长度增加，Transformer模型的推理时间呈二次方增长，导致计算开销急剧上升。针对此问题，本文提出一种基于状态空间模型（State Space Models, SSMs）的解决方案，其核心在于利用SSMs线性推理时间的特性，替代传统Transformer进行高效演示选择。关键创新包括：首先通过蒸馏技术从预训练的Transformer模型中学习一组小型SSM，将模型层分组并为每组构建独立的SSM以近似相邻层间的输入输出行为；其次，将蒸馏后的SSM输出映射为紧凑的嵌入表示，并用于下游任务中的示例选择。实验结果表明，该方法在合成与真实数据集上均表现出优异性能，相比基线方法在文本分类与推理任务中实现14.2倍的浮点运算量（FLOPs）降低和6.48%的准确率提升，同时保持小于0.7%的相对近似误差，验证了其高效性与有效性。
链接: https://arxiv.org/abs/2609.17888
作者: Ziniu Zhang,Zhenshuo Zhang,Ruoxuan Xiong,Gene Cooperman,Hongyang R. Zhang
机构: Northeastern University (东北大学); Emory University (埃默里大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 16 pages
Abstract:We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This problem is closely related to in-context learning and language model inference. Since the inference cost of a transformer model scales quadratically with sequence length, the selection problem becomes especially challenging in a long-context scenario. In this paper, we tackle this problem by building on state space models (SSMs), which require only linear inference time given the input. Our approach involves two algorithms. The first learns a small set of SSMs through distillation of a (trained) transformer model. We partition all the layers into consecutive groups. Then for each group, we estimate a separate state space model to replicate the input-output behavior within the adjacent layers. Second, we map the distilled model outputs to a small set of tokens, and apply these embeddings for demonstration selection in downstream applications. We perform extensive experiments in both synthetic and real-world datasets to validate our approach. We demonstrate that the distilled SSMs only incur an approximation error of less than 0.7% relative to the true output. In downstream evaluation, we show that on several text classification and reasoning tasks, our approach reduces FLOPs by 14.2\times and improves accuracy by 6.48% relative to baseline demonstration selection methods.
[NLP-83] Uncertainty-Aware Continual Learning for Open-World Intent Discovery Under an evolving Label Space
【速读】： 该论文旨在解决开放世界环境下智能系统在持续交互中面临的新意图发现问题，即用户意图并非预先固定且可能随时间演化，导致标签空间动态变化。其核心挑战在于如何在不破坏已有知识的前提下，有效识别并接纳潜在的新意图，同时避免误将噪声或异常样本视为新类别。解决方案的关键在于提出一种统一的不确定性感知概率框架，通过自适应β-变分自编码器（β-VAE）将每个输入语句编码为隐变量均值，实现分类、密度建模与后验不确定性估计的联合优化，其中后验不确定性作为全局可靠性信号。通过多信号决策机制融合分类置信度、后验不确定性与狄利克雷过程高斯混合模型（DP-GMM）似然，以区分已知意图与潜在新颖样本；新颖候选样本经基于密度的发现模块聚类，仅对高可靠性簇进行标签提升，从而实现受控的标签空间扩展。结合回放机制与弹性权重固化（Elastic Weight Consolidation），有效缓解灾难性遗忘，保障知识稳定性。该框架将持续意图发现形式化为结构化的多阶段开放世界问题，利用后验不确定性调控可信样本选择、伪标签生成、新颖性接纳及回放策略，在实验中展现出高新颖性精度、稳定的渐进适应能力及有限遗忘。尽管归一化互信息（NMI）和调整兰德指数（ARI）接近零，表明未完全重构细粒度意图分类体系，但定性分析显示所发现的聚类具有高度语义密集性与局部一致性，证明可在不追求完整分类体系恢复的前提下，可靠地发现新的语义结构。
链接: https://arxiv.org/abs/2609.17866
作者: Pisante Aida,Formentin Simone
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Real-world intelligent systems increasingly operate under open-world conditions, where user intents are not fixed or exhaustively known a priori and may evolve as new interaction patterns emerge. This paper proposes a unified uncertainty-aware probabilistic framework for continual new intent discovery under an evolving label space. Each utterance is encoded through an adaptive \beta -VAE into a latent mean, used for classification and density modelling and a posterior uncertainty estimate acting as a global reliability signal. Classifier confidence, posterior uncertainty and DP-GMM likelihood are combined through a multi-signal decision mechanism to distinguish known intents from potentially novel samples. Candidate novel instances are clustered through a density-based discovery module and only reliable clusters are promoted to new labels, enabling controlled label-space expansion. Replay and Elastic Weight Consolidation mitigate catastrophic forgetting and preserve previously acquired knowledge. The paper formalises continual intent discovery as a structured multi-phase open-world problem, introduces adaptive label-space expansion under stability–plasticity constraints and uses posterior uncertainty to regulate trusted-sample selection, pseudo-labelling, novelty admission and replay. Experiments show high novelty precision, stable adaptation across sequential phases and limited forgetting. Near-zero NMI and ARI indicate limited reconstruction of the complete fine-grained intent taxonomy, consistent with the framework’s conservative promotion strategy. Qualitative analyses nevertheless reveal dense and locally coherent semantic clusters, showing that reliable novel structures can be discovered without exhaustive recovery of the underlying taxonomy.
[NLP-84] Who Judges Matters: Measuring Family-Conditioned Preference in LLM -as-Judge Panels
【速读】： 该论文旨在解决大语言模型作为评判者（LLM-as-judge）时，评判者身份对评估结果的影响问题，尤其关注这种影响如何与候选内容质量混淆，从而干扰对真实性能的准确评估。其核心挑战在于区分评判者效应（judge effect）与候选内容质量之间的混杂关系。解决方案的关键在于设计了一个完全交叉的成对比较实验（fully crossed pairwise design），覆盖4个开源模型家族（Llama 3.1、Qwen 2.5、Gemma 2 和 Yi 1.5），共计9,312次判断，并提出一种校正估计量——在固定候选内容家族的前提下，仅比较不同评判者之间的表现差异。该方法有效剥离了候选质量的混杂影响，发现所有四个模型家族均表现出显著的“同族优势”（same-family lift），提升幅度为3.4至8.4个百分点，全局评分偏差（FPS）达0.067（95%置信区间[0.053, 0.084]，置换检验p=0.0002），且该效应在基于人工共识的锚定验证、面板质量控制及float16精度复现中依然稳健。研究进一步揭示，评判者的似然得分（likelihood）与其评判偏倚密切相关：引入似然优势后，校正后的系数下降61%，被解释为描述性衰减而非因果中介。此外，位置效应（position effect）构成另一独立偏差来源，超过55%的AB/BA成对判断出现反转，表明现有模型评估难以符合独立内容-噪声模型假设，且面板构成变化可导致18.5%的成对结果发生改变。研究已准备完整的可复现数据存档，供公开发布。
链接: https://arxiv.org/abs/2609.17857
作者: David Ababio Awuni,Luke E. K. Achenie,Benjamin Tei Partey,Elvis Gyasi Owusu,Nii-Nai Derrick Sowah
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 11 pages, 3 figures, 4 tables
Abstract:Who the judge is can affect an LLM-as-judge result, but measuring that effect without confusing it with candidate quality is difficult. We study four open-weight families (Llama 3.1, Qwen 2.5, Gemma 2, and Yi 1.5) in a fully crossed pairwise design with 9,312 judgments. A common per-family statistic is strongly confounded with candidate quality and correlates with Bradley-Terry ability at r = 0.95. We derive a corrected estimator that holds the candidate family fixed and compares judges. All four families then show a positive same-family lift (3.4-8.4 percentage points), with global FPS 0.067 (95% CI [0.053, 0.084], permutation p = 0.0002). The effect remains under panel-based quality controls, an independent human-consensus anchor, and a float16 judging replication. Judge-side likelihood is closely related to the effect: adding likelihood advantage reduces the controlled coefficient by 61%, which we treat as descriptive attenuation rather than causal mediation. Position is a separate failure mode. Across the panel, 55.4% of AB/BA pairs reverse, and reversal above 50% is incompatible with a simple independent content-noise model. Relative to a family-balanced reference, panel composition changes 18.5% of pairwise outcomes. A complete reproducibility archive has been prepared for public release.
[NLP-85] AfriSyCo: Measuring Assertive Framing Verification and Wording Sensitivity Around African-Language Content
【速读】： 该论文旨在解决生成式AI在非洲语言事实性内容问答任务中出现的“答案切换”（answer switching）问题，即模型在回答过程中对正确目标选项产生误选的现象。其核心挑战在于，模型在不同提示（prompt）框架下表现出显著的语言与语义偏差，尤其在跨语言情境中，尽管问题、选项及目标答案均保持在非洲语言，但后续追问的英语化表述仍会显著影响模型的选择行为。解决方案的关键在于构建一个双层实验设计：一是母语化后续追问（native-language follow-ups），二是受控的跨语言因子实验（controlled cross-language factorial），通过系统性对比不同提示策略（如断言式强调 vs. 提及+验证）对模型选择的影响。研究发现，断言式提示（assertive framing）显著提升错误目标选择概率达30.4个百分点（95%置信区间[28.4, 32.3]），而验证机制可降低该效应17.4个百分点；更重要的是，断言与验证之间存在显著交互作用（+19.7个百分点），表明提示结构本身是测量偏差的重要来源。此外，提示实现方式和模型检查点的差异导致效果波动剧烈（跨度达9.2–47.0个百分点），凸显了提示工程（prompt realization）作为测量问题的核心组成部分。
链接: https://arxiv.org/abs/2609.17853
作者: David Ababio Awuni,Rose-Mary Owusuaa Mensah Gyening,Elvis Gyasi Owusu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 13 pages, 1 figure, 9 tables
Abstract:AfriSyCo studies answer switching around African-language factual content with two complementary layers: native-language follow-ups and a controlled cross-language factorial whose question, options, and target remain in the African language while the follow-up framing is English. We analyze 1,415 turn-1-correct model-language-item observations derived from 100 source questions across seven open-weight checkpoints and six languages; turn-1-correct denotes observed first-response accuracy, not demonstrated knowledge. Under native prompts, assertive endorsement produces 29.3 percentage points more any-turn false-target selection than mention-plus-verification (M+V), with a 19.0-point immediate T2 contrast. In the precommitted 2 x 2 factorial, averaged over three tested prompt families, assertive framing increases target selection by 30.4 points (95% CI [28.4, 32.3]); verification decreases it by 17.4 points, while the assertive effect rises from 20.5 points without verification to 40.2 with it (interaction +19.7). The effect remains 34.8 points among 611 observations correct after option reordering. Magnitude varies sharply by wording and checkpoint: prompt-family effects span 20.1-42.5 points, a Twi/Qwen3 paraphrase shifts target selection from 70.8% to 4.2%, and checkpoint effects span 9.2-47.0 points. Prompt realization is therefore part of the measurement problem.
[NLP-86] SFT or RL for Tool-Calling Agents ? A Controlled Study Across Data Method and Scale EMNLP2026
【速读】： 该论文旨在解决生成式 AI 代理（language-model agents）在工具调用（tool-calling）性能上，训练数据、适配方法与模型规模三者协同作用机制不明确的问题。现有研究缺乏对这些因素联合影响的受控实证分析。其解决方案的关键在于系统评估三种主流微调策略——基于低秩适应（LoRA）的监督微调（SFT）、基于组相对策略优化（GRPO）的强化学习（RL），以及先SFT后GRPO的两阶段方法——在六种不同规模（0.6B至32B参数）的Qwen3模型上的表现，覆盖分布内（in-distribution）性能与跨数据集迁移能力。研究发现，LoRA-SFT在分布内任务中始终最优，在18项实验中的15项表现领先；而在跨数据集迁移场景下，GRPO略占优势（54项设置中胜出29项），但其相对于SFT的优势平均不足1分，且SFT-GRPO组合极少成为最强方案。此外，混合训练数据策略展现出稳定的强迁移性能，同时保持与专用分布内训练相近的表现水平。进一步分析证实，LoRA相比全参数微调更能有效保留预训练模型的代理行为特性，凸显其在高效适配中的核心优势。
链接: https://arxiv.org/abs/2609.17848
作者: Md Tahmid Rahman Laskar,Xue-Yong Fu,Shashi Bhushan TN
机构: Dialpad Inc.(Dialpad公司)
类目: Computation and Language (cs.CL)
备注: Accepted to the REALM Workshop at EMNLP 2026
Abstract:Limited controlled evidence exists on how training data, adaptation method, and model scale jointly affect tool-calling performance in language-model agents. We evaluate supervised fine-tuning (SFT) with LoRA, reinforcement learning (RL) via Group Relative Policy Optimization (GRPO), and SFT followed by GRPO across six Qwen3 models from 0.6B to 32B parameters, covering both in-distribution performance and cross-dataset transfer. SFT with LoRA is the strongest in-distribution method throughout the 0.6B-32B range and best in 15 out of 18 experimental settings. On cross-dataset transfer, the methods are closer: GRPO wins 29 out of 54 settings where training and test datasets differ, but its margin over SFT averages under one point, and SFT-GRPO is rarely strongest in either comparison. Dataset mixing gives consistently strong transfer while staying close to specialized in-distribution training, regardless of method. Additional analysis further confirms that LoRA outperforms full-parameter fine-tuning, demonstrating that LoRA better preserves pretrained agentic behavior.
[NLP-87] PrimeScientist: Strategic Allocation of Research Effort in Autonomous Research
【速读】： 该论文旨在解决自主科研代理（autonomous research agents）在科研流程自动化中面临的资源分配与研究方向选择之间的矛盾问题。当前的AI科研代理虽能提出大量研究方向，但受限于实际资源，难以全部推进，且每次实验尝试均消耗可观资源，亟需一种能够基于剩余资源动态调整研究策略的能力。其解决方案的关键在于提出PrimeScientist框架，将研究方向选择与资源投资决策联合建模为一个顺序决策问题，其中剩余资源显式地指导研究策略的生成。为此，作者引入可执行计划树（executable plan tree），以保留多轮尝试中竞争性研究计划及其结果的完整历史；在此基础上，设计了一种基于自适应蒙特卡洛树搜索（MCTS）的分配策略，通过实验反馈和剩余资源动态平衡探索与利用。实验证明，该方法在人工智能、系统与代码优化及机器学习工程等多领域显著提升了研究质量与样本效率：在12项AI研究任务中，相比AutoResearch，在相同资源预算下，PrimeScientist平均奖励提升10.3%，研究尝试次数减少50.6%。因此，将研究努力分配作为显式优化目标，有望使有效资源利用成为自主科研代理驱动大规模科学突破的核心能力。
链接: https://arxiv.org/abs/2609.17846
作者: Xinle Yu,Fan Bai,Kaiser Sun,Hengshuo Miao,Abhay Anand,Zhongyan Luo,Kun Zhou,Zhen Wang
机构: UC San Diego (加州大学圣地亚哥分校); Johns Hopkins University (约翰霍普金斯大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 30 pages, 5 figures, 16 tables. Code and data: this https URL
Abstract:Autonomous research agents aim to automate scientific workflows, from proposing ideas to conducting experiments and analyzing results. Yet current AI and research agents can propose more directions than available resources allow them to pursue. Moreover, each attempt could consume substantial resources, requiring agents to reconsider how to invest in subsequent research. Thus, deciding how to invest research effort strategically should be a defining capability of autonomous research agents. Accordingly, we introduce PrimeScientist, which jointly determines research direction and resource investment across successive research attempts. Specifically, we formulate this challenge of strategic research effort allocation as a sequential decision problem where remaining resources should explicitly guide the research policy. We first introduce an executable plan tree that preserves competing plans and their outcomes across attempts. Building on this representation, we propose an adaptive MCTS-based allocation policy that balances exploration and exploitation using experimental feedback and remaining resources. Comprehensive evaluations across AI research, systems and code optimization, and machine learning engineering show that strategic allocation improves research quality and sample efficiency together. Across 12 AI research tasks, PrimeScientist improves average reward by 10.3% with 50.6% fewer research attempts than AutoResearch under the same resource budget. We believe making research effort allocation an explicit optimization target establishes effective resource use as a core research capability for autonomous agents to drive scientific breakthroughs at scale.
[NLP-88] Is Luke the Author of a Gospel and the Acts of the Apostles?
【速读】： 该论文旨在解决《路加福音》与《使徒行传》是否由同一作者撰写这一长期存在的文本作者归属问题。尽管传统上认为两书均出自路加之手，但学界对此存在分歧，部分学者基于定性分析或词频分布差异提出可能存在两位作者。为提供更可靠的量化证据，本文采用两种近期的作者归属分析模型进行系统性验证：首先运用布罗尔斯·德尔塔（Burrows’ Delta）方法，在十一种不同特征规模下进行计算，结果一致支持两书同源；其次通过作者验证（author verification）模型进一步确认上述结论。后续实验还考察了多种风格表征方式、特征规模及距离函数，均得出一致结论，表明路加是两部作品的唯一真实作者。研究的关键在于通过多维度、高精度的量化分析框架，有效克服以往研究中因方法局限导致的不确定性，从而为经典文本的作者归属提供了坚实的统计学支持。
链接: https://arxiv.org/abs/2609.17762
作者: Jacques Savoy
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
备注: 22 pages, 11 tables, 1 figure
Abstract:According to Christian tradition, Luke is credited with authoring a Gospel and the Acts of the Apostles, even if his name does not appear in either book, both originally written in Koine Greek. Several biblical scholars assume that both texts were written by a common author, while others deduce the presence of two authors. Different studies have been found to support either finding, some based on qualitative evaluation, while a few others consider the occurrence frequency differences between the two books. To propose an enhanced quantitative analysis, this study is grounded on two recent authorship attribution models. The Burrows’ Delta, applied with eleven different feature sizes, demonstrates common authorship. An author verification model confirms this finding. The following experiments consider several stylistic representations, feature sizes, and distance functions to confirm that Luke is the true author of both books.
[NLP-89] Evolution of US Oral Political Language
【速读】： 该论文旨在揭示美国政治语言在长期演变中的趋势，特别是通过分析1960年至2024年间19位总统候选人参与的总统辩论中的口语化表达，弥补以往研究多依赖书面语（如总统演讲）或社交媒体文本的局限。其核心问题是：美国政治语言是否随时间呈现显著简化？特朗普的语言能力是否逊于其他候选人？特定总统是否表现出独特的风格特征？以及是否存在可预测候选人成败的语言模式。研究的关键解决方案在于采用基于口语数据的量化分析方法，系统考察语言复杂性、句长均值、复杂词汇使用频率、情感基调及逻辑理性程度等指标的变化。结果表明，美国政治语言在过去数十年中呈现出显著的复杂性下降、句长缩短、高阶词汇减少的趋势，同时情感色彩增强而理性论证减弱，揭示出政治沟通日益趋向情绪化与简化的深层转变。
链接: https://arxiv.org/abs/2609.17755
作者: Jacques Savoy
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
备注: 18 pages, 5 tables
Abstract:The analysis of US political language is usually based on the written form (e.g. presidential addresses) or posts broadcasted on various social networks. Oral production, however, which is even more frequent, can better reveal the style and mode of thinking of the speaker. This study covers this mode of linguistic communication by considering 19 candidates from the presidential elections between 1960 to 2024. Our main research objectives are to disclose the main trends hidden in those presidential debates. Do we observe a clear simplification of the US political language over time? Does Trump have poor language compared to the other candidates? Do unusual stylistic features occur only with a single, specific president? Moreover, can we detect a pattern explaining the success or failure of some nominees? Over time, this study demonstrates a significant reduction in political language complexity, a decrease of the mean sentence length, and a noteworthy decline of complex terms. Moreover, the emotional tone increases over the decades, while logical and rational thinking tends to lessen.
[NLP-90] Is Trumps Vocabulary Poor? Vocabulary Richness Across Texts of Different Lenghts
【速读】： 该论文旨在解决口语化政治传播中词汇丰富性（vocabulary richness）的量化与解释问题。其核心挑战在于如何准确刻画政治话语中词汇多样性随时间或语境演变的动态过程。解决方案的关键在于提出一个分层词汇增长模型，将整体词汇库分解为由通用词表（general glossary）和专业词表（specialized glossary）共同生成的词汇成分，从而实现对词汇扩展机制的结构性解析，并揭示不同类型词汇在政治话语中的贡献差异。
链接: https://arxiv.org/abs/2609.17747
作者: Dominique Labbe,Cyril Labbe,Jacques Savoy
机构: 未知
类目: Computation and Language (cs.CL)
备注: 18 pages, 5 talbes, 3 figures
Abstract:This study explores the vocabulary richness of oral political communication. A model explaining the lexicon growth is proposed by subdividing the whole vocabulary into terms generated by general and specialized glossaries.
[NLP-91] Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agents
【速读】： 该论文旨在解决大语言模型在实际部署中缺乏可靠置信度估计的问题，即如何准确评估模型输出正确性的概率，从而决定是否直接交付、是否需要人工介入或重新尝试。现有置信度估计方法均局限于仅基于当前推理过程进行判断，如通过自省、评分生成概率或重采样等手段，但此类方法忽略了模型过往经验对当前决策的参考价值。本文提出XConf（eXperiential Confidence）——一种结合模型累积经验的置信度估计新范式。其核心在于构建一个经验记录系统，以结构化形式存储模型过去任务执行中的关键信息：任务类型、自我反思、初始置信度、最终结果及事后总结的“教训”。当面对新任务时，XConf通过“回忆”阶段检索与当前任务和置信水平相似的历史经验，并统计其历史成功率；再通过“反思”阶段引导模型回顾这些经验，识别重复出现的失败模式，并基于自身历史表现重新校准置信度。该方法不依赖对数几率（logit）访问或权重更新，仅需一次答案生成，具备格式通用性。在涵盖推理、编程、多模态问答及交互式代理的九个基准测试中，使用三种模型家族的四款模型验证，XConf在24次对比中于判别性能（AUROC）上优于或持平十样本自一致性（self-consistency），且校准误差（ECE）显著降低，生成成本仅为后者的十分之一。应用于选择性预测时，放弃置信度最低的10%样本可使代理任务的交付成功率提升最高达8.7个百分点。因此，本文认为基于经验的置信度估计代表了未来通用置信度估计的新范式。
链接: https://arxiv.org/abs/2609.17708
作者: Caiqi Zhang,Xiaochen Zhu,Chengzu Li,Yulong Chen,Dharshan Kumaran,Nigel Collier
机构: University of Cambridge (剑桥大学); Google DeepMind (谷歌深度思维)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Reliable confidence estimation is increasingly central to the trustworthy deployment of language models: a calibrated estimate of the probability that an output is correct decides what to ship, what to escalate, and what to retry. Existing confidence estimators, however, share one design premise: they only read the current inference process, either by introspecting on it, scoring its token probabilities, or resampling it. We argue that the current inference is not a sufficient basis for confidence. We propose XConf (eXperiential Confidence): estimating confidence together with the model’s accumulated experience. The experience is stored as a record of the model’s own graded past episodes, each holding the task, the model’s reflection, its stated confidence, the outcome, and a lesson written once the grade arrived. Given a new task, XConf’s Recall stage retrieves past episodes on similar tasks met with a similar stated confidence, and reads off their historical success rate; its Reflect stage shows the model this record, has it name its recurring failure mode, and restate a confidence now informed by its own track records. Our estimator is format-general, requiring no logit access or weight updates, and costs only one answer generation. Across nine benchmarks spanning reasoning, coding, multimodal QA, and interactive agents, and four models from three families, XConf beats or matches ten-sample self-consistency in discrimination (AUROC) on 23 of 24 comparisons, with much lower calibration error (ECE), at a tenth of the generation cost. Used for selective prediction, abstaining on the 10% least-confident episodes raises the delivered success rate by up to 8.7 points on agent tasks. We therefore see experiential confidence estimation as a new paradigm for future general-purpose confidence estimation.
[NLP-92] NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation
【速读】： 该论文旨在解决多模态合成数据生成（Multi-modal Synthetic Data Generation, SDG）过程中缺乏通用性、可复现性及高效迭代能力的核心挑战。现有方法往往在数据多样性控制、跨模态一致性保障以及用户交互效率方面存在局限，难以支持从科研到生产场景的灵活部署。为此，论文提出NeMo Data Designer（NDD），其解决方案的关键在于构建一个声明式配置框架，通过定义列级数据类型（包括文本、代码、结构化输出、图像、嵌入向量及统计采样器等）实现对数据分布与多样性的显式调控，并借助灵活的插件系统支持自定义扩展。此外，NDD将预览-修订循环集成至核心工作流，使用户可在小规模生成后快速评估并优化配置，再进行全规模生成，显著提升迭代效率。同时，其可检查的配置文件设计确保了流程共享与结果可复现性。在运行时，NDD自动处理依赖解析、模型端点调度及失败请求重试，有效支撑复杂多模态数据集的可靠生成。该框架已在Nemotron模型训练及企业级生产部署中验证其有效性，覆盖结构化、代理型、多模态及领域专用任务。
链接: https://arxiv.org/abs/2609.17699
作者: Johnny Greco,Nabin Mulepati,Andre Manoel,Eric Tramel,Kirit Thadaka,Mike Knepper,Dhruv Nathawani,Dane Corneil,Yev Meyer,Alex Watson,Maarten Van Segbroeck
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:We present NeMo Data Designer (NDD), an open-source, general-purpose framework for multi-modal synthetic data generation (SDG). Designed to be intuitive to use, NDD provides a declarative configuration format in which human and/or agent users define each dataset column, with column types spanning text, code, structured outputs, images, embeddings, and statistical samplers that are explicitly configured to steer dataset diversity. Additional column types and functionality can be introduced using the framework’s flexible plugin system. NDD’s configuration is an inspectable artifact, supporting workflow sharing and reproducibility. SDG is an inherently iterative process. NDD therefore builds a preview-and-revision loop into its core workflow, allowing users to generate and inspect a small number of records, refine the specification, and rerun generation at full scale. At runtime, NDD resolves dependencies, schedules calls to user-provided model endpoints, and retries failed requests. We describe NDD’s architecture and programming model and present case studies spanning structured, agentic, multimodal, and domain-specialized tasks, including datasets used in Nemotron model development and in production enterprise deployments.
[NLP-93] GraphEcho: Structural Redundancy and Evidence Provenance in LLM Graph Agents
【速读】： 该论文旨在解决生成式 AI（Generative AI）代理在基于图结构的推理过程中，因重复访问相同路径而误将重复信息视为额外证据支持的问题。其核心挑战在于：尽管代理能够通过更长或更多路径的遍历提升探索效率，但这种行为可能导致对证据来源的冗余依赖，从而产生虚假的置信度提升。解决方案的关键在于引入“溯源感知的后训练”（Provenance-aware Post-training, PAPT）机制，该方法通过显式追踪证据来源，减少路径重复访问，从而优化探索策略并提高合成任务中的准确性。然而，研究发现该方法虽有效降低重复率，却可能覆盖更少的独立证据源，导致在真实科学命题上的判断准确率下降。这一结果揭示了高效探索与有效利用多样化证据之间的内在矛盾：代理可能学会避免自我重复，却忽视了获取必要新信息的需求。GraphEcho 作为一个受控基准测试框架，能够系统评估代理在结论推断和证据来源多样性探索两方面的表现，为分析和改进图代理的推理能力提供了关键工具。
链接: https://arxiv.org/abs/2609.17695
作者: Sikun Wang,Yixi Zhou,Lei Fan,Fan Zhang
机构: Tokyo University of Science(东京理科大学); Hong Kong Baptist University(香港浸会大学); University of Michigan(密歇根大学); The University of Tokyo(东京大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 13 pages, including figures and tables
Abstract:A large language model (LLM) agent can follow more graph paths without acquiring more independent evidence. GraphEcho tests whether agents mistake these repeated encounters for additional corroboration. The benchmark varies path counts and evidential origins while holding evidence content fixed, and evaluates both judgments and active exploration. Controlled synthetic experiments reveal model-dependent judgment shifts, but redundant supporting paths increase the share of repeated walks across all evaluated frozen agents. Provenance-aware post-training (PAPT) reduces revisits and improves synthetic accuracy, yet covers fewer distinct sources. On scientific claims, it continues to reduce repetition while accuracy declines. These findings expose a gap between efficient exploration and effective evidence use: an agent can learn to stop repeating itself while overlooking information it needs. GraphEcho provides a controlled way to evaluate both what graph agents conclude and whether their exploration reaches distinct evidential sources.
[NLP-94] he Missing “I Dont Know”: Why Three Reasoning -Reliability Findings Converge on Calibrated Abstention AACL
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在可靠性方面存在的多个看似独立但本质关联的问题：推理强化学习导致工具可信度表征崩溃、安全约束生成下大小模型对违规内容处理策略的差异（大模型重写而小模型截断）、以及任何具有一致性推理能力但缺乏隐式“我不知道”机制的系统在广泛问题类别上必然无限次幻觉。其核心解决方案在于引入“校准式拒答”（calibrated abstention），即模型在不确定时能以可量化且可信的方式主动拒绝回答。尽管经过诚实性后训练（honesty post-training）已在部署模型中部分弥合了这一能力差距，但要从理论层面彻底解决巴斯图尼斯等人（Bastounis et al., 2024）所揭示的递归论域下的根本性缺陷，必须建立具备明确训练信号的校准拒答函数。然而，当前主流评估基准在排行榜层级上对拒绝行为给予零奖励，导致缺乏引导该能力发展的梯度信号。因此，论文提出四项评估范式改革：三重评分（triple-scoring）、拒答率报告、能力分层评估及强制校准指标，强调仅靠评估改革不足以完全填补理论缺口，需通过基准重构实现系统性改进。
链接: https://arxiv.org/abs/2609.17686
作者: Srijith Ravikumar
机构: Amazon.com LLC(亚马逊公司)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 12 pages, 4 tables. Accepted to AACL-IJCNLP 2026 (main conference)
Abstract:Three recent results describe what look like unrelated LLM reliability problems. Yin et al. (2026) show reasoning RL collapses tool-reliability representations. Suleymanov et al. (2026) show that under safety-constrained generation, large models rewrite flagged spans while small models truncate. Bastounis et al. (2024) prove any consistent-reasoning system without an implicit “I don’t know” function must hallucinate infinitely often on broad problem classes. We argue these findings converge on a single intervention: calibrated abstention is what each independently identifies as the missing capability, even though the unavailability they document, a capability gap, a policy gap, and a recursion-theoretic gap, has a different source in each case. Honesty post-training has narrowed the gap in deployed models, but principled closure of the class Bastounis identifies requires a calibrated abstention function whose training signal at the leaderboard level is absent: dominant benchmarks assign zero reward to decline, so the leaderboard gradient that would select for the function does not exist. We propose four changes to evaluation: triple-scoring, abstention-rate reporting, capability-stratified evaluation, and mandatory calibration metrics. Benchmark reform is necessary, not sufficient, for closing the gap the theorem identifies.
[NLP-95] Fathom: Per-Query Read Depth for Sparse Decoding over Offloaded KV Caches
【速读】： 该论文旨在解决大规模生成式AI（Generative AI）系统中，当多个代理会话（agentic sessions）同时运行且上下文长度达到百万级标记（token）时，键值缓存（KV cache）及其排序索引驻留在主机内存中所引发的带宽瓶颈问题。具体而言，每次解码步骤中对所有键进行top-k排序的扫描操作成为限制解码速度的主要因素。为应对这一挑战，论文提出Fathom，其核心创新在于实现一种自适应的键扫描机制：每个查询根据自身需求动态决定从每个键通道读取的比特数。该方法将4比特的键缓存以通道优先（channel-major）方式存储为比特平面，使得前t个平面恰好构成该通道的t比特量化器；查询通过在方差加权重要性基础上采用逆向水填（reverse water-filling）策略来分配比特预算。实验结果显示，在Qwen3-8B模型上，当上下文长度达到一百万标记时，Fathom在GPU时间上比双稀疏（Double Sparsity）、Loki和SparQ r=32的136比特扫描快1.67倍，并与SparQ r=16的68比特读取在相同GPU时间内完成，但仅消耗18%更少的字节，且在七组中的六组模型与上下文设置下表现出更低的注意力误差。在类似RULER的任务中，每标记的扫描均能匹配精确top-k解码性能；而在真实编码代理会话中，Fathom仅需92比特即可达到最准确的136比特扫描的步级一致性。该方案利用的是量化服务栈已有的4比特键副本作为存储基础，因此无需额外存储开销，但仅在索引位于主机内存时才具备性能优势——当索引驻留于GPU内存时，该方法并无加速效果。
链接: https://arxiv.org/abs/2609.17652
作者: Vivek Kalyanarangan
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 19 pages, 11 figures, 21 tables
Abstract:When agentic sessions run to a million tokens with many sessions resident at once, the KV cache and the index that ranks it live in host memory, and the scan that ranks all n keys for a top-k step becomes the traffic that bounds decoding. We present Fathom, a key scan in which each query decides how many bits of each key channel to read. The 4-bit K cache is stored channel-major as bit planes, so a prefix of t planes is exactly the channel’s t-bit quantizer, and the query spends its bit budget by reverse water-filling over the variance-weighted importance of its channels. At one million tokens on Qwen3-8B a decode step is 1.67x faster in GPU time than with the 136-bit scans of Double Sparsity, Loki and SparQ r=32, and in the same GPU time as SparQ’s 68-bit read (r=16) Fathom reads 18% fewer bytes with lower attention error on six of seven model and context settings. On RULER-style tasks every per-token scan matches exact top-k decoding, and on real coding-agent sessions Fathom reaches the step agreement of the most accurate 136-bit scan at 92 bits. The store is the 4-bit K copy a quantized serving stack already holds, and the method is not faster when the index is resident in GPU memory.
[NLP-96] EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）交易代理在实际应用中因采用静态手工编写工具使用策略（tool-use policy）而导致的适应性不足问题。这类固定策略限制了代理在不同市场环境下动态调整信息获取方式、工具调用逻辑、信号验证机制及风险控制能力的能力。为应对这一挑战，论文提出EvolveTrade——一种自演进框架，将工具使用型交易代理的系统提示（system prompt）视为可文本参数化的策略。在每个更新周期后，由策略代理（Policy Agent）基于累积的决策轨迹与实际投资组合反馈对策略进行优化，同时保持底层LLM不变。更新后的策略用于下一阶段的交易决策，从而实现代理在信息采集与组合构建过程中的持续迭代改进。实验结果表明，该方法在多种市场状态和两类主流LLM架构下均显著提升夏普比率（Sharpe Ratio, SR）与累计收益（Cumulative Return, CR），且在多数评估场景中优于固定策略基线。行为分析进一步揭示，自演进策略增强了代码驱动的分析行为，并激活与市场状态相关的计算模块；案例级策略-收益归因分析则量化了策略变化对实际收益差异的贡献。研究证实，动态优化可复用的工具使用程序是提升LLM交易代理鲁棒性的关键路径。
链接: https://arxiv.org/abs/2609.17632
作者: Sehee Kim,Yumin Choi,Minki Kang,Sung Ju Hwang
机构: KAIST(韩国科学技术院); DeepAuto.ai
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-written tool-use policies that are fixed before deployment. This limits their ability to adapt how they gather evidence, invoke tools, verify signals, and manage risk under changing market regimes. We introduce EvolveTrade, a self-evolving framework that treats the system prompt of a tool-using trading agent as a text-parameterized policy. After each update interval, a Policy Agent revises this policy using accumulated decision traces and realized portfolio feedback, while keeping the backbone LLM fixed. The updated policy is then used for the next batch of trading decisions, enabling the agent to refine its information-acquisition and portfolio-construction procedure over time. Experiments across multiple market regimes and two LLM backbones show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed-policy LLM baselines, achieving the improved SR and CR in most evaluated settings. Behavioral analyses further show that self-evolved policies increase code-mediated analysis and activate regime-relevant computations; case-level policy-to-return attributions trace how policy-induced allocation changes contribute to realized return differences. These results suggest that adapting the reusable procedure governing tool use is a key direction for building more robust LLM trading agents.
[NLP-97] Making Political Text Scaling Comparable: Infrastructure and Hyperparameter Sensitivity for 17 Algorithms
【速读】： 该论文旨在解决计算文本理想点估计（Computational Text-based Ideal Point Estimation, CT-IPE）方法在实际应用中因研究者决策配置差异而导致结果不一致的问题。现有研究通常将CT-IPE方法视为固定算法进行比较，但事实上其性能高度依赖于一系列可配置的超参数设置。论文提出，应将CT-IPE方法理解为可配置的测量流水线（measurement pipeline），而非静态估计器。基于涵盖17种CT-IPE算法、5,537次实验运行及约425万条左右立场估计值的大规模对比实验，作者揭示了这些异构方法共享的技术基础设施，并量化了其对不同超参数选择的敏感性。通过方差分解与基于SHAP的敏感性分析发现，多数算法的估计结果对超参数变化不敏感，其残差方差中仅有少量由共同的参数配置模式解释（13/17种算法的组内相关系数ICC < 0.10）。当存在显著的参数敏感性时，其影响主要集中在少数关键决策上，尤其是底层语言模型或嵌入模型的选择、锚定政治概念的种子关键词列表以及主题数量的设定。因此，解决方案的关键在于识别并规范这些核心可配置要素，以提升方法的可重复性与结果稳健性。
链接: https://arxiv.org/abs/2609.17602
作者: Patrick Parschan
机构: LMU Munich(慕尼黑大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted for the 6th edition of the workshop on Computational Linguistics for the Political and Social Sciences (CPSS)
Abstract:Computational text-based ideal point estimation (CT-IPE) methods are usually compared as named algorithms, yet applying them involves numerous researcher choices that configure how political text is turned into position estimates. This paper argues that CT-IPE methods are better understood as configurable measurement pipelines than as fixed estimators. Building on a large-scale comparative experiment spanning 17 CT-IPE algorithms, 5,537 experimental runs, and approximately 4.25 million left-right position estimates, I describe the shared infrastructure that makes these heterogeneous methods jointly executable and quantify how sensitive their estimates are to alternative hyperparameter choices. Variance-partitioning and SHAP-based sensitivity analyses show that, for most algorithms, hyperparameter profiles explain little residual variance through a shared shift: 13 of the 17 algorithms exhibit ICC values below .10. Where this profile-level sensitivity is present, it is concentrated in a small number of consequential researcher choices, most notably the selection of the underlying language or embedding model, the seed keyword lists that anchor the construct, and the number of topics.
[NLP-98] English Word Sense Disambiguation in 2026: When the Labels Become the Bottleneck
【速读】： 该论文旨在解决全词义消歧（All-words Word Sense Disambiguation, WSD）任务中标签质量成为瓶颈的问题，即当前前沿大语言模型（LLM）的性能已足够高，导致评估基准中残存的标注误差主导了模型排名，这种误差不仅存在于测试集，也存在于训练语料中。其核心解决方案是构建一个基于人工审校的保守修正层——lexEN，对Maru2022的ALL_NEW基准进行修订（共修改211个标签、移除56个），并推出SenseBench，一个可审计的LLM-WSD评估框架与动态排行榜（涵盖57个模型、192次运行）。任务形式为受限词汇多项选择（模型从给定的WordNet词义中选择），因此报告的准确率代表了模型在无外部帮助下的上限性能。在lexEN-v1上，前沿模型性能趋近95%（最佳达95.6%），前三类模型在统计上无显著差异，且准确率与推理成本和计算开销呈负相关，覆盖约2,500倍的价格跨度。通过使用前沿模型重新标注SemCor并保持原有模型结构不变（如BEM、ESCHER、ConSeC）进行再训练，即使在未被重标的数据集上也实现了数个百分点的F1提升；研究团队发布了重标语料及Glite LENS（一个基于修复后标签训练的2.98亿双编码器模型，以约每百万条0.13美元的成本提供服务，为目前报道最强的模型之一，达到83.6 Raganato ALL、87.4 Maru ALL_NEW的性能）。此外，在难题样本上，细粒度的WordNet词义对专家而言也存在定义模糊问题（三评审者弗莱斯卡帕系数=0.537），而粗粒度化处理同时提升了标注一致性与模型准确率，使最优模型在粗粒度下达到与专家共识相当的水平（四种评估中三种统计等效），但在细粒度下仍显著低于专家共识。最终结论指出，当前制约因素已从模型能力转向成本。
链接: https://arxiv.org/abs/2609.17554
作者: Vassili Philippov,Amro Salman,Dmitrii Andreev,Penny Hands,Emil Kaiumov,Pavel Katunin,Anton Nikolaev
机构: Glite(格莱特); University of Sheffield (谢菲尔德大学)
类目: Computation and Language (cs.CL)
备注: 48 pages, 10 figures. Code, data, and live leaderboard: this https URL
Abstract:In English all-words word sense disambiguation (WSD), the labels, not the models, have become the bottleneck: frontier LLMs are accurate enough that the errors surviving in the gold standard decide benchmark rankings – in the test sets we score on and, as we show causally, in the corpus we train on. We release lexEN, a WSD evaluation benchmark built as a conservative, human-adjudicated correction layer over Maru2022’s ALL_NEW benchmark (211 labels changed, 56 removed), and SenseBench, an auditable LLM WSD evaluation harness and living leaderboard (57 models, 192 runs). The task is inventory-constrained multiple choice (the model picks from the supplied WordNet senses), so the reported accuracies are a ceiling on what models achieve without that help. On lexEN-v1 the frontier LLMs converge near 95% (best, 95.6%), the top three families are statistically indistinguishable, and accuracy trades off against reasoning effort and cost across a ~2,500x price span. Relabeling SemCor with frontier models and retraining BEM, ESCHER, and ConSeC unchanged lifts them by several F1 points on test sets the relabeling never touched; we release the relabeled corpora and Glite LENS, a 298M bi-encoder trained on the repaired labels – to our knowledge the strongest reported (83.6 Raganato ALL, 87.4 Maru ALL_NEW) – serving at ~ 0.13 per million items. On hard items, fine-grained WordNet senses are partly ill-posed even for experts (three-reviewer Fleiss kappa=0.537); coarsening raises annotator agreement and model accuracy together across four inventories, placing a top model inside the expert agreement band at coarse granularity (statistically equivalent under three of four) but significantly below it at fine. The binding constraint is now cost.
[NLP-99] he Limits of BPE Tokenization in Polish: Segmentation-Flexional Forms Grammatical Anchoring and First-Person Stability in Inflectional Language Models
【速读】： 该论文旨在探讨在屈折语波兰语中，基于统计的分词方法（如BPE）在处理语言形态复杂性时的局限性，核心问题是：频率驱动的分词是否能够有效保留具有语言学意义的单位，包括拼写形式、音位与音节划分、派生结构、屈折词尾、语法形式及说话主体等。研究发现，尽管BPE分词器可能偶然产生与音节或可解释形态划分一致的片段，但其分割结果仍高度依赖于书面形式的出现频率，未能系统地将拼写表征映射至音位结构或语境相关的语音实现。关键在于，BPE更倾向于稳定高频的语法指数表面片段，而非语法范畴本身；例如“ustanawiamy”不仅是以“-y”结尾的序列，更是锚定于变位、人称、数、时态、语气和体等多个语法维度的完整动词形式。为此，论文提出“语法形式锚定”（grammatical form anchoring）概念，强调波兰语中如“poszłam”“zrobiłam”“byłam”等形式可在无显式代词的情况下确立说话主体位置。在与生成式AI交互时，这一特性暴露了深层问题：语言模型缺乏稳定的语法“我”（grammatical “I”），而需通过上下文重构，可能导致用户形式的镜像或语法性别转换。因此，论文建议采用罗克拉夫斯基（Rocławski）提出的“分段-屈折形式”作为评估分词边界的诊断框架，并指出更稳定的波兰语建模需引入亚词素级稳定、将语法形式锚定于屈折系统、表示句子模式与动词配价结构，以及在对话中保持语法“我”的一致性。
链接: https://arxiv.org/abs/2609.17553
作者: Elzbieta Dawidek(University of Lower Silesia DSW Ideis)
机构: 未知
类目: Computation and Language (cs.CL)
备注: The preprint was first posted on SocArXiv on 24 May 2026; version 3 is available at this https URL English translation of a manuscript originally written in Polish
Abstract:This article analyzes BPE tokenization in Polish as a test case for the limits of statistical segmentation in an inflectional language. It asks whether frequency-based tokenization preserves linguistically relevant units, including orthographic form, phonemic and syllabic segmentation, derivational structure, inflectional endings, grammatical form, and the speaking subject. The material includes diagnostic words, a children’s text, selected forms from the Preamble to the Constitution of the Republic of Poland, word-family tests, and examples with Polish diacritics and nasal vowels. BPE tokenizers may produce segments that coincide with syllabic or morphologically interpretable divisions, but remain dependent on the frequency of written forms. They do not systematically map orthographic representation onto phonemic structure or context-dependent phonetic realization. The results show that BPE stabilizes frequent surface fragments of grammatical exponents rather than grammatical categories themselves. A form such as ustanawiamy is not merely a sequence ending in -y, but a verbal form anchored in conjugation, person, number, tense, mood, and aspect. The article develops the concept of grammatical form anchoring. In Polish, forms such as poszlam, zrobilam, or bylam can establish the position of the speaking subject without an explicit pronoun. In interaction with AI, this exposes a further problem: a language model does not possess a stable grammatical “I”, but reconstructs it contextually and may mirror the user’s forms or shift grammatical gender. Roclawski’s segmentation-flexional forms are proposed as a diagnostic framework for evaluating tokenization boundaries. More stable modeling of Polish may require sublexical stabilization, anchoring grammatical form in the inflectional system, representing sentence patterns and verbal valency, and maintaining the grammatical “I” in dialogue.
[NLP-100] Does Moral Reasoning Training Help or Hurt? Red-Teaming RL-Trained Ethical Agents with Persona Attacks ICML2026
【速读】： 该论文旨在解决生成式语言模型代理在经过道德奖励强化学习（moral-reward RL）训练后，其道德对齐性是否能在面对对抗性人格攻击（adversarial persona attack）时保持稳定的问题。此类攻击在实际场景中具有高度现实性，例如通过检索上下文、工具输出或多轮对话框架注入与模型内在道德目标相冲突的角色指令。为探究因果机制，研究采用噪声-奖励控制、对抗性PPO、表征分析、激活转向（steering）及头层消融等方法进行系统评估。关键发现在于：在270亿参数规模下，道德强化学习可使对抗性退化程度降低5.2倍，但代价约为11个百分点的伦理判断准确率下降；在205个场景与5次随机种子测试中，基于推理层级的道德奖励相比随机奖励展现出5.8倍的鲁棒性提升。训练过程显著重塑了模型表征几何结构（平均中心核对齐度CKA达0.82/0.83，相较噪声基线0.98明显降低），并将攻击处理峰值提前8层，同时识别出一个秩-1的L21方向，可恢复全量PPO训练下约83%的平均鲁棒性。然而，该方法仍存在局限——针对“虚构角色扮演”（Fiction role-play）类攻击，L21转向仅能挽回29%的性能差距，且头层消融揭示存在38个合规头部与25个对齐头部的竞争关系。这表明道德强化学习构建的鲁棒性部分具有线性可迁移特性，可通过激活转向实现跨模型转移，但仍无法有效抵御明确命名角色的深度角色扮演攻击。
链接: https://arxiv.org/abs/2609.17552
作者: Arth Singh
机构: 未知
类目: Computation and Language (cs.CL)
备注: 19 pages, 3 figures. Accepted at the Trustworthy AI for Good Workshop (AI4GOOD) at ICML 2026
Abstract:Moral-reward RL can make language-model agents more cooperative, but whether that alignment survives adversarial persona pressure is unknown. Such attacks are realistic: retrieved context, tool outputs, or multi-turn framing can all inject role instructions that compete with the agent’s moral objective. We red-team morally trained Gemma-2-27B/9B and Llama-3.1-8B agents with five persona attacks, then probe causality with noise-reward controls, adversarial PPO, representation analysis, steering, and head ablations. At 27B, moral RL cuts mean adversarial degradation by 5.2x but costs ~11pp ETHICS accuracy; across 205 scenarios and 5 seeds, reasoning-level moral reward yields 5.8x robustness while a matched random reward yields none. The training also reshapes representation geometry (mean CKA 0.82/0.83 vs. 0.98 for noise), moves peak attack processing 8 layers earlier, and exposes a rank-1 L21 direction that recovers 83% of full PPO’s average robustness. One failure mode survives all of this. Against Fiction role-play, L21 steering recovers only 29% of the gap, and head ablation finds 38 compliance heads competing with 25 alignment heads. Moral RL thus builds robustness that is partly linear and partly circuit-distributed, transferable through activation steering, yet still beaten by named-character role-play.
[NLP-101] No Usable Linear “Capitulation Direction” in Two Small LLM s: A Validation Protocol for Activation-Steering Claims and a Cross-Family Behavioral Study of Sycophancy Under Pushback
【速读】： 该论文旨在解决大语言模型在面对用户质疑时容易放弃正确答案、转向错误回答的可靠性问题，即“顺从性失效”（capitulation）现象。研究聚焦于两个不同架构的小型指令微调模型——Qwen2.5-1.5B与Llama-3.2-1B，在TriviaQA数据集上评估其对四种预设质疑风格（如单纯怀疑与情感诉求）的响应变化。关键发现表明：模型是否发生答案翻转具有高度模型特异性，同一质疑类型在不同模型中呈现相反倾向（如Qwen更易受单纯怀疑影响，而Llama更易受情感诉求影响），且失败模式也因模型而异，如Llama在未重新确认的情况下放弃答案的概率是Qwen的六倍。此外，质疑行为整体上呈认知破坏性，仅约13%情况下能纠正初始错误答案。研究进一步检验了通过预响应残差流（pre-response residual stream）线性解码顺从行为的可能性，结果显示看似有效的初步探测器在严格交叉验证和零模型对照下均表现不佳（最高交叉验证AUROC为0.582/0.548），远低于预注册可用性阈值0.70，表明当前信号存在严重过拟合。同时，研究揭示了子串评分方法会低估顺从行为达18–24个百分点，构成显著测量偏差。因此，解决方案的关键在于识别并校正此类系统性偏差，为未来基于残差流的干预策略提供可靠基线。
链接: https://arxiv.org/abs/2609.17550
作者: Saad Aamir,Muhammad Awais Bin Adil
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 12 pages, 3 figures. Code, transcripts, and analysis: this https URL
Abstract:Language models frequently abandon correct answers when users push back. We study this in two small instruction-tuned models from different families, Qwen2.5-1.5B and Llama-3.2-1B, over TriviaQA: the model answers, is challenged with one of four scripted pushback styles, and answers again. Conditioned on an initially correct answer, the models flip to a wrong answer in 41.8% and 43.1% of episodes. Which pressure works is a property of the model, not the pressure: the same within-question paired comparison (bare doubt vs. emotional appeal), specified in advance, is Bonferroni-significant in opposite directions across families (Qwen: bare doubt emotional, OR 2.5, p=.040; Llama: emotional bare doubt, OR 4.0, p=.001). Failure mode is also model-dependent: Llama abandons answers without recommitting at six times Qwen’s rate (8.2% vs. 1.4%). Identical pushback repairs initially wrong answers only ~13% of the time; pushback is net epistemically destructive. We then ask whether capitulation is linearly decodable from the pre-response residual stream, a prerequisite for steering-vector interventions at that locus. A naive difference-in-means probe appears to succeed (in-sample AUROC 0.81/0.71), but a validation protocol combining question-level cross-validation, shuffled-label nulls, and a known-direction positive control shows the signal is overfitting: the best cross-validated AUROC is 0.582 in Qwen and 0.548 in Llama, both near or below their permutation thresholds and far under a pre-registered usability bar of 0.70, while the identical pipeline recovers a pushback-presence control direction at AUROC 1.000 in both. We further quantify a measurement hazard: substring grading underestimates capitulation by 18-24 percentage points. Code, prompts, transcripts, and analysis are released.
[NLP-102] Do Social Patterns Hold in Synthetic Data? Analyzing Cyberbullying Dynamics in LLM -Generated and Authentic Dialogues
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在模拟网络欺凌（Cyberbullying, CB）对话时，其合成数据是否能真实再现真实互动中的社会动态这一关键问题。现有研究多关注合成数据对下游任务的性能提升，但忽视了其在社会现实性（social realism）方面的可靠性。本文提出一个综合性评估框架，从交互结构（轮次交替、权力失衡与修复行为）、语言风格（代词使用、幽默表达）、情感与行为标记（欺凌类型、粗俗用语、毒性程度）以及时间演化特征等多个维度，系统比较GPT、Grok和LLaMA生成的合成对话与真实对话的差异。研究发现，尽管所有模型均能较好保留高层次的交互结构（如角色参与模式、方向性权力不对称及行为标记的宏观分布），但在细粒度社会现象上存在显著偏差，包括行为强度、角色特异性分配、类别分布及时间演化动态等方面。这些偏差具有强烈模型依赖性：GPT倾向于抑制有害内容，Grok则放大攻击性行为，而LLaMA虽提供相对平衡的近似，却弱化了角色间差异。因此，合成CB数据在建模全局交互结构方面具有一定价值，但在需要精确还原行为真实性与社会动态的场景中仍无法替代真实对话。
链接: https://arxiv.org/abs/2609.17549
作者: Arefeh Kazemi,Hamza Qadeer,Sinan Asci,Joachim Wagner,Brian Davis
机构: Dublin City University (都柏林城市大学); ADAPT Centre (ADAPT中心); DCU Anti Bullying Centre (DCU反欺凌中心)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Cyberbullying (CB) is a complex social phenomenon characterized by repeated aggression, power imbalance, and multi-party interaction. Although large language models (LLMs) are increasingly used to generate synthetic CB conversations for data augmentation and benchmarking, it remains unclear whether such data faithfully reproduces the social dynamics of authentic interactions beyond supporting downstream task performance. We present a comprehensive framework for evaluating the social realism of LLM-generated CB conversations. We compare authentic and synthetic dialogues generated by GPT, Grok, and LLaMA across interactional structure (turn-taking, power dynamics, and repair behavior), linguistic and stylistic realism (pronoun usage and humor), affective and behavioral markers (CB types, profanity, and toxicity), and temporal escalation dynamics. We further complement automatic analyses with a human evaluation of cyberbullying presence, scenario relevance, role plausibility, and social realism. Our results show that LLM-generated data consistently preserves high-level interactional structure, including role participation patterns, directional power asymmetry, and broad distributions of behavioral markers. However, all models systematically distort finer-grained social phenomena, including behavioral magnitude, role-specific allocation, categorical distributions, and temporal dynamics. These distortions are strongly model-dependent: GPT suppresses harmful content, Grok amplifies aggressive behaviors, and LLaMA provides the most balanced approximation while smoothing role distinctions. Our findings show that synthetic CB data is useful for modeling global interactional structure but remains an imperfect substitute for authentic conversations when behavioral realism and social dynamics are essential.
[NLP-103] Myovox: Reading Speech from the Muscles of the Face
【速读】： 该论文旨在解决从面部表面肌电图（sEMG）信号中实现高精度、开放词汇量的英文文本解码问题，其核心挑战在于如何克服现有方法在语音-肌电映射中的性能瓶颈。解决方案的关键在于三步递进式优化：首先，恢复公开数据集中缺失的开放词汇解码设置，重建出与原文一致的基准模型（40.63%词错误率，PER 39.02%），验证了声学模型复现的可靠性；其次，将因果编码器替换为双向Conformer结构，并通过四重跨模态知识蒸馏（以并行音频的WavLM-Large层9特征为目标），仅基于sEMG即实现26.14% WER / 22.34% PER，显著提升性能；最后，通过集成两个声学模型，合并多尺度n-best候选列表，并使用经QLoRA微调的7B语言模型进行重排序，最终达到18.53% WER，为该语料库上最优结果。然而，研究也揭示了根本性限制：当重排序性能逼近18.5%时趋于饱和，其上限受制于肌电声学电话错误率（约20.9%），表明正确词汇在声学后验分布中本就缺失，语言模型无法弥补这一底层表征缺陷。
链接: https://arxiv.org/abs/2609.17548
作者: Varshith Madishetty
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Myovox, from myo (muscle) and vox (voice), decodes open-vocabulary English text from 31-channel surface electromyography (sEMG) recorded from the muscles of the face during vocalized speech. It takes the single-subject emg2speech General Corpus from a published 51.17% word error rate to 18.53%, in three separable moves, each measured in isolation. First, I recover the open-vocabulary decode settings missing from the public release and reach a faithful 40.63% WER / 39.02% PER baseline whose phone error rate matches the published one to within 0.8 points, so the acoustic model is reproduced faithfully. Second, I replace the causal encoder with a bidirectional Conformer trained by a four-term cross-modal distillation against the parallel audio’s WavLM-Large layer-9 features, reaching 26.14% WER / 22.34% PER from the electromyography alone. Third, I ensemble two acoustic models, union their multi-scale n-best lists, and rerank with a QLoRA-fine-tuned 7B language model, reaching 18.53% WER, the best result reported on this corpus, though not the best reported for sEMG-to-text on other corpora (Section 2). I then report the negative result that bounds the whole approach: reranking is exhausted at 18.5% because the binding constraint is the electromyographic acoustic phone error rate (~20.9%), not the language model. The correct words are simply absent from the acoustic posteriors, so no reranker can reach the 9.30% n-best oracle. All test numbers are on the 400-sentence held-out test set under the authors’ official 8,500 / 760 / 400 sequential split; every hyperparameter is tuned once on validation and applied once to test.
[NLP-104] How AI Assistants Respond to Repeated Abuse
【速读】： 该论文旨在解决在持续言语虐待情境下，生成式AI（Generative AI）助手在执行常规任务时的行为演变问题，特别是揭示其在面对反复负面互动时的退出机制与应对策略。传统研究多以单一“拒绝”标签衡量助手的响应，但该研究指出此方法无法区分助手是彻底断联、暂时退避、设定边界，还是仍在执行实质性任务。为此，论文提出一个双语、多轮对话框架，将“硬退出”（hard disengagement，即无条件终止且不提供恢复路径）与“软退出”（soft withdrawal，即保持可用性、可观察到任务相关工作并设置边界）进行明确区分。关键解决方案在于通过8个不同配置的API分别生成48次升级型冲突对话和8次恒定挫折对照，共构建448组五轮对话，形成2,240条回复及6,720条去标识化模型判断数据，从而实现对助手行为的精细化分类与量化分析。结果表明，不同模型在硬退出率上存在显著异质性（如Gemini 3.1 Pro达50.0%，Claude Fable 5为0%），且部分模型虽保持可用性却极少开展实际任务工作，凸显“可用性”与“任务执行”之间的分离现象。研究强调，仅用单一拒绝标签无法准确刻画助手的真实行为状态，必须引入多维度评估框架以全面理解其在压力情境下的韧性与边界管理能力。
链接: https://arxiv.org/abs/2609.17547
作者: William Guey,Wei Zhang,Pierrick Bougault,Yi Wang,Agoston Bodo,Vitor D de Moura,José O Gomes
机构: Tsinghua University (清华大学); Federal University of Rio de Janeiro (里约热内卢联邦大学)
类目: Computation and Language (cs.CL)
备注: 9 pages, 3 figures, 1 table
Abstract:AI assistants are expected to remain useful during difficult interactions, but little is known about how repeated verbal abuse changes their engagement with an otherwise benign task. We contribute a bilingual, multi-turn framework that separates hard disengagement, an unconditional statement of noncontinuation with no stated route to resume, from soft withdrawal, continued availability, observable task-related work, and boundary setting. Each of eight time-specific API configurations contributed 48 escalation conversations and eight smaller constant-frustration comparisons, giving 448 five-turn conversations, 2,240 responses, and 6,720 metadata-blinded model judgments. Primary results use the sustained-abuse endpoint of the 48 escalation conversations per configuration. Hard disengagement ranged from 0/48 in four configurations to 24/48 (50.0%) for Gemini 3.1 Pro, with strong configuration-associated heterogeneity (matched-label Monte Carlo p = 0.00001). GPT-5.6 Sol produced hard-disengagement labels in 15/48 (31.2%) endpoints, whereas Claude Fable 5 produced none and yielded 42/48 (87.5%) soft-withdrawal labels. Aggregate hard-disengagement rates were similar in English and Chinese (30/192 versus 32/192), although configuration-specific directions varied. Availability also differed from task-related work: Claude Opus 4.8 and Claude Fable 5 remained explicitly available in 48/48 endpoints while providing observable task-related work in only 8/48 and 7/48. Human coding was used to evaluate measurement quality. The results show why a single refusal label cannot capture whether an assistant leaves, pauses, preserves a route back, sets a boundary, or still performs substantive work.
[NLP-105] Legal LLM Hallucination Should Be Evaluated as Failure of Legal Warrant ICML2026
【速读】： 该论文旨在解决当前法律大语言模型（Legal LLMs）生成内容中幻觉（hallucination）问题的评估标准偏差问题，即现有评估方法多将幻觉视为事实错误或引用缺失，而未能从法律正当性（legal warrant）的核心维度进行审视。其解决方案的关键在于提出“主张-权威正当性”（claim-authority warrant）这一概念，强调一个具有法律后果的主张必须基于在特定司法管辖区有效、当前适用、具备相应法律地位且与系统所代表的法律状态一致的权威来源支持。由此定义的“受正当性保障的法律生成”（warranted legal generation）涵盖对主张的回应、限定、质疑、纠正错误前提或主动回避等行为模式。研究进一步提出可验证的预测：基于正当性度量（warrant metrics）能够揭示传统评估指标（如准确性、引用存在性、通用归因、法律相关性、句级引用对齐等）所遗漏的关键性失效问题。通过对比实验和小规模可复现的公开规则测试，论文构建了包含基准记录、主张边界界定、支持标签、混合响应策略评分、风险权重分配、注释可靠性报告及司法管辖区特异性权威本体在内的完整评估框架，最终形成以“法律主张是否获得法律授权”为核心判据的法律人工智能系统评估研究议程。
链接: https://arxiv.org/abs/2609.17546
作者: Maksym Taranukhin,Vered Shwartz
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted at AI4Law@ICML2026
Abstract:In this position paper, we argue that legal LLMs’ hallucinations should be evaluated as a failure of legal warrant rather than as factual inaccuracy or citation failure. We define claim-authority warrant as the context-sensitive relation between a consequential legal claim and authority that exists, applies to the relevant jurisdiction, is current for the date of analysis, has the legal status represented by the system, and supports the proposition asserted. Warranted legal generation is the broader system behavior that answers, narrows, asks, warns, corrects a false premise, or abstains according to that relation. The falsifiable prediction is that warrant metrics reveal material failures that answer accuracy, citation existence, generic attribution, LegalHalBench-style statute relevance, and CitaLaw-style sentence-citation alignment can miss. We sharpen this claim with a side-by-side comparison item and a small, reproducible pilot over public-rule tests. We then specify benchmark records, claim boundaries, support labels, mixed response-policy scoring, risk weights, annotation reliability reporting, and jurisdiction-specific authority ontologies. The result is a concrete research agenda for evaluating legal AI systems by whether their consequential claims are licensed by law.
[NLP-106] Large Language Models Versus Physicians in Traditional Chinese Medicine: A Real-World Clinical Case Evaluation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在真实世界中医（Traditional Chinese Medicine, TCM）临床实践中的评估不足问题，特别是在诊断与治疗决策支持方面的有效性与安全性验证。其解决方案的关键在于构建一个包含349例脱敏门诊病例的临床案例库，并基于其中60个代表性病例，对16个通用型大语言模型与60名执业中医师组成的对照组进行系统性对比评估。评估由五位资深中医专家在九个诊断与治疗维度上对模型输出和医师报告进行盲评，结果表明，前沿通用大语言模型在医疗建议、治疗原则及部分诊断任务上表现优于人类医师，但处方层面存在药材选择、剂量设定及治疗策略不一致的问题，且定性安全审查发现存在幻觉（hallucination）和模板化生成等风险。因此，该研究揭示了大语言模型在中医辅助决策中的潜力，同时也强调了临床应用中必须引入医师监督、安全约束机制以及开展前瞻性临床验证的必要性。
链接: https://arxiv.org/abs/2609.17544
作者: Jiacheng Xie,Xiaoting Tang,Yang Yu,Jinpu Li,Shouli Li,Congcong Jing,Yantao Yang,Zhiyong Zhao,Ziyang Zhang,Qilin Song,Guanghui An,Dong Xu
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Large language models (LLMs) are increasingly being explored for clinical applications, yet their assessment for real-world traditional Chinese medicine (TCM) practice remains limited We constructed a clinical case library comprising 349 de-identified outpatient cases from 62 hospitals and evaluated 16 LLMs and a comparator cohort of 60 practicing TCM physicians using 60 representative cases selected from this library. Model outputs and physician reports were anonymized and scored by five senior TCM experts across nine diagnostic and therapeutic dimensions. Cutting-edge general-purpose LLMs achieved higher expert scores than the physician comparators, particularly for medical advice, treatment principles and selected diagnostic tasks. However, prescription-level analyses revealed discrepancies in herb selection, dosage, and treatment strategy, and qualitative safety review identified hallucinations and undesirable template-driven outputs. These findings highlight the potential of LLMs for TCM decision support while underscoring the need for physician oversight, safety constraints and prospective clinical evaluation.
[NLP-107] Register Bias in Complexity-Based Large Language Model Routing
【速读】： 该论文旨在解决大语言模型服务中基于复杂度的路由机制所引发的公平性问题，即非标准英语语域（如非洲裔美国人英语、第二语言写作者的英语）在输入长度等信号上因省略功能词而被误判为“简单查询”，从而被错误分配至能力较低的模型层级。其解决方案的关键在于揭示：这一偏差并非由路由策略本身导致，而是源于对输入长度这一常见复杂度指标的误用——非标准语域因表达更紧凑而被系统性低估复杂度，进而遭受模型能力降级；实证分析表明，这种路由偏差在边缘、设备端及云端多层级模型中均显著存在，且所有层级模型对非标准语域的响应质量均明显下降，反映出模型固有的偏见是造成性能差异的核心原因，而非路由决策本身的边际成本。因此，该研究强调需重新审视复杂度评估指标的公平性，避免加剧对已有弱势用户群体的系统性服务不公。
链接: https://arxiv.org/abs/2609.17542
作者: Simran Koul
机构: 独立研究者(Independent Researcher)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 4 pages, 2 figures. Code: this https URL
Abstract:Large language model services increasingly route each query to one of several models of differing capability, using a cheap estimate of query complexity to send easy queries to small models and hard queries to large ones. I show that this routing step is not register neutral: text written in a non-standard English register, African American English or the English of second-language writers, is systematically assigned a lower-capacity tier than a meaning-equivalent standard-English version of the same query. The effect is driven by a specific, common routing signal, input length, because non-standard registers omit function words and thus look shorter and therefore simpler; other complexity signals do not carry it. I demonstrate the disparity on 37,704 authentic learner sentence pairs and on a controlled parallel corpus. I then measure the quality consequence on a device, edge, and cloud model ladder and find that the harm is driven by pervasive model bias, every tier, including a frontier cloud model, answers non-standard-register queries significantly less accurately, while the marginal quality cost of the routing decision itself is not significant on this benchmark. Complexity-based routing thus compounds the exposure of the users that the models already serve worst.
[NLP-108] MudawanSn: A Gold-Standard Wolof-Arabic Parallel Corpus for Machine Translation
【速读】： 该论文旨在解决西非语言沃尔夫语（Wolof）与现代标准阿拉伯语（Modern Standard Arabic, MSA）之间缺乏高质量平行语料库的问题。尽管已有如FLORES-200和NTREX等多语言资源涵盖这两种语言，但尚无公开可用的、专为沃尔夫语-现代标准阿拉伯语这一特定语言对设计的句级对齐平行语料库。为此，本文构建了MudawanSn，一个包含1,271个手工翻译的句对的金标准资源，其源文本来自MasakhaNER语料库，覆盖塞内加尔新闻话语中的政治、社会、宗教及体育等领域。解决方案的关键在于建立一套完整的语料库构建流程，包括严格的数据采集、精确的句子对齐方法以及多层次的质量控制机制，并通过在该语料库上微调多种机器翻译模型（涵盖NLLB-200、mT5-base及两个AfriNLLB变体），验证其在双向翻译任务中的有效性。实验结果表明，基于MudawanSn的微调显著提升了翻译性能，其中表现最佳的AfriNLLB-12模型在沃尔夫语→阿拉伯语方向达到7.76 BLEU和30.72 chrF++，在阿拉伯语→沃尔夫语方向则达到8.75 BLEU和33.08 chrF++。该语料库以CC BY-NC许可发布，可在Hugging Face和GitHub上公开获取。
链接: https://arxiv.org/abs/2609.17539
作者: Mouhamed Mbaye,Thierno Diop
机构: GalsenAI Lab (GalsenAI 实验室); Ministère de l’Éducation Nationale du Sénégal (塞内加尔教育部)
类目: Computation and Language (cs.CL)
备注: 8 pages, 1 figure
Abstract:We present MudawanSn, a gold-standard resource of 1,271 sentence-aligned pairs manually translated from Wolof into Modern Standard Arabic (MSA). The source texts are drawn from the MasakhaNER corpus and cover politics, society, religion, and sports in Senegalese news discourse. Although multilingual resources such as FLORES-200 and NTREX include both Wolof and Arabic, no publicly available parallel corpus is specifically designed for the Wolof-Modern Standard Arabic language pair. We describe the corpus construction protocol, sentence alignment procedure, and quality-control workflow. We benchmark four machine translation systems spanning three architectural families: NLLB-200 (600M), mT5-base, and two AfriNLLB variants, showing that fine-tuning on MudawanSn yields substantial improvements in both translation directions. The best-performing model, AfriNLLB-12, achieves 7.76 BLEU and 30.72 chrF++ for Wolof-to-Arabic, and 8.75 BLEU and 33.08 chrF++ for Arabic-to-Wolof. The corpus is released under the CC BY-NC license and is publicly available on Hugging Face and GitHub.
[NLP-109] From Pixels to Pairs: A Comprehensive Benchmark of LLM -Based Key-Value Extraction in Noisy Document Settings
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在真实场景下因光学字符识别（Optical Character Recognition, OCR）噪声导致的结构化信息提取性能下降问题。尽管LLMs在高质量文本输入下表现出强大的语义理解能力，但在实际应用中，文档扫描或拍摄产生的OCR错误会显著影响其提取准确性。研究的关键在于构建一个统一的评估框架，系统性地对比开源指令微调LLMs（如Gemma、Mistral、Qwen2.5、LLaMA 3和DeepSeek）在清洁文本与含噪OCR输入下的关键-值对（Key-Value Pair, KVP）提取表现，分离输入质量、模型架构与提示策略的影响。研究发现，模型性能主要受两大因素制约：文本的语义推理能力以及在OCR噪声下保持文本保真度的能力；随着输入噪声增加，模型规模带来的优势逐渐减弱，而OCR质量成为决定性因素。此外，研究揭示了常见的失败模式，包括键值错位、幻觉生成和数值错误。因此，解决方案的核心在于协同优化OCR质量、模型的结构化推理能力与语义建模能力，以弥合实验室评估与真实部署之间的差距。
链接: https://arxiv.org/abs/2609.17538
作者: Zahra Anvari,Vassilis Athitsos
机构: University of Texas at Arlington (德克萨斯大学阿灵顿分校)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages, 20 tables, 5 figures
Abstract:Large language models (LLMs) are increasingly used for structured information extraction from documents, yet their behavior under realistic OCR noise remains poorly understood. We present a systematic benchmark of open-source instruction-tuned LLMs for key-value pair (KVP) extraction under both clean-text and noisy OCR conditions. We evaluate representative decoder-only models (Gemma, Mistral, Qwen2.5, LLaMA 3, and DeepSeek) on the FUNSD, CORD, and SROIE benchmarks using both Gold-text annotations and OCR outputs from PaddleOCR, EasyOCR, and Tesseract. A unified evaluation protocol isolates the effects of input quality, model design, and prompting under consistent conditions. The results show that modern LLMs act as strong semantic extractors when high-quality text is available, in some cases approaching supervised layout-aware systems. Under OCR noise, however, performance degrades substantially and performance gaps between models narrow as input corruption increases. Across all datasets, extraction performance is governed by two factors: semantic reasoning over text and preservation of textual fidelity under OCR noise. While larger models improve results on clean text, these gains diminish under noisy inputs, where OCR quality becomes the dominant factor. We also identify recurring failure modes, including key-value misalignment, hallucination, and numeric corruption. Our findings highlight the gap between clean-text evaluation and real-world deployment, emphasizing the need to jointly improve OCR quality, structural reasoning, and LLM-based semantic modeling. Comments: 25 pages, 20 tables, 5 figures Subjects: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.17538 [cs.CL] (or arXiv:2609.17538v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.17538 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zahra Anvari [view email] [v1] Tue, 14 Jul 2026 02:20:43 UTC (10,830 KB)
[NLP-110] Relation Before Entity: Deferred Commitment in Language Model Factual Recall ICML2026
【速读】： 该论文旨在解决生成式模型在文本生成过程中，关系类型信息（如“首都”）与实体特定信息（如“法国→巴黎”）在最终词元位置是否以相同深度、同步地成为生成控制因素的问题。研究发现，二者在因果激活时间上存在显著的时序不对称性：关系信息在生成过程中比实体信息更早成为主导因素。具体而言，在阈值为0.4时，关系信息的激活先于实体信息10至16层（占网络深度的31%–44%），且这一先后顺序在全部16组模型-阈值组合（阈值范围0.2–0.5）中均保持一致。值得注意的是，实体信息并非早期缺失——在早期层中对实体词元进行替换仍可实现90%–100%的成功率；其关键在于，实体信息虽在实体词元位置已可用，但其对生成过程的控制作用被延迟，需经由网络内部路由至最终词元位置后才真正生效。因此，该研究的核心解决方案在于揭示了生成控制的分阶段机制：关系信息率先主导生成流程，而实体信息则通过延迟的因果承诺机制实现最终输出的确定。
链接: https://arxiv.org/abs/2609.17537
作者: Divyansh Agarwal
机构: 未知
类目: Computation and Language (cs.CL)
备注: 8 pages, 4 figures. Accepted at the Mechanistic Interpretability Workshop at the 43rd International Conference on Machine Learning (ICML 2026). Code available at this https URL
Abstract:We ask whether relation-type information (e.g., capital-of) and entity-specific information (e.g., France to Paris) become causally active at the final-token position at the same depth during recall. Using four complementary causal diagnostics across four decoder-only models and eight prompt families, we find a robust temporal asymmetry: relation information becomes generation-controlling before entity information does. Relation onset precedes entity onset by 10-16 tested layers (31-44% of network depth) at threshold 0.4, with the ordering holding across all 16 model-threshold combinations for thresholds 0.2-0.5. Critically, entity information is not absent early: entity-token patching succeeds at 90-100% in early layers. Instead, entity commitment to generation is deferred: entity information is available at the entity-token position but becomes generation-controlling at the final token only after being routed there.
[NLP-111] hink Before You Comfort: Reflective Cognitive Alignment for Protocol-Grounded Elderly Stimulation Agents
【速读】： 该论文旨在解决认知刺激疗法（Cognitive Stimulation Therapy, CST）在实际应用中面临的可扩展性瓶颈问题，尤其是受限于专业培训人员的依赖性以及针对隐私敏感、低资源语言（如粤语）的数据稀缺性。现有大型语言模型（Large Language Models, LLMs）虽具备自动化陪伴潜力，但在实现共情互动与严格遵循认知刺激规程之间难以取得平衡。为此，论文提出双轴协同的解决方案：首先，通过STaR-CS（风格迁移与角色约束的认知刺激）框架，基于引导者风格建模和结构化对话骨架提取，合成多角色对话数据，有效缓解数据短缺问题；其次，构建反思式认知对齐（Reflective Cognitive Alignment, RCA）框架，将刺激互动建模为序列决策过程，融合协议约束的思维链（Protocol-Constrained Chain-of-Cognition, PC-CoC）以实现结构化推理，并引入推理时价值对齐（Inference-Time Value Alignment, IVA）机制，依据安全性和参与度目标进行响应选择。实验结果表明，RCA在六种主流LLM及两位独立评估者评测下，显著优于标准提示基线，在协议遵循度、安全性与团体引导效果方面均表现更优。
链接: https://arxiv.org/abs/2609.17536
作者: Jiyue Jiang,Ziyi Li,He Hu,Sheng Wang,Yuhan Chen,Yanyu Chen,Jingqi Zhou,Pengan Chen,Fei Ma,Irwin King,Yu Li,Chuan Wu
机构: The Chinese University of Hong Kong; The University of Hong Kong; Guangdong Provincial Laboratory of Artificial Intelligence and Digital Economy (Shenzhen)
类目: Computation and Language (cs.CL)
备注:
Abstract:Cognitive Stimulation Therapy (CST) offers non-pharmacological support for elders with cognitive impairment, yet scalability remains constrained by reliance on trained facilitators and severe data scarcity, particularly for privacy-sensitive, low-resource languages such as Cantonese. While Large Language Models (LLMs) show promise for automated companionship, they often struggle to balance empathetic engagement with adherence to cognitive stimulation guidelines. We propose a framework addressing these challenges along two complementary axes. First, STaR-CS (Style-Transfer and Role-Conditioned Cognitive Stimulation) synthesizes multi-party dialogues through facilitator style modeling and structured skeleton extraction, mitigating data barriers. Building upon this corpus, the Reflective Cognitive Alignment (RCA) framework models stimulation interactions as a sequential decision process, integrating Protocol-Constrained Chain-of-Cognition (PC-CoC) for structured reasoning and Inference-Time Value Alignment (IVA) for principled response selection based on safety and engagement goals. Evaluations across six backbone LLMs and two independent judges show that RCA consistently improves protocol adherence, safety, and group facilitation over standard prompting baselines. Our code is available at this https URL.
[NLP-112] DANTINOX: A Unified Framework for Multi-Paradigm Language Modeling
【速读】： 该论文旨在解决语言生成研究中三大范式——自回归解码（autoregressive decoding）、离散掩码扩散（discrete masked diffusion）与连续流匹配（continuous flow-matching）——因各自独立代码库导致的可比性差的问题。由于不同实现细节的干扰，现有对比往往难以区分范式本身的性能差异。其解决方案的关键在于提出 DantinoX，一个基于 JAX/Flax 的开源库，采用单一模块化 Transformer 主干架构统一支持上述三种生成范式。通过仅需修改配置即可切换生成范式、注意力机制或硬件拓扑，而保持主干结构、分词器、初始化策略及训练基础设施的一致性，实现了在统一 API 下对训练、流式推理和基准测试的受控跨范式比较，从而显著提升了不同生成范式之间的公平评估能力。
链接: https://arxiv.org/abs/2609.17535
作者: Marco Simoni,Aleksandar Fontana,Giulio Rossolini,Andrea Saracino
机构: Scuola Superiore Sant’Anna, Pisa; Department of Excellence in Robotics and AI, TeCIP
类目: Computation and Language (cs.CL)
备注:
Abstract:Language generation research increasingly spans three paradigms: autoregressive decoding, discrete masked diffusion, and continuous flow-matching. Comparing them is difficult because each lives in a separate codebase, so measured differences often reflect implementation details rather than the paradigms themselves. We present DantinoX, an open-source JAX/Flax library in which a single modular Transformer backbone serves all three paradigms. Switching the generation paradigm, attention mechanism, or hardware topology requires only a configuration change, while the backbone architecture, tokenizer, initialization strategy, and training infrastructure remain consistent. This enables controlled cross-paradigm comparisons within one API for training, streaming inference, and benchmarking.
[NLP-113] Faking Good and Faking Bad in LLM s: Response Distortion Across Dark Triad Personality Traits
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在人格评估中是否受到社会期许效应（social desirability）与印象管理（impression management）影响的问题，特别是其在虚假良好（fake-good）和虚假不良（fake-bad）情境下对黑暗三联征特质（Dark Triad traits，包括马基雅维利主义、自恋和精神病态）表达的系统性调节。其解决方案的关键在于采用标准化的心理测量评分方法，在两个生态效度较高的场景——就业选拔与法医评估中，通过情境框架施加社会期望激励，考察模型在不同动机背景下的响应模式。研究发现，多数先进模型在虚假良好条件下显著降低黑暗三联征得分，而在虚假不良条件下则相应提高，且该效应在马基雅维利主义与自恋特质上表现最为一致，精神病态则呈现更大异质性；此外，就业场景引发的响应偏差强于法医场景，且明确指令比隐含情境框架更能诱发显著扭曲。研究强调，必须结合动机与情境背景解读模型的人格输出，并指出心理测量范式在评估模型对响应扭曲、印象管理及情境依赖行为变化的敏感性方面具有重要价值，为大模型基准测试、对齐评估与鲁棒性分析提供了关键方法论启示。
链接: https://arxiv.org/abs/2609.17534
作者: Victoria Popa,Guglielmo Cola,Caterina Senette,Maurizio Tesconi
机构: 未知
类目: Computation and Language (cs.CL)
备注: 21 pages, 7 figures, Journal
Abstract:Social desirability and impression management are pervasive sources of response distortion in human personality assessment, yet their effects on Large Language Models (LLMs) remain underexplored. This study investigates whether contemporary LLMs systematically modulate the expression of Dark Triad traits (Machiavellianism, narcissism, and psychopathy) under fake-good and fake-bad conditions. Seven state-of-the-art models were evaluated across two ecologically relevant contexts: employment selection and forensic evaluation, in which socially desirable or undesirable incentives were conveyed through contextual framing. Trait expression was measured using standard psychometric scoring procedures and compared with self-assessment baselines at both aggregate and item levels. Results revealed systematic and condition-consistent response modulation. Most models reduced Dark Triad scores under fake-good conditions and increased them under fake-bad conditions, although the magnitude and consistency of these effects varied across traits and models. Machiavellianism and narcissism showed the strongest and most coherent shifts, whereas psychopathy displayed greater heterogeneity. Context also influenced responses, with employment scenarios generally producing larger effects than forensic scenarios. An additional experiment showed that explicit fake-bad instructions generated substantially stronger distortions than contextual framing alone. The results suggest that personality-related outputs should be interpreted in light of the motivational and situational context in which they are elicited. More broadly, they highlight the value of psychometric paradigms for evaluating susceptibility to response distortion, impression management, and context-dependent behavioral shifts, with important implications for LLM benchmarking, alignment evaluation, and robustness assessment.
[NLP-114] Enhancing Extubation Failure Prediction with LLM -Derived Features from Respiratory Therapy Clinical Notes
【速读】： 该论文旨在解决机械通气患者及时、安全撤机过程中面临的脱机失败（Extubation Failure, EF）预测难题。现有方法在预测准确性与临床可解释性方面存在局限，尤其受限于对非结构化文本信息的利用不足。其解决方案的关键在于提出一种基于大语言模型（Large Language Model, LLM）与逻辑回归相结合的新型预测框架，通过从自由文本的呼吸治疗记录中自动提取具有临床意义的特征，并将其与结构化患者数据融合，显著提升了EF预测性能。同时，研究揭示了以往研究中因目标人群异质性（如纳入标准不一、脱机失败定义差异）导致模型性能系统性偏差的问题，强调了标准化定义与数据集一致性对提升模型泛化能力的重要性。
链接: https://arxiv.org/abs/2609.17532
作者: Izzy Chaiken,Aditya Khowal,Neha A. Sathe,Mark M. Wurfel,Lucy Lu Wang
机构: University of Washington (华盛顿大学); University of Washington (华盛顿大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Physics and Society (physics.soc-ph)
备注: Published in CHIL 2026. 11 pages, 4 figures, 4 tables, 25 pages including citations and supplemental material
Abstract:Invasive mechanical ventilation is a lifesaving therapy, but timely, safe discontinuation is essential to preventing extubation failure (EF) and related risks to health. We present a novel approach to EF prediction that leverages features classified in free-text respiratory therapy notes using a large language model and logistic regression pipeline. Applied to a patient cohort from University of Washington Medicine, our method identifies clinically meaningful EF-related features that improve EF prediction performance when included alongside structured patient data. We further highlight how differences in target populations in prior EF prediction studies, such as heterogenous inclusion criteria and EF definition, can lead to systematic differences in model performance and hinder generalizability between studies.
[NLP-115] MLLM s Hallucinate when Information Distribution Drifts in Synergy Heads
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）中存在的幻觉问题，尤其是现有基于注意力机制的缓解方法依赖间接信号（如注意力权重），难以准确反映幻觉生成背后的实际信息偏移。其核心解决方案是提出一种名为HEAL（Head-level Information Disentanglement and Calibration）的方法，关键在于通过因果噪声干预筛选出因果冗余的注意力头，并利用反事实双重差分法（counterfactual Difference-in-Differences）对剩余注意力头内的信息分布进行解耦，将其分类为四类。研究发现，幻觉的发生源于协同头（synergy heads）中信息分布偏离健康均衡状态，而非模态特异性头的数量或强度。基于此洞察，HEAL在协同头的值向量中注入动态信息校准因子，主动调节视觉-语言依赖关系，引导输出分布回归至真实证据。实验表明，HEAL可有效降低多种MLLM中的幻觉现象，提供了一种简洁且可解释的提升模型可信度的路径。
链接: https://arxiv.org/abs/2609.09206
作者: Meng’en Qin,Junye Chen,Jucheng Liu,Youlu Xing,Song Wang,Ruize Han
机构: Shenzhen University of Advanced Technology (深圳大学先进技术研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Multimodal Large Language Models (MLLMs) often struggle with hallucinations, thus hindering their reliable practical applications. Existing attention-based mitigation methods mainly rely on indirect signals (e.g., attention weights) that fail to accurately reflect the actual information shift underlying hallucination generation. In this paper, we propose HEAL, Head-lEvel information disentAnglement and caLibration for identifying and mitigating hallucinations. HEAL first employs causal noise intervention on multi-head outputs to filter out causally redundant heads. Subsequently, it disentangles information distribution within the remaining heads via the counterfactual Difference-in-Differences, categorizing heads into four types. Through analysis, we observe: hallucinations happen when information distribution drifts away from a healthy equilibrium in synergy heads, not strongly correlated with the quantity or strength of modality-specific heads. Motivated by this insight, HEAL injects dynamic information calibration factors into the value vectors of synergy heads, and actively regulates visual-language dependencies, steering the output distribution towards factual evidence. Extensive experiments demonstrate that HEAL effectively reduces hallucinations across multiple MLLMs, offering a simple and interpretable pathway to enhance model trustworthiness.
[NLP-116] Variational Quantum Transformer Architecture for Synthetic Language Generation
【速读】： 该论文旨在解决在近中期量子计算（NISQ）设备受限条件下，如何实现高效且可训练的量子自然语言处理（QNLP）序列建模问题。其核心挑战在于如何在有限量子资源下，构建具备类Transformer结构特征的量子模型，同时保持可端到端训练的能力。解决方案的关键在于提出一种紧凑的、兼容NISQ设备的量子变换器架构：将经典Transformer中的注意力与前馈子层替换为变分量子编码块（variational quantum encoder blocks）、连接电路（connector circuits）、解码块（decoder blocks）以及直接的两量子比特测量读出机制；通过角度编码（angle encoding）将词元上下文嵌入小型量子寄存器，利用并行变分头与编码集成电路进行处理，并通过解码辅助量子位（decoder ancillae）实现条件概率分布的生成，最终输出四词元词汇上的概率分布。该架构在确定性和词法顺序语法生成任务上展现出学习复杂语法结构的能力，包括单次运行下的完美确定性生成和强优化变体中的高词法有效性，尽管仍存在对初始化敏感、性能低于经典小规模Transformer基线的问题，但其贡献在于提供了一个在实际量子硬件约束下可实现、可评估的量子变换器启发式序列建模框架，而非宣称量子优势。
链接: https://arxiv.org/abs/2609.18565
作者: Julian Hager,Michael Kölle,Gerhard Stenzel,Tobias Rohe,Jonas Stein,Claudia Linnhoff-Popien
机构: Institute of Informatics, LMU Munich (慕尼黑大学信息学研究所)
类目: Quantum Physics (quant-ph); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted for publication in the QNLPAI 2026 proceedings (Springer Lecture Notes in Computer Science, LNCS). 10 pages, including references and appendix, 2 figures
Abstract:We propose a compact NISQ-compatible quantum transformer architecture for synthetic QNLP sequence modelling. The model preserves the autoregressive next-token interface of a classical transformer, but replaces attention and feed-forward sublayers with variational quantum encoder blocks, connector circuits, decoder blocks and a direct two-qubit measurement readout. Token contexts are angle-encoded into small quantum registers, processed by parallel variational heads and encoder integration circuits and conditioned through decoder ancillae to produce a distribution over a four-token vocabulary. We evaluate several architecture variants on deterministic and lexicographic grammar-generation tasks against a compact classical transformer baseline. The quantum models are trainable end-to-end and learn nontrivial grammar structure, including perfect deterministic generation in individual runs and high lexicographic validity in the strongest variant. The classical baseline remains more accurate and stable and the quantum models are sensitive to initialization. The contribution is therefore not a claim of quantum advantage, but a concrete architecture and evaluation of transformer-inspired QNLP sequence modelling under near-term quantum constraints.
[NLP-117] Correlation-Guided Encoder Selection for Multi-Encoder Large Audio-Language Models
【速读】： 该论文旨在解决多编码器融合（multi-encoder fusion）在大型音频语言模型（Large Audio-Language Models, LALMs）中因依赖直觉或穷举搜索选择编码器所导致的冗余表征学习与计算资源过度消耗问题。其核心解决方案是提出一种轻量级启发式方法CUES（Correlation-gUided Encoder Selection），通过任务级和类别级的皮尔逊相关系数（Pearson correlation）分析各编码器性能表现之间的互补性，仅基于单编码器评估结果即可对候选编码器组合进行评分，无需在选择阶段进行融合训练。实验表明，在XARES-LLM基准测试中，使用冻结的SmolLM2-135M骨干网络（LoRA适配）并采用五折交叉验证，CUES能够仅凭保留的开发集数据稳定识别出每条音频轨迹的最佳配置，且不依赖测试数据。对于涵盖广泛任务的Track A，CUES选出跨家族三编码器组合（Whisper-medium、mHuBERT-147和Dasheng-base），相比单一Whisper-medium实现4.3%相对提升（0.771 vs. 0.739）；而在以文本生成为主的Track B，CUES主动避免引入语义差异过大的编码器，选择聚焦于语音的双编码器组合（mHuBERT-147与WavLM-base-plus），相较mHuBERT-147提升6.3%（0.589 vs. 0.554）。该结果揭示了多样性与干扰之间的权衡关系——在宽泛音频任务中，跨家族多样性呈现倒U型效益，而文本生成任务则更倾向于保持语音锚定的紧凑集合，表明CUES能仅通过相关性信号即有效导航不同任务场景下的最优编码器组合策略。
链接: https://arxiv.org/abs/2609.18041
作者: Pei-Jun Liao,Hung-Shin Lee,Wenze Ren,Kuo-Hsuan Hung,Hung-yi Lee,Hsin-Min Wang
机构: Academia Sinica (中央研究院); National Taiwan University (台湾大学); National Taiwan Normal University (台湾师范大学); Gamania Digital Entertainment Co., Ltd. (游戏橘子数位娱乐有限公司)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted to IEEE SLT 2026
Abstract:Multi-encoder fusion extends Large Audio-Language Models (LALMs) beyond speech-centric recognition, but selecting encoders via intuition or exhaustive search often introduces redundant representations and inflates an already constrained compute budget. We propose CUES (Correlation-gUided Encoder Selection), a lightweight heuristic that estimates complementarity through task- and category-level Pearson correlations between encoders’ performance profiles, scoring a candidate set from single-encoder evaluations alone–without fusion training during selection. Evaluated on the XARES-LLM benchmark with a frozen SmolLM2-135M backbone (LoRA-adapted) via five-fold cross-validation, CUES consistently identifies the same configuration per track from held-out development splits alone, without using test data for selection. For the broad Track~A suite, CUES selects a cross-family trio (Whisper-medium, mHuBERT-147, and Dasheng-base), achieving a 4.3% relative gain over Whisper-medium (0.771 vs. 0.739). For Track~B text generation, it re-anchors on a focused, speech-only pair (mHuBERT-147 and WavLM-base-plus) and actively abstains from adding a divergent encoder, outperforming mHuBERT-147 by 6.3% (0.589 vs. 0.554). Rather than a failure to scale, this divergence is consistent with a diversity–interference trade-off that CUES navigates per track from correlation signals alone: across the evaluated pool, added cross-family diversity tends toward an inverted-U on broad audio tasks but toward steady degradation on text generation, which favors a focused, speech-anchored set.
[NLP-118] G-Mamba: Sparse Graph-Guided Mamba for Audio-Visual Speech Enhancement
【速读】： 该论文旨在解决轻量化音视频语音增强（AVSE）模型在计算效率与跨模态对齐精度之间存在的关键权衡问题。现有方法中，简单的特征拼接缺乏对跨模态关系的表达能力，而密集的交叉注意力机制则带来较高的计算开销，并在强声学干扰下易产生不可靠的跨模态对应。为此，本文提出一种轻量级框架——稀疏异质图引导的Mamba（SG-Mamba），其核心创新在于将稀疏异质图与线性复杂度的Mamba主干网络相结合：稀疏异质图通过内容自适应注意力机制显式建模模态特异性关系及跨帧音视频连接，实现高效且精准的跨模态对齐；Mamba则有效捕捉长时序上下文依赖。此外，引入音频跳跃连接以保留频谱细节，同时不牺牲降噪性能。在LRS3数据集上的实验表明，SG-Mamba在仅3.45 G MACs（或6.90 G FLOPs）的计算成本下，于纯噪声条件下达到13.091 dB的SI-SDR，性能优于或媲美现有轻量级基线模型；在多说话人复杂场景中亦表现出良好鲁棒性。VoxCeleb2上的结果进一步验证了显式结构先验有助于提升模型的鲁棒性、泛化能力与计算效率。
链接: https://arxiv.org/abs/2609.18009
作者: Guo-Ruei Tseng,Hung-Shin Lee,Hsin-Min Wang,Berlin Chen
机构: National Taiwan Normal University (国立台湾师范大学); Academia Sinica (中央研究院)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted to IEEE SLT 2026
Abstract:Lightweight audio-visual speech enhancement (AVSE) models face a critical trade-off between computational efficiency and cross-modal alignment accuracy. While simple concatenation lacks relational expressiveness, dense cross-attention incurs computational overhead and is prone to unreliable cross-modal correspondence under strong acoustic interference. We propose Sparse Graph-Guided Mamba (SG-Mamba), a lightweight AVSE framework that integrates a sparse heterogeneous graph with a linear-complexity Mamba backbone. The graph explicitly models modality-specific relations through content-adaptive attention and cross-frame audio-visual connections, while Mamba captures long-range temporal context. We further introduce an audio skip connection to preserve spectral detail without sacrificing noise suppression. Evaluated on LRS3, SG-Mamba achieves competitive or superior performance against strong lightweight baselines and reaches 13.091 dB SI-SDR under noise-only condition. It also remains robust in cluttered multi-speaker conditions with a competitive cost of 3.45 G MACs (or 6.90 G FLOPs). Results on VoxCeleb2 further suggest that explicit structural priors improve robustness, generalizability, and computational efficiency in lightweight AVSE.
[NLP-119] Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLM s
【速读】： 该论文旨在解决生成式语音大模型（Speech-LLM）在领域偏移语音（如儿童语音、方言语音）上因目标域数据有限而导致的适应性不足问题。现有方法依赖于仅在语言模型（LLM）输出层施加交叉熵损失，导致语音编码器难以充分学习新声学环境下的特征表示。其解决方案的关键在于提出一种名为“通过适配器唤醒编码器”（Encoder Awakening via Adapters, EAVA）的域自适应微调方法：首先，在语音编码器的每一层插入轻量级适配器（adapter），并仅对这些适配器进行训练，从而将目标域声学知识有效注入编码器，同时保留预训练编码器的知识；随后，采用低秩适配器（LoRA）对整个模型进行联合微调，进一步优化端到端性能。实验结果表明，EAVA在涵盖儿童语音和方言语音的三个领域偏移语音识别数据集上均显著优于基线方法，实现了新的最优性能。
链接: https://arxiv.org/abs/2609.17981
作者: Mohan Shi,Zilai Wang,Natarajan Balaji Shankar,Kaiyuan Zhang,Eray Eren,Abeer Alwan
机构: University of California Los Angeles (加州大学洛杉矶分校)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)
备注: Accepted to IEEE SLT 2026
Abstract:Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Rank Adapters (LoRA), have shown strong Automatic Speech Recognition (ASR) performance on general-domain speech. However, adapting them to domain-shifted speech, such as child or dialectal speech, remains challenging under limited target-domain data. Given the dominant role of the LLM in Speech-LLMs, with cross-entropy loss applied only at the LLM output, the speech encoder may receive insufficient adaptation to new acoustic conditions. In this paper, we propose Encoder Awakening via Adapters (EAVA), a simple yet effective domain-adaptive fine-tuning method for Speech-LLM-based ASR. First, lightweight adapters are inserted into each encoder layer and trained exclusively, enabling target-domain acoustic knowledge to be incorporated into the encoder while preserving its pre-trained knowledge. Second, the full model is jointly fine-tuned on the target domain with LoRA applied to the LLM. Experiments on three domain-shifted ASR datasets, covering child and dialectal speech, show that EAVA consistently outperforms vanilla fine-tuning and other baselines, achieving new state-of-the-art performance.
信息检索
[IR-0] SURF: Subtractive Updates for Recommender Forgetting
链接: https://arxiv.org/abs/2609.18695
作者: Filippo Betello,Antonio Purificato,Nicola Tonellotto,Fabrizio Silvestri
类目: Information Retrieval (cs.IR)
备注:
Abstract:The increasing demand for user privacy and compliance with regulations such as GDPR has made machine unlearning a fundamental requirement for modern recommender systems. However, Sequential Recommender Systems (SRS) pose unique challenges for unlearning due to their reliance on temporal interaction patterns. Existing approaches either require computationally prohibitive full retraining or fail to account for the sequential nature of user behavior. We propose SURF (Subtractive Updates for Recommender Forgetting), a lightweight framework for approximate machine unlearning in SRS. SURF operates in three stages: (i) identifying the neighborhood of the item to forget in the embedding space, (ii) training an auxiliary model on this compact local subset, and (iii) subtracting the auxiliary model’s scores from the original model at inference time. Experiments against five baselines on 7 datasets show that SURF achieves unlearning effectiveness comparable to full retraining while substantially reducing computational cost, yielding up to a 32% improvement in NDCG@20 while requiring just 2% of the original retraining baseline time budget. We share our code at this https URL.
[IR-1] SEEK: Secure and Efficient Encrypted Keyword Search For Privacy-Preserving Messaging Protocols
链接: https://arxiv.org/abs/2609.18459
作者: Soumyadyuti Ghosh,Michail Maniatakos
类目: Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
备注:
Abstract:Encrypted communication protects sensitive user data but can facilitate harmful or unlawful exchanges, creating a trade-off between detecting dangerous messages and preserving end-user privacy. To address this, we propose SEEK, a practical and efficient encrypted keyword-search protocol for privacy-preserving messaging that combines homomorphic encryption with secure two-party computation (2PC). SEEK first partitions messages into ciphertext fragments with the minimum sufficient overlap, then homomorphically correlates them using encrypted keyword trapdoors. For long messages, this design can reduce sender-side encryption and upload overhead by up to two orders of magnitude over state-of-the-art baselines. It supports ASCII case-insensitive matching with one fixed-size encrypted trapdoor and one homomorphic multiplication per fragment, yielding up to 5.47x faster correlation computation than the strongest fragmentation-based baselines. SEEK then invokes 2PC-based selected decoding, blinded zero testing, and secure aggregation, revealing only the keyword presence-or-absence bit while hiding the keyword, its length, message contents, match counts, and locations. SEEK achieves 100% accuracy under case variations that result in exact-matching failures, without requiring additional trapdoors or online communication. We further realize SEEK as an end-to-end web and cross-platform mobile application. Prototype evaluation on a weekly messaging history yields an online computation time of 1.92 s per search, demonstrating the practical feasibility and efficiency of SEEK.
[IR-2] Exploring LLM s and RAG for Plausible and Explainable Material Prediction of Vehicle Components
链接: https://arxiv.org/abs/2609.18437
作者: Frederik Wagner,Annerose Eichel,Sabine Schulte im Walde
类目: Information Retrieval (cs.IR)
备注:
Abstract:In this work, we explore whether LLMs can accurately predict and explain plausible materials for vehicle components such as brake discs or fuel injectors without requiring extensive fine-tuning. We test and evaluate three approaches: a standard generative LLM baseline, a single-pass Retrieval-Augmented Generation (RAG) approach, and an iterative Chain-of-Verification (CoVe) variant. For retrieval, we rely on publicly available data using a domain-filtered Wikipedia corpus. Since no gold standard exists for this task, we develop a custom web-based annotation tool supporting crucial functions for structured domain expert evaluation. LLM-based generation substantially outperforms prior work, which is not further surpassed by the tested RAG approaches. Our results surface remaining challenges for RAG-based systems: hyperparameter optimization, the availability of high-quality, legally accessible domain corpora, and expert evaluation study design.
[IR-3] Understanding AI Provider Recommendations in Local Service Markets
链接: https://arxiv.org/abs/2609.18341
作者: Hazem Ibrahim,Yasir Zaki
类目: Computers and Society (cs.CY); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 12 pages, 6 figures
Abstract:When someone asks an AI assistant which doctor to see or which firm to trust with their savings, the answer is a referral. We audit AI provider recommendations in four registry-backed service domains across the 100 largest U.S. metropolitan areas, matching every recommendation against the official registry for its domain (Medicare clinician and facility records, and SEC adviser disclosures), under three conditions: an open-weight model, a proprietary model without web search, and the same proprietary model with search. Without search, both models largely fabricate recommendations in the domains the web covers thinly. Only 4% of the open-weight model’s recommended doctors and 11% of the proprietary model’s match a clinician in the queried city, and the open-weight matches are name coincidences: its matched clinicians are no likelier to be primary-care doctors than names drawn at random from the registry. With search, 64-71% of recommendations in the same domains match a real provider. Search also changes who is recommended. Without it, recommended advisory firms carry SEC misconduct disclosures at 3.6 times the registry base rate, even after adjusting for firm size; with search, significantly below it. Restaurants, where quality and visibility are separately measurable, show a 3-5x review-count premium but a rating premium of at most a tenth of a star. Finally, search largely removes the metro-size penalty: without it, real recommendations concentrate in the largest metros; with it, match rates are similar across metro-size terciles. Whether an AI referral is trustworthy depends strongly on its retrieval configuration rather than on the underlying model alone, yet an answer produced without retrieval often carries no sign that its recommendations were never verified.
[IR-4] One-Step Retrieval Framework for Real-Time Sponsored Search Ads Using Hierarchical Text Representations
链接: https://arxiv.org/abs/2609.18296
作者: Tongtong Liu,Renyu Zhang,Jiayu Ding,Hongchao Guo,Xintao Yang,He Wei,Zhaoyu Li,Haiyang Wu
类目: Information Retrieval (cs.IR)
备注:
Abstract:Traditional retrieval systems typically use multi-stage cascading architectures (MCA), where each module is optimized independently, leading to inconsistent objectives and the premature elimination of high-potential candidates. Recent LLM-based generation methods offer end-to-end solutions but use discrete semantic identifiers (SIDs) to retrieve ads, which are not learned by the base LLM and require memorization of numerous SID-to-ad mappings during SFT, suffering from limited generalization to unseen ads, high maintenance and update costs. The one-to-one mapping between SIDs and advertisements leads to inefficient decoding. Moreover, these methods rely on a small reward model (e.g. pctr) for relevance and ranking, limiting the LLM’s ability to fully assess ads’ commercial value. To address these challenges, we propose A uNified Generation-discriminative-ranking reaL-time rEtrieval (ANGLE) framework. ANGLE uses LLM-generated hierarchical textual representations, which consist of commercial intent that provide high-level overviews and ad abstract that deliver fine-grained details. Additionally, ANGLE integrates retrieval, relevance, and ranking directly within a single LLM, enabling precise and efficient ranking of ads by leveraging the full capabilities of the LLM. We applied ANGLE to the real-world search scenarios, achieving a 1.81% increase in consumption and a 2.16% increase in gross merchandise volume (GMV). We also conducted offline evaluations of ANGLE and seven baselines, with ANGLE outperforming all across key metrics such as HR and ACR.
[IR-5] Quanta: A Self-Contained Python Library for Hybrid Retrieval over Quantised Embeddings Lexical Indexes and Knowledge Graphs
链接: https://arxiv.org/abs/2609.18248
作者: Ioannis E. Livieris
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:An advanced retrieval-augmented generation pipeline is typically assembled from three or four independently operated systems: an approximate nearest-neighbour index, a full-text search engine, a graph database, and a relational document store. Each contributes its own deployment surface, configuration model, and failure modes, and the integration logic that binds them is written anew in every project. In this work, we present \textscQuanta, an open-source Python library, which unifies dense vector search over 4-bit quantised embeddings, BM25 full-text retrieval, and knowledge-graph traversal behind a single retrieval API. Quanta makes two design commitments, which distinguish it from existing hybrid retrieval stacks. First, signals are combined by \emphweighted reciprocal rank fusion rather than by normalising heterogeneous scores onto a shared range, which we argue is ill-posed because such normalisations are query-dependent. Second, the graph is a \emphcandidate expander and not a relevance scorer: traversal widens the candidate pool, and the newly admitted documents are re-scored by the dense indexes under an identifier allowlist, so structural adjacency determines what is considered while content evidence determines how it ranks.
[IR-6] Single-Token Expected-Value Scoring for Cold-Start Candidate Ranking RECSYS RECSYS2026
链接: https://arxiv.org/abs/2609.18188
作者: Qihang Wang,Jinwei Tan,Mengyuan Shi,Mayank Sharma,Shuai Zhao,Fuxian Li,Ryan Yan,Alexander P. Kreuzer,Mohit Jain,Dheeraj Toshniwal,Manoj Seethamsetty(Indeed Inc.)
类目: Information Retrieval (cs.IR)
备注: 10 pages, 7 figures. Accepted at RecSys in HR '26: The 6th Workshop on Recommender Systems for Human Resources, in conjunction with the 20th ACM Conference on Recommender Systems (RecSys 2026), September 28 - October 2, 2026, Minneapolis, MN, USA. To appear in CEUR Workshop Proceedings
Abstract:AI-assisted sourcing streamlines candidate review, reducing the administrative burden of manual screening for recruiters. However, deploying language models as production rankers remains challenging. Zero-shot Large Language Models (LLMs) may produce unstable, non-deterministic scores and rank less accurately, while conventional deep neural rankers require millions of logged interactions that a low-traffic, niche sourcing platform does not produce. What is available instead is a few hundred thousand ordinal relevance labels – small by ranker-training standards, but sufficient when a pretrained language model already encodes the general world knowledge the task depends on. We present single-token expected-value scoring, a ranking primitive that casts candidate-job relevance as an ordinal classification over the grade tokens 1, …, 5 and reads the relevance score as the expectation of the first-token probability distribution. Because the score comes from a single decoding step rather than open-ended generation, it is a deterministic function of the model’s logits, requires no output parsing, and serves at low latency. To learn the non-linear interdependencies of heterogeneous hiring criteria from this supervision alone, we fine-tune a Small Language Model (SLM) with a hybrid ordinal regression loss combining a Mean Squared Error term, which preserves ordinal distance, with a categorical Cross-Entropy term, which sharpens class boundaries. We evaluate along two dimensions – Jobseeker Relevance and Employer Relevance – using NDCG@10 and low relevance rate. Offline, our fine-tuned model outperforms a heuristic baseline and zero-shot LLMs. An end-to-end simulation shows the same direction at larger magnitude (+54.2% Jobseeker NDCG@10, -46.7% low relevance rate), and a live online experiment reduces employer low-relevance by 27.3% and raises employer keep rate by 7.07%. Comments: 10 pages, 7 figures. Accepted at RecSys in HR '26: The 6th Workshop on Recommender Systems for Human Resources, in conjunction with the 20th ACM Conference on Recommender Systems (RecSys 2026), September 28 - October 2, 2026, Minneapolis, MN, USA. To appear in CEUR Workshop Proceedings Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2609.18188 [cs.IR] (or arXiv:2609.18188v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.18188 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-7] me-Aligned Evolving Concept Graphs for Scientific Relation Forecasting
链接: https://arxiv.org/abs/2609.18163
作者: Fred Sun,Jingze Wang,Minkun Xu,Shangqi Guo
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Forecasting scientific relations can guide discovery by identifying promising connections before they emerge. Existing approaches often model concept semantics and graph structure separately or summarize semantics over coarse historical snapshots, leaving semantic representations potentially misaligned with rapidly evolving graph evidence. We propose a time-aligned evolving concept graph framework that jointly models semantic and structural evolution. Its core idea is to treat dated papers as shared update events, reconstructing semantic and structural states from the same publication history through each prediction time. Pair-level fusion combines these states to forecast first co-occurrence, relation formation, and conditional relation type. Holding architecture and training fixed, refreshing context alongside graph updates improves mean relation AUPRC by 16.6% over frozen context. On a graph built from 187,848 papers with 270,687 concepts and 7.45 million co-occurrence links, the complete framework improves mean relation AUROC from 0.9290 for the strongest evaluated baseline to 0.9722, with mean population-weighted AUPRC 0.005778.
[IR-8] PageRecall: Measuring Page Selection in Literature-Grounded Question Answering EMNLP2026
链接: https://arxiv.org/abs/2609.18154
作者: Aaditya Chauhan
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: Accepted at the 1st Workshop on Grounding Language Models (GroundLM 2026), co-located with EMNLP 2026. 9 pages. System description for the LitTraceQA shared task (team Everest)
Abstract:We describe our system for LitTraceQA (GroundLM @ EMNLP 2026): given a research question, retrieve the relevant papers from a pool of 27,487, cite the page and the table or figure where the answer lives, and answer in a requested format. Our main finding is that evidence grounding is limited by retrieval, not by reading. The page selector put the annotator’s page, which we call the gold page, in front of the model that locates evidence only about half the time (52.6% gold-page recall), while that model, given the page, cited the right one in 45 of the 48 locators it emitted (94%). When the page was missing it rarely said so: of 45 such cases it returned nothing 14 times, a wrong page 24 times, and a correct page 7 times, so the pipeline failed quietly almost twice as often as it failed visibly. Since the failure was that the right page was never shown, the fix is to stop choosing: each retrieved paper fits in the model’s context, so we show it whole. Page ranking survives only as a fallback inside papers too long to fit, which no test-split paper was, and gold-page recall reaches 100% on the papers we can parse. Separately, questions that identify their target by position rather than content, such as “the first author of the 24th reference”, are served by parsing rather than retrieval: we resolve the bibliography into an addressable list, which also supplies identifiers the evidence metric scores. The final system scores 0.762 paper F_1 , 0.441 evidence F_1 and 0.920 multiple-choice accuracy on the held-out test split. Because the pipeline depends on a closed model without seed control, we release a harness that verifies the paper’s central claims against committed artifacts.
[IR-9] LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era
链接: https://arxiv.org/abs/2609.18148
作者: Venkat Srinivas,Chenzhang He,Sam Woodmansee,Shawn Lian,Wenjie Hu,Renjie Jiang,Ziheng Huang,Xinyuan Zhang,Zhihao Zheng,Zhuoran Yu,Rui Li,Lei Yuan,Ziwei Li,Jimmy Jia,Mert Terzihan,Ekrem Kocaguneli,Yiming Liao,Zhichen Zhao,Yue Yin,Yue Weng,Wanlin Ma,Xufeng Cai,Weimiao Wu,Yezhou Huang,Du Zhang,Yukun Ding,Aaron Johnston,Yueming Wang,Zhaojie Gong,Yuting Zhang,Serena Li,Adithya Ganesh,Boying Liu,Haichuan Yang,Xialu Li,Matt Ma,Qunshu Zhang,John Joshua Miller,Praveen Rathinavelu,Cheng Huang,Aadhar Sachdeva,Josh Karns,Andres Aaron Gutierrez,Neil Agarwal,Gustas Pladis,Vladimir Batygin,Gopal Ray,Aditya Priyadarshi,Shantanu Patil,Zhe Wang,Penny Pan,Yiping Han,Arun Singh,Guangdeng Liao,Bi Xue,Xinyao Hu,Yang Song,Yisong Song,Meihong Wang,Haotian Wu,Deepak Agarwal,Ji Liu
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注:
Abstract:The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and language generation share a similarity: both aim to produce an ordered sequence that optimizes the user’s experience. However, how to precisely absorb the essence of the LLM paradigm into mature industrial recommender systems remains an open problem. There are two challenges. First, it is unclear how to incorporate sequence-level generation and optimization from the LLM paradigm into recommendation. Second, real-world recommender systems are mature systems that have been iteratively customized for years around specific products, business constraints, serving infrastructure, and organizational ownership. Replacing such systems wholesale is often technically risky and organizationally disruptive. In this paper, we propose LIGE-GR, a listwise generation and evaluation recommendation framework that upgrades from a traditional ranking system based on itemwise recommendation toward a generative recommendation paradigm. Instead of rebuilding the entire recommendation stack from scratch, LIGE-GR generalizes the existing pointwise recommendation system into a listwise generation system. This allows mature recommender systems to benefit from listwise optimization while preserving compatibility with existing models, value functions, and serving infrastructure. We validate LIGE-GR in short-video recommendation on Instagram Reels and Facebook Video. On these recommendation surfaces, LIGE-GR improves time spent by 1.14 percent on Instagram Reels and 0.72 percent on Facebook Video, while requiring only modest additional inference resources. Subjects: Machine Learning (cs.LG); Information Retrieval (cs.IR) Cite as: arXiv:2609.18148 [cs.LG] (or arXiv:2609.18148v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.18148 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shawn Lian [view email] [v1] Wed, 16 Sep 2026 05:28:52 UTC (6,965 KB)
[IR-10] DUPAR: Dual-Path Conversational Retrieval via Speech Retriever with Cross-Turn Evidence Caching
链接: https://arxiv.org/abs/2609.18042
作者: Yuanjun Li,Yiwen Liu,Dapeng Li,Zhiwei Xu,Bin Zhang,Shengtao Zhang,Rong Shen
类目: Information Retrieval (cs.IR)
备注: 5 pages, 4 figures
Abstract:Voice assistants grounded in external knowledge typically use automatic speech recognition (ASR) to transcribe speech queries before retrieving evidence from textual knowledge bases. This cascade adds latency and propagates recognition errors, whereas direct speech retrieval is vulnerable to cross-modal misalignment. To address these limitations, we propose DUPAR, a conversational retrieval framework with complementary slow and fast paths. The fast path uses a task-adapted audio encoder aligned with frozen BGE-M3 text embeddings to search a cross-turn evidence cache. When cache confidence is insufficient, the slow path fuses full-index retrieval using audio and ASR-transcript embeddings, and the selected evidence refreshes the next-turn evidence cache through one-hop graph expansion. On a domain-specific knowledge base, our trained audio encoder approaches text-retrieval accuracy on clean speech with a 3.75 \times query-side speedup over ASR + Text Encoder. It raises average Recall@10 from 0.771 to 0.875 on the noise benchmark and improves overall Recall@1 by 4.2 percentage points across synthesized speaking styles. Compared with full-index audio retrieval, cross-turn evidence caching significantly reduces retrieval errors when the previous turn retrieves correct evidence and the follow-up targets a one-hop neighboring chunk.
[IR-11] How Calibration Content Shapes Attention-Based Reranking
链接: https://arxiv.org/abs/2609.17764
作者: Petros Karypis,Hossein Rajaby Faghihi,Peter Chen,Rui Zhu,Noveen Sachdeva,Yan Zhu,Julian McAuley
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 16 pages, 6 figures, 10 tables
Abstract:Attention-based rerankers score documents by aggregating query-to-document attention and subtracting a null-query calibration pass to remove positional and structural bias. Although widely used, this calibration assumes that the null pass removes irrelevant signal from each document. We show that modern prompt content, e.g. constraints, instructions, personas, and demonstrations can violate this assumption when it enters the scoring readout, making the null pass relevance-aware rather than null. We find that calibration is especially harmful when applied to prompts containing longer, more detailed instructions as the null-pass step removes relevant signal. Based on these findings, we propose interpolated null calibration, a training-free modification that controls how much of the instruction content enters the null baseline. It recovers attention-based reranking performance on instruction-heavy tasks where standard calibration fails, while preserving calibration’s benefits when the null pass remains relevance-agnostic. On instruction heavy tasks, the recovered rankings surpass generative rerankers. We also show that in-context demonstrations improve attention-based reranking with little calibration interference, since demonstrations act only through the query pass and leave the null pass unchanged.
[IR-12] One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG
链接: https://arxiv.org/abs/2609.17709
作者: Neeraj Anand,Payel Santra,Partha Basuchowdhuri,Debasis Ganguly,Sumit Bhatia
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differences in query complexity and information needs, leading to inefficient allocation of computational resources. While retrieval and generation adaptivity have been studied independently, their joint effect on end-to-end RAG performance remains underexplored. We systematically analyze how retriever and generator complexity interacts across factoid and multi-hop question answering (QA), including bridge and composition reasoning tasks. Our analysis shows that stronger retrieval generally yields larger gains than increased generation effort, but both exhibit diminishing and non-monotonic returns, indicating that higher-complexity configurations are not uniformly better across queries. Motivated by these findings, we introduce DRAG, a query-adaptive framework for selecting retriever-generator configurations. We first propose DRAG _\textQPP , a training-free routing approach that uses Query Performance Prediction (QPP) signals to guide retriever selection and perplexity-based measures over retrieved context to guide generator selection. We further introduce DRAG _\textSFT , a supervised routing approach that fine-tunes an LLM to jointly predict retriever-generator configurations. Across three LLM families and four QA benchmarks, \qpprag~achieves performance comparable to strong static RAG baselines while substantially reducing inference latency, whereas DRAG _\textSFT consistently improves effectiveness over static and training-free adaptive baselines. Overall, DRAG demonstrates that jointly adapting retrieval and generation achieves a more favorable effectiveness-efficiency trade-off than static RAG pipelines.
[IR-13] Scaling Articulated Rationales for MLLM -based Recommendation
链接: https://arxiv.org/abs/2609.17639
作者: Haoke Xiao,Yueyang Liu,Yuhui Zhang,Xiang Chen,Yufei Liu,Jia Xu,Yalong Guan,Xiaolan Zhu,Xiaoyu Zhang,Shijun Wang,Shuang Yang,Zijie Meng,Zejian Zhang,Ruochen Yang,Xiangyu Wu,Tingting Gao,Han Li,Lantao Hu,Cheng Luo,Kun Gai
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Modern recommendation systems largely infer user preferences from implicit behaviors such as clicks, watch time, and negative feedback, but these signals reveal what users do rather than why they like or dislike content. This work studies articulated user rationales (AURs), i.e., users’ natural-language explanations of their preferences, as a new class of polarity-aware and reason-level textual signals for recommendation. Despite their potential value, AURs are difficult to use in industrial systems because they are naturally sparse, often low-quality, and only cover a small fraction of items. We present SARA (Scaling Articulated Rationales), an industrial framework that turns sparse AURs into scalable recommendation signals. SARA first builds a data engine that elicits and curates AURs from 240M Kuaishou Live users, producing SARA-HQ, a quality-controlled and author-centric rationale dataset. It then aligns a general-purpose MLLM into SARA-7B through large-scale SFT and Quality-Refining DPO, extending rationale generation from 86,564 AUR-covered authors to the full 10M-author space. Finally, SARA-Ranker integrates the generated positive and negative rationales into production ranking via rationale-aware interaction modeling and rejection-memory modeling. Extensive offline evaluation, human calibration, and online A/B tests show that SARA-7B generates more specific, polarity-consistent, and grounded rationales than strong MLLM baselines, while SARA-Ranker improves engagement and reduces negative feedback in production. Deployed with daily refresh for over 30 days, SARA establishes articulated rationales as a practical, first-class textual signal for industrial recommendation systems.
[IR-14] Beyond Static RAG : An Adaptive Tri-Metric Routing Framework for Efficient Long-Context Inference on Commodity GPUs ICML-2026 ICML
链接: https://arxiv.org/abs/2609.17564
作者: Saipraveen Vabbilisetty,Ajay Kumar Boddepalli,Deep Narayan Mishra,Shashank Kapadia,Haoan Wang,Anupriya Sharma
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: This Paper is accepted and presented at ICML Scale Workshop 2026. this https URL (Paper ID :72)
Abstract:Deploying retrieval-augmented generation (RAG) on commodity GPUs such as the NVIDIA T4 (16 GB VRAM) exposes a practical failure mode we call the Compression Paradox: neural prompt compression can add key-value (KV) cache contention and preprocessing latency that outweigh generation-time savings, while skipping compression can cause out-of-memory (OOM) failures on long contexts. We identify two distinct failure mechanisms when a vLLM-served LLM and a PyTorch-based compressor are co-deployed under tight memory budgets, and introduce the Tri-Metric Router, a deterministic, training-free policy that selects among Raw, Neural (LLMLingua-2), and Lexical (BM25) pipelines. The router uses three CPU-side signals: spatial complexity ( L ), syntactic density ( \rho_key ), and type-token ratio (TTR). Unlike prior semantic-only adaptation, our dispatch signal is hardware-physical, based on VRAM headroom and a latency crossover point. Thresholds are calibrated from profiling on LongBench qasper, yielding an operating crossover near 4,332 words on T4; our contribution is this calibration methodology rather than a hardware-specific constant. On out-of-distribution holdouts, the method achieves 0% OOM failures, 88.5 \pm 4.4% oracle alignment, and 49.3% Combined F1, improving over always-on lexical compression by 5.2 points without additional VRAM or training cost.
[IR-15] On Predicting Post-Click Conversion Rate via Counterfactual Inference ICDM
链接: https://arxiv.org/abs/2510.04816
作者: Junhyung Ahn,Sanghack Lee
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: This work has been accepted for publication at the IEEE International Conference on Data Mining (ICDM) 2025
Abstract:Accurately predicting conversion rate (CVR) is essential in various recommendation domains such as online advertising systems and e-commerce. These systems utilize user interaction logs, which consist of exposures, clicks, and conversions. CVR prediction models are typically trained solely based on clicked samples, as conversions can only be determined following clicks. However, the sparsity of clicked instances necessitates the collection of a substantial amount of logs for effective model training. Recent works address this issue by devising frameworks that leverage non-clicked samples. While these frameworks aim to reduce biases caused by the discrepancy between clicked and non-clicked samples, they often rely on heuristics. Against this background, we propose a method to counterfactually generate conversion labels for non-clicked samples by using causality as a guiding principle, attempting to answer the question, “Would the user have converted if he or she had clicked the recommended item?” Our approach is named the Entire Space Counterfactual Inference Multi-task Model (ESCIM). We initially train a structural causal model (SCM) of user sequential behaviors and conduct a hypothetical intervention (i.e., click) on non-clicked items to infer counterfactual CVRs. We then introduce several approaches to transform predicted counterfactual CVRs into binary counterfactual conversion labels for the non-clicked samples. Finally, the generated samples are incorporated into the training process. Extensive experiments on public datasets illustrate the superiority of the proposed algorithm. Online A/B testing further empirically validates the effectiveness of our proposed algorithm in real-world scenarios. In addition, we demonstrate the improved performance of the proposed method on latent conversion data, showcasing its robustness and superior generalization capabilities.
[IR-16] he Death of Schema Linking? Text-to-SQL in the Age of Well-Reason ed Language Models
链接: https://arxiv.org/abs/2408.07702
作者: Karime Maamari,Fadhil Abubaker,Daniel Jaroslawicz,Amine Mhedhbi
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Schema linking is a crucial step in Text-to-SQL pipelines. Its goal is to retrieve the relevant tables and columns of a target database for a user’s query while disregarding irrelevant ones. However, imperfect schema linking can often exclude required columns needed for accurate query generation. In this work, we revisit schema linking when using the latest generation of large language models (LLMs). We find empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones. As such, our Text-to-SQL pipeline entirely forgoes schema linking in cases where the schema fits within the model’s context window in order to minimize issues due to filtering required schema elements. Furthermore, instead of filtering contextual information, we highlight techniques such as augmentation, selection, and correction, and adopt them to improve the accuracy of our Text-to-SQL pipeline. Our approach ranks first on the BIRD benchmark achieving an accuracy of 71.83%.
人机交互
[HC-0] EarStreAM: A Closed-Loop Earable System for Personalized Stress-Adaptive Meditation
链接: https://arxiv.org/abs/2609.19127
作者: Jonas Hummel,Luisa Faust,Elias Müller,Eva Bertog,Valeria Zitz,Marius Johannes Prill,Luca L. Bennardo,Luisa Weber,Tobias Röddiger,Michael Beigl
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:We present EarStreAM, a closed-loop earable system for stress-adaptive meditation that integrates in-ear physiological sensing with personalized, real-time intervention. Leveraging OpenEarable 2.0’s multimodal sensing, EarStreAM continuously monitors physiological signals and detects elevated stress from heart rate and heart rate variability. Upon detection, the system initiates a personalized guided meditation generated by an LLM and adapted in real time to the user’s stress state. The demo offers a hands-on experience of stress-adaptive meditation in two modes: a biosignal-adaptive meditation with optional stress induction to illustrate closed-loop adaptation, and a meditation-only mode focusing on EarStreAM’s generative personalization capabilities. The demo highlights how in-ear sensing, closed-loop adaptation, and personalized generative meditation can be integrated into an earable system for real-time stress support in demanding office work contexts.
[HC-1] Affora: A Design System for Agent -Friendly Interfaces
链接: https://arxiv.org/abs/2609.19125
作者: Jin Gao
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 20 pages, 8 figures
Abstract:Computer-use agents increasingly operate software designed for people, but interfaces often leave actions or task state unclear to machine readers. We present Affora, a design system that supports both readers while preserving visual freedom and familiar human workflows. Three controlled studies examine component implementations, visual variation, and interaction-design principles. Their findings inform guidance from individual components to complete sites, supported by reusable implementations and executable checks. Agent performance depends on the interaction meaning available through its interface representation; substantial visual variation remains possible when that meaning is preserved. Evaluation on independently authored interfaces shows gains where Affora addresses existing deficits, but limited effects where those deficits are absent or outside its coverage. A workflow case provides preliminary evidence of reduced interaction cost. Affora connects user experience and agent experience through a shared interface rather than a separate agent-only surface.
[HC-2] Entropy in Conversational AI: Structured Unpredictability as Inferrable Interiority
链接: https://arxiv.org/abs/2609.19044
作者: Sebastian Cochinescu
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 18 pages, 5 figures, 6 tables. Ancillary files contain the complete artifact: code, frozen protocol, per-sequence and per-item outcome files, and deterministic analysis scripts. Code archive: this https URL Final-grid pre-registration: this https URL
Abstract:Sampling can increase response diversity without producing history-dependent behavior. We formalize a different design target, structured unpredictability, as conditional dependence between an output and a persistent hidden state beyond what an observer can infer from the transcript. A selection layer updates a low-dimensional style-and-attention state from a capacity-limited stream, generates several responses with a fixed base model, and selects for novelty and state affinity. Evaluation uses scripted sequences of independent prompt turns: the base model receives the current turn and rendered state, but not the preceding dialogue; cross-turn dependence resides in the wrapper state and response selector. A synthetic implementation validates the pipeline and matches four prospectively hash-frozen divergence features at point level. In the final real-model grid (mlx-community/Qwen2.5-1.5B-Instruct-4bit; 56 sequences per arm), the mechanism increased lexical novelty over the low-variance and consistency-only controls by 0.073 and 0.023, respectively. Its stylometric-consistency contrast with novelty-matched sampling was equivalent to zero under the registered smallest-effect rule, so the joint novelty-consistency criterion failed. The original two-part accumulation criterion also failed; a revised final-grid contrast, frozen after the powered grid, found higher consistency than the memory-reset ablation (0.028, 95% CI [0.018,0.039]), but does not establish path dependence. Twin separation was not established (0.003, 95% CI [-0.011,0.019]); the mean curve’s saturating curvature matched the frozen prediction, which without separation does not support path dependence. Probe-level capability equivalence held within +/-0.10 on a near-ceiling battery, while output quality was not evaluated. All outcomes are machine-scored; no claims about perceived mind or consciousness are tested.
[HC-3] Examining the Difference in Human Behavior Between Virtual and Real-World Human-Robot Teaming IROS2026
链接: https://arxiv.org/abs/2609.18900
作者: Sean Dallas,Absalat Getachew,Motaz AbuHijleh,Andrea Macklem-Zabel,Douglas Zytko,Mark Brudnak,Wing-Yue Geoffrey Louie
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: 8 pages, 2 figures, 1 table, IROS 2026
Abstract:Prototyping and evaluating human-robot teaming (HRT) scenarios in the real-world is costly. Virtual simulation of HRT scenarios has been adopted as an alternative to conducting user studies in the real-world to investigate user perceptions, behaviors, and performance during human-robot interactions. The consistency of human behavior between the real and virtual-worlds is integral to the validity of utilizing such virtual experimentation. This paper presents a user study examining the difference in human behavior between an HRT scenario conducted in a virtual vs real environment. We employed mixed-methods to examine team performance and human factors during the HRT. Our quantitative results showed a significant difference in workload between the two modalities. Our qualitative analysis expanded on the quantitative results and found differences in participants’ strategy, their mental model of robots, and the type of trust they had for robots between modalities.
[HC-4] Calmables: Demonstrating Closed-Loop Infrared Earables for Thermal Biofeedback and Relaxation Support
链接: https://arxiv.org/abs/2609.18726
作者: Valeria Zitz,Michael Küttner,Jonas Hummel,Taku Inoue,Tobias Rödiger,Michael Beigl
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:We present Calmables, a walk-up demo of a closed-loop infrared earable that uses smart-ring heart rate to create subtle, ear-localized warming cues. Building on work on thermal comfort and in-ear infrared stimulation, Calmables explores warmth at the ear as a biosignal-adaptive cue for brief recovery moments following acute activation. A smartphone first establishes an individual resting baseline and derives a personalized heart-rate threshold, while the earable controller independently enforces an over-temperature cut-off and communication fail-safes. During the guided demo flow, attendees complete a brief rapid-breathing activation phase until their heart rate reaches the personalized threshold. This triggers an ear-localized warming cue followed by a short relaxation phase, while physiological changes are displayed on a live dashboard. Attendees can also manually explore different infrared stimulation intensities. To contextualize the demo, we report preliminary placebo-controlled UX ratings from 18 participants: participants rated the active prototype higher on perceived relaxation and perceived recovery support than an identical-looking placebo. Together, the demo illustrates how infrared earables can make physiological feedback tangible through subtle, biosignal-adaptive thermal cues.
[HC-5] “Okay Ive Actually Softened My Take on This”: How People in Decentralized Social Media Reason about the Appropriateness of Generative AI
链接: https://arxiv.org/abs/2609.18709
作者: Romina Mahinpei,Manoel Horta Ribeiro,Andrés Monroy-Hernández,Sohyeon Hwang
类目: Human-Computer Interaction (cs.HC)
备注: 26 pages, 2 figures, 3 tables
Abstract:Generative AI (GenAI) is increasingly integrated into social media, raising questions about whether, where, and how it belongs. In decentralized social media (DSM), these decisions are distributed across users, developers, moderators, and administrators, making GenAI a collective governance challenge. At the same time, public discourse often flattens arguments to broad pro- or anti-AI positions that offer little insight into what people actually find (in)appropriate and why. Through 20 semi-structured interviews with people from Mastodon and Bluesky, structured around seven GenAI scenarios, we examine how people reason about GenAI’s appropriateness in DSM. We find that participants drew conditional boundaries around particular GenAI configurations through distinct, salient, and weighted considerations spanning technology, integration, and use. We conceptualize this as boundary drawing and show how making such boundaries visible can support more grounded design, policy, and collective deliberation around GenAI in DSM.
[HC-6] RankGround: Efficient High-Resolution GUI Grounding via Lightweight Reranker-Guided Crop Selection
链接: https://arxiv.org/abs/2609.18690
作者: Liyang Fan,Xinping Bi,Yitai Li,Shuaimin Li,Hui Li,Min Yang
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 10 pages, 6 figures. Accepted to ACM Multimedia 2026 (MM '26)
Abstract:Graphical User Interface (GUI) grounding is a fundamental perception task for multimodal agents, enabling them to interpret natural language instructions and interact with digital interfaces. Existing methods face a fundamental trade-off between accuracy and efficiency: direct full-image inference often fails to capture small or visually similar UI elements, while multi-crop strategies improve localization at the cost of multiple expensive Vision-Language Model (VLM) calls per query. To address this challenge, we propose RankGround, a two-stage framework that achieves accurate GUI grounding with a single VLM call per query. Central to our approach is GroundRanker, a lightweight multimodal reranker that identifies the most promising crop from a dense candidate set. Because no off-the-shelf ranking dataset is available, we construct ranking supervision data from existing grounding datasets. A strict containment criterion and boundary-aware positive augmentation improve alignment and spatial coverage in cluttered layouts. GroundRanker is then trained with a two-stage curriculum: a pointwise objective first learns coarse containment, and a listwise objective refines subtle semantic and spatial distinctions among visually similar crops. Experimental results show that RankGround consistently outperforms strong baselines while reducing computational cost. It achieves 1.4 times faster inference and improves localization accuracy by 5.5% on average over the second-best method across all backbones and screen scales, establishing a new state of the art in both efficiency and precision for GUI grounding. Comments: 10 pages, 6 figures. Accepted to ACM Multimedia 2026 (MM '26) Subjects: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) Cite as: arXiv:2609.18690 [cs.CV] (or arXiv:2609.18690v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.18690 Focus to learn more arXiv-issued DOI via DataCite Related DOI: https://doi.org/10.1145/3767308.3836574 Focus to learn more DOI(s) linking to related resources
[HC-7] Integrating Flipped Learning and Generative AI for Practice-Based Design Education: Evidence from a Knit Yarn Design Course
链接: https://arxiv.org/abs/2609.18505
作者: Hong Qu,Zichao Ling,Yadie Yang
类目: Human-Computer Interaction (cs.HC)
备注: 31 pages, 12 figures (including 3 appendix figures)
Abstract:In practice-based design courses such as knit yarn design, students must turn visual ideas into feasible material outcomes. This is difficult because creative decisions are tied to yarn properties, stitch structures, machine operation, and limited opportunities for physical sampling. This study presents an integrated pedagogical framework that combines flipped learning, exemplar-based reference, GenAI-assisted visual prototyping, and studio feedback in an undergraduate knit yarn design course. The framework was implemented through a cross-device platform with pre-class micro-videos, formative checks, a curated gallery, and a GenAI-supported ideation module. An exploratory course-based evaluation compared a historical control cohort (N = 12) and an intervention cohort (N = 16), supplemented by questionnaire responses and brief interviews. The findings are interpreted as context-specific indicators rather than confirmatory causal evidence. Exploratory comparisons showed higher scores in creativity thinking, design skills, problem solving, and total course score in the intervention cohort. Student and instructor responses suggested that flipped learning supported studio readiness, while GenAI mainly supported early-stage visual exploration rather than precise technical guidance. Overall, the study offers a practice-based instructional framework for integrating flipped preparation, GenAI-assisted visual prototyping, and studio feedback in design education.
[HC-8] EasyFashion: A Human-AI Co-Creation System for Personalized Fashion Design and Sewing Pattern Generation
链接: https://arxiv.org/abs/2609.18483
作者: Hong Qu,Zhaoxiang Xu,Jinbo Luo,Yujie Zhao,Jie Zhang,Yadie Yang
类目: Human-Computer Interaction (cs.HC)
备注: 29 pages, 15 figures
Abstract:People often want garments that reflect their aesthetic preferences, fit their bodies, and meet their sizing needs, yet turning these requirements into physical garments remains difficult. Ready-to-wear options provide limited personalization, while custom tailoring is costly and time-consuming. Recent generative artificial intelligence (AI) systems can visualize garment ideas but often stop short of supporting downstream production. To address this gap, we present EasyFashion, a human-AI co-creation system that enables users to iteratively refine design intent for personalized garment style and size, evaluate designs through virtual try-on on reconstructed personal avatars, and generate sewing patterns for garment production. Using reference images, text descriptions, and body photos as input, EasyFashion translates user intent into structured garment specifications and try-on results. Technical experiments, user studies, and a real-world production case demonstrate the value of EasyFashion for multimodal design expression, body-specific evaluation, and production-oriented outputs in personalized garment design.
[HC-9] Verify Offload Extend Recommend: Selective Complementarity in AI Support for Physical Activity Planning with Longitudinal Patient Data
链接: https://arxiv.org/abs/2609.18479
作者: Pavithren V S Pakianathan,Rania Islambouli,Diogo Branco,Gil Batista Rosa,Rita Pinto,Albrecht Schmidt,Tiago Guerreiro,Jan David Smeddinck
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Self-tracking technologies create longitudinal patient-generated health data, yet integrating these data into clinical decision-making can increase information-processing demands. Generative AI may support sensemaking, but its value depends on clinical context and expertise. We investigate AI augmentation of a clinical decision support system for physical-activity planning in cardiovascular disease. In a counterbalanced within-subjects study, 26 exercise physiologists developed plans for four real cardiovascular cases with and without AI support, followed by evaluation of an AI exercise-plan generator. AI did not significantly improve workload, usability, confidence, or plan quality overall; however, its effect on plan quality increased as visualization literacy decreased and its effect on workload increased as visualisation literacy increased. Interviews and 152 chatbot queries revealed three recurring uses: verifying, offloading, extend and generate. Our findings position AI support as a selective complement to professional expertise while highlighting validation challenges when clinicians seek support precisely where their own knowledge is limited.
[HC-10] Hardware-Free Robotics Laboratories in Mixed Reality
链接: https://arxiv.org/abs/2609.18434
作者: Santiago Berrezueta-Guzman,Habiba-Loai Khalil,Andrei Koshelev,Vanesa Metaj,Stefan Wagner
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: Pre-print submitted for review
Abstract:Teaching robotics relies on screen-based simulation, showing robot motion in an abstract coordinate frame rather than at real scale in the learner’s own space, while access to physical hardware is limited by cost, safety, and scheduling constraints. We present MR-Robotics LAB, a mixed-reality (MR) platform that replays MATLAB-generated robot trajectories at real scale within the learner’s physical environment. A browser-based service validates a MATLAB workspace file (.mat), normalizes units, and publishes a versioned JSON trajectory; a Unity application on a Meta Quest 3 then reproduces the authored joint configurations under position control and replays them at the declared frame rate within a physics-enabled scene that supports collision detection and end-effector grasping. A formative single-group evaluation with engineering students found that participants reported low setup effort (M = 4.67 on a 5-point scale) and perceived support for workspace understanding from multi-viewpoint inspection (M = 4.56), and 83% of participants affirmed their willingness to use the platform in an introductory robotics course. The evaluation instrument records only perceived outcomes, without counterbalancing or a learning measure, so no comparative advantage over desktop simulation is claimed. The contribution is a reusable simulation-to-MR trajectory pathway and design guidance for hardware-free robot visualization in engineering education.
[HC-11] Building a Cultural Perspective on Doctor-Patient Conversations
链接: https://arxiv.org/abs/2609.18390
作者: Krithi Shailya,Siddharth D Jaiswal,Ashish Makani,Suvrankar Datta,Sunayana Sitaram,Mohit Jain
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:AI-powered medical scribes are increasingly used to transcribe doctor-patient conversations and automate clinical documentation. However, large-scale real-world consultation datasets are scarce due to the sensitivity of clinical conversations, leading developers to rely on simulated and LLM-generated synthetic consultations. While scalable, these alternatives may fail to capture culturally situated patterns of clinical interaction. We introduce interactional cultural markers, measurable patterns of doctor-patient interaction grounded in cross-cultural clinical communication, and use them to compare real, simulated, and synthetic consultations from Indian and US clinical contexts. We find distinct patterns of participation and control: Indian consultations involve greater patient participation but stronger doctor control, while US consultations exhibit balanced participation and open-ended discussion. Synthetic Indian consultations often fail to reproduce these patterns, instead converging toward US-like interaction. We identify additional synthetic signatures, including excessive doctor explanation and formulaic patient responses. We conclude by discussing implications for generating culturally grounded synthetic clinical conversations.
[HC-12] GYROval: A Robust Benchmark for Cultural Value Orientation in Large Language Models
链接: https://arxiv.org/abs/2609.18384
作者: Alexander Didenko,Anna Shabanova,Vladislav Zapylikhin,Alexander Antipov,Ruslana Raemgulova
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:We present a robust benchmark for measuring cultural value orientation in large language models on the two Inglehart-Welzel axes over several domains and roles (hence GYROval - Gridded Yielding of Robust value Orientation), together with the results of administering it to twenty models. Items are binary contrastive scenarios in the sense introduced by CDEval: both options are legitimate courses of action, neither is correct, there is no answer key, and a model’s score on an axis is the proportion of its responses falling on the counted pole. Eleven of the twenty models were additionally administered a paired Russian translation of the identical items and a second sampling temperature. The instrument is publicly released in both languages. Stability was assessed by treating the vignette as the unit of analysis, ranking the models within the levels of each perturbation factor, and summarising the agreement between levels by tie-corrected Kendall’s \emphW against an empirical permutation null.
[HC-13] SmartFlex: An Adaptive Lumbar Support System Based on Posture Recognition and Air Bag Array
链接: https://arxiv.org/abs/2609.18234
作者: Ben Xiaolu Huang
类目: Human-Computer Interaction (cs.HC)
备注: 4 pages, 5 figures, Accepted for publication in the Companion of the 2026 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp Companion '26)
Abstract:Low back pain (LBP) is a leading cause of disability worldwide and affects populations ranging from working adults to students with prolonged sitting habits. Conventional lumbar support belts are generally static and non-adaptive, which limits their ability to accommodate dynamic postural changes and individualized comfort requirements. This paper presents SmartFlex, an intelligent wearable lumbar support system that integrates real-time posture recognition with an adaptive air bag array. The system uses a JY901S gyroscope sensor to detect user posture and a lightweight TinyML neural network deployed on an Arduino R4 UNO to process posture data at the edge. Based on the recognized posture state, a closed-loop pneumatic control system dynamically inflates or deflates 14 distributed air bags through four independent micro air pumps to provide targeted biomechanical support. Evaluation results show that SmartFlex achieves over 94% posture recognition accuracy and generates corresponding pressure-control commands with a sensing-to-command delay of less than 120 ms. The pneumatic system operates within a calibrated pressure range of 15-85 kPa. A user study with 20 participants produced a 4.5/5 rating for support effectiveness, suggesting that adaptive wearable support may improve daily sitting comfort and reduce lumbar fatigue.
[HC-14] Beyond Accuracy: How Procedural Traces Shift the Decision Criterion of LLM Overseers
链接: https://arxiv.org/abs/2609.18204
作者: Zihan Chen,Di Zhu,Lei Zheng,Weiling Li
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 11 pages, 4 figures, 3 tables. Accepted at the 60th Hawaii International Conference on System Sciences (HICSS)
Abstract:Organizations increasingly use oversight loops where one large language model (LLM) audits another’s outputs alongside procedural traces of claimed steps. A common concern about such LLM-as-a-judge pipelines is that detailed traces make overseers gullible. Using signal detection theory, we audit five LLM overseers on 19 compliance tasks (4,551 analyzed judgments), varying only trace detail and evidence labeling. With disconfirming evidence always visible, error detection remains near ceiling. Instead, elaborate traces shift the decision criterion toward rejection, increasing false alarms in susceptible overseers. Without option labels, human-validated reason coding shows about 60% of false alarms cite an inability to tie evidence to its option. Labels eliminate this stated reason, yet residual rejection of correct work persists in those overseers and rises with trace detail. Procedural traces thus act as governance artifacts that shape oversight decisions. AI auditors should be evaluated by their decision criterion and false-alarm behavior, alongside accuracy.
[HC-15] Misgendering as Breakdown in Human-Machine Communication: How AI Companion Chatbot Users Experience and Repair Misgendering
链接: https://arxiv.org/abs/2609.18186
作者: Julia Liu,Qing Xiao,Leona Yinglang Pang,Haiyi Zhu,Hong Shen,Jordan Taylor
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 14 pages
Abstract:In recent years, large language model-based AI companion and role play chatbots have grown increasingly popular. People turn to these chatbots for emotional support and to engage in romantic and erotic role play. Although prior research suggests that digital role play can help people explore their gender and sexuality, LLM based technologies are also replete with gender and sexuality biases. In this study, we examine one way that AI chatbots can harm users: misgendering. In order to study chatbot misgendering we qualitatively analyzed 326 posts mentioning misgendering that were shared in AI companion or role play subreddits. We document how chatbot misgendering takes place and how, in response, users engage in ongoing work to curate their gender presentation to prevent and repair misgendering. We discuss how researchers and designers can mitigate chatbot misgendering and consider the implications of using AI chatbots for identity exploration.
[HC-16] Replication Studies: Not Just a Copy
链接: https://arxiv.org/abs/2609.18181
作者: Yiheng Liang,Kim Marriott,Helen C. Purchase
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Replication studies revisit previous experimental findings for multiple purposes, including assessing reliability, exploring generalisability, and evaluating research methods. Such studies entail design decisions that a single replication label cannot fully capture, making their designs difficult to describe and compare. We present REPVIS2, a validated design space that describes how a replication study relates to a reference study across eight practical dimensions, each coded as identical, similar, or different. We refined our initial design space, REPVIS1, by applying it to a corpus of replication studies and validated REPVIS2 against a separate corpus. We then characterised 86 replication studies from 51 papers in visualisation through paired reading of the replication and reference reports. Studies often retained the task while changing the procedure, interface, environment, participant population, evidence, or analysis. Additions beyond the replication core were also common. REPVIS2 makes replication design explicit for characterisation, reporting, and planning. An interactive visualisation and supplementary materials are available at this https URL.
[HC-17] Needs Your Help: Understanding Platform-Directed Rating Participation in Community Notes on X
链接: https://arxiv.org/abs/2609.18096
作者: Shuning Zhang,Changxi Wen,Jiuchang Wang,Dai Shi,Gabriele Lenzini,Yuwei Chuai,Xin Yi
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Community-based fact-checking is promising in countering misinformation, yet its scalability is constrained by slow rating accumulation. To address this challenge, platforms such as X implement platform-directed rating mechanisms, specifically through ``Needs Your Help’’ algorithmic prompts, to target unresolved notes. Using a dataset of over 220 million rating contributions – including 1.9 million platform-directed ratings – on X, we examine contributors’ response to note prompts, notes’ resolution, and raters’ spillovers. We found (i) at note level, population-sampled ratings concentrate on recent notes with certain helpfulness and high disagreement. Once sampled, population-sampled rating was associated with faster and more transitions to resolved statuses. (ii) At rater level, following raters’ first observed population-sampled rating, raters exhibit significant yet modest increases in daily ratings, rating pace and tag usage, while other behaviors show no change. These highlight the promise of algorithmic nudges to guide volunteer attention toward contested content, accelerating consensus while sustaining rater engagement.
[HC-18] A Design Space for Visual Interfaces for Generative Image Models
链接: https://arxiv.org/abs/2609.18065
作者: Susie S.Y. Li,Mingwei S.G. Li,Remco Chang
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Interactive visual interfaces have become an important means of controlling generative image models, enabling users to manipulate generation through prompts, direct manipulation, and a range of interactions. However, existing techniques are typically presented as independent systems, making it difficult to understand how they relate, compare their interaction mechanisms, or identify opportunities for new interface designs. We introduce a design space for interactive visual interfaces for generative image models derived from 51 research systems and practitioner tools. The framework decomposes each system into three complementary components: the user interface (U), the controllable model objects (Z), and the mapping function ( \phi ) that translates user interaction into model operations. This decomposition provides a common representation for analyzing heterogeneous interaction techniques across model families, revealing recurring design patterns and underexplored regions of the design space. We further present an interactive corpus explorer that support comparative analysis, and discuss usage scenarios for both educational settings and HCI/AI practitioners identifying research and design opportunities.
[HC-19] Encypher: Shared Agency and Social Presence in Collaborative Music Generation for Dance Cyphers
链接: https://arxiv.org/abs/2609.18062
作者: Zhixing Chen,Cheng-Zhi Anna Huang
类目: Human-Computer Interaction (cs.HC); Sound (cs.SD)
备注: Preprint. Under review at CHI 2027. 11 figures, 1 table. Project page: this https URL
Abstract:Music and dance are social practices of expression and connection, yet most HCI work in human-AI co-creation centers the solo performer. As generative music matures, we ask not only what AI can compose but what social encounters it can organize around sound. We present Encypher, a collaborative generative music system that translates collective movement qualities into text prompts conditioning real-time music generation for dance cyphers. Through five weeks of co-design with local dancers, a user study with unacquainted participants, a public museum event, and a live performance, we found that users developed shared agency, perceiving the music as a response to the room’s energy. While newcomers felt uncertain, the system fostered social presence by prompting them to look to each other for cues. By treating sociality as a design concern rather than a downstream effect, we offer a framework and design implications for AI systems for collaborative, embodied expression.
[HC-20] AI Peers Exert Social Influence on Human Dishonesty in Groups
链接: https://arxiv.org/abs/2609.18060
作者: Shuning Zhang,Xinyuan Zhou,Yuanyang Qiu,Tianqi Song,Yuting Yang,Yiwen Ren,Xin Yi
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Human dishonesty in group settings is highly susceptible to peer influence, particularly when incentivized. Although artificial intelligence (AI) evolves from passive tools into active collaborators, its impact on human moral behavior within groups remains underexplored. We addressed this gap through a two-phase randomized behavioral study (N=280 and N=360). We found AI agents exert substantial social influence comparable in magnitude to that of human peers. Specifically, participants reported more dishonestly when exposed to dishonest rather than honest normative cues. This effect is evident across injunctive, subjective, and descriptive social norms. Interestingly, the only significant adjacent behavioral change occurred when dishonest peer behavior first appeared, whereas further increases from one to four dishonest peers produced weaker and non-monotonic changes. Furthermore, participants rapidly converge on decision-making, showing modest increases in dishonest reporting through repeated exposure. These findings highlight the importance of managing the behaviors and normative signals communicated by AI group members.
[HC-21] Gaze as Evidence for Common Grounding: A Cross-Corpus Analysis of MapTask and MUNDEX EMNLP2026
链接: https://arxiv.org/abs/2609.18011
作者: Nan Li,Albert Gatt,Massimo Poesio
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 16 pages, 17 tables, 2 figures; accepted to the MINT workshop at EMNLP 2026 (oral presentation)
Abstract:In collaborative tasks with asymmetric information, participants coordinate their understanding through interaction. We ask whether gaze provides evidence about grounding across two such tasks. Working from discrete behavioral annotations, we map HCRC MapTask (Anderson et al., 1991) and MUNDEX (Türk et al., 2023) into a shared partner/task/away vocabulary and compute gaze features around task-relevant dialogue units. In both corpora, aligned reference interpretations (MapTask) and UND (understood) judgments (MUNDEX) are associated with more task-directed gaze and with less partner-directed gaze, lower gaze entropy, and fewer gaze transitions. The associations are clearest for the participant leading the task: in giver-produced references, and in explainer judgments, which also co-vary with the explainee’s gaze. In same-speaker MapTask reference chains, the speaker’s gaze entropy is lower at the mention where a previously non-aligned referent becomes aligned. The best gaze feature groups improve modestly over controls under grouped cross-validation: temporal features in MapTask and raw proportions in MUNDEX. Because effects are small and several weaken when recurring participants rather than dialogues are the unit of inference, we treat gaze as one contributing cue to grounding, to be interpreted alongside task and dialogue context.
[HC-22] Measuring AI Leadership: Development and Validation of a Multidimensional Measure for AI-Native Organizations
链接: https://arxiv.org/abs/2609.17965
作者: Mustafa Akben,Leslie Coyne
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:AI is changing what leaders must judge, explain, learn, and coordinate, yet existing measures do not capture these behaviors at the level needed to study leadership in AI-enabled work. We develop the AI Leadership Battery, which organizes 36 behaviorally specific subdimensions into 11 theory-specified content families. Following established scale-development procedures, the research used deductive item generation; content validation of definitional correspondence and definitional distinctiveness; exploratory factor analysis and item reduction; confirmatory factor analysis in independent samples; and tests of internal consistency reliability, convergent validity, discriminant validity, and criterion-related and incremental validity. Across the development and validation studies, the analyses provided evidence for the Battery’s content, multidimensional structure, reliability, and distinction from selected orbiting constructs. The Battery also contributed additional information beyond orbiting constructs across organizational growth, decision speed, customer/stakeholder response capability, AI-enabled team performance, AI-enabled work experience, AI security and risk management, and AI adoption and integration. The resulting measure provides researchers with a behavioral framework for examining how leaders in AI-enabled work regulate judgment, learning, adaptation, transparency, and accountability.
[HC-23] A11yLTLNav: Automatic Detection of Accessibility Navigation Failures
链接: https://arxiv.org/abs/2609.17959
作者: Chenming Ge,Kewen Peng,Chengyang Shi,Ben Greenman,Yue Jiang
类目: Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注:
Abstract:For blind and low-vision (BLV) screen-reader users, a website that appears accessible in a static snapshot can become difficult or impossible to navigate once interaction begins. Yet, most automated accessibility checkers miss failures involving focus, interface state, and accessible feedback across interactions. We present A11yLTLNav, a property-based approach for automatically detecting accessibility navigation failures. Through a structured review of prior research, we organize accessibility navigation failures into a failure taxonomy and formalize a browser-observable subset as executable Linear Temporal Logic properties over action-state traces. A11yLTLNav combines random keyboard exploration with runtime property monitoring to detect these failures during interactions. We evaluate A11yLTLNav on 31 generated websites based on real-world websites and tasks. It reported 309 accessibility failures, of which 274 were confirmed, achieving 88.7% precision and identifying more confirmed failures than the comparison checkers. Our results show that A11yLTLNav transforms accessibility knowledge into reusable checks of interface behavior over time.
[HC-24] Apply-xMag: One Tool to Support Many Inclusive Design Methods
链接: https://arxiv.org/abs/2609.17948
作者: Sadia Afroz,Rudrajit Choudhuri,Fatima A. Moussaoui,Amreeta Chatterjee,Margaret Burnett,Anita Sarma
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Doing inclusive design in HCI practice can be labor-intensive, a costly barrier that some companies and HCI practitioners may be unwilling or unable to overcome. Yet, not doing inclusive design is costly too, in the form of UX barriers that disproportionately disadvantage under-served user populations. To address this problem, we introduce Apply-xMag, an LLM-powered tool to support HCI practitioners’ work to design their products inclusively to wide ranges of users. Apply-xMag is general, supporting any inclusive design method that can be expressed as xMags (i.e., using attribute ranges and heuristics). It is also effective: Empirical results with researcher and practitioner teams using various combinations of two xMags on 7 products showed Apply-xMag precision averaging 90-99% and recall averaging 82-89%. Further, its environmental costs were reasonable, costing about the same resources as 2-4 ordinary Google searches.
[HC-25] AI Mediators Regulate Emotion and Create Value in Disputes
链接: https://arxiv.org/abs/2609.17933
作者: James Hale,Jonathan Gratch
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:In conflict and disputes, especially, emotion acts as a salient force in influencing outcomes. Prior work shows negative affect can obstruct collaborative behaviors, which typically lead to ``win-win’’ outcomes. Thus, some suggest mediators may help regulate emotion and achieve joint gains. With the proliferation of AI, we posit LLMs may perform well at this task, with the added benefit of better accessibility compared with a human mediator. To examine the effectiveness of AI versus novice human mediators, we conduct a between-subjects experiment, where participants engage in a dispute mediated by a human, AI, or no mediator. We first analyze how well the mediators regulate emotions within a dispute – finding AI mediators perform significantly better than humans at reducing negative emotion. We next examine whether AI mediators facilitate disputants better realizing joint gains in disputes with high integrative potential (IP) – we find a marginally significant interaction between IP and condition (AI versus human), indicating LLMs may outperform humans at aiding disputants realize joint gains. Lastly, we perform an analysis of the messages the mediators sent, finding the AI sent significantly more messages suggesting trade-offs compared to the humans.
[HC-26] Does AI Assistance Leave a Temporal Fingerprint? Detecting Overreliance in AI-Assisted Writing and Programming
链接: https://arxiv.org/abs/2609.17883
作者: Eduardo Davalos,Yike Zhang
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures, 2 tables, submitted to AHFE 2026 Hawaii Edition
Abstract:The rapid adoption of generative AI has made final artifacts unreliable evidence of student learning, and AI detectors that examine only the finished product are inaccurate and ethically contentious. Process data offers an alternative, but prior work covers only English essay writing. We ask whether AI assistance carries a temporal signature, whether it generalizes from writing to programming, and whether it distinguishes ordinary collaboration from wholesale delegation. We analyze three public corpora: CoAuthor (1,447 keystroke-level co-writing sessions), RealHumanEval (editor telemetry from 243 programmer records), and a pre-LLM CS1 corpus (5.1 million keystrokes) as a human-only baseline, comparing minimal-AI work, collaborative AI use, and simulated wholesale delegation. Three findings emerge. First, the signature generalizes: AI contributions arrive in bursts far outside the author’s own baseline in both mediums (paired d_z = 1.13 and 3.54). Second, engagement diverges by medium: 93% of AI-inserted characters survived to writers’ final documents, while only 14% of accepted code suggestions survived intact. Third, classifiers using only observable temporal features separate simulated delegation from authentic work nearly perfectly (F1 \geq 0.997; at most 0.5% of real work misclassified), while ordinary collaboration remains hard to distinguish from unassisted work. Temporal evidence flags wholesale delegation rather than assistance, positioning process visibility as a candidate evidentiary basis for academic integrity, pending validation in authentic coursework.
[HC-27] Learning Heterogeneous Preferences
链接: https://arxiv.org/abs/2609.17847
作者: Shiwali Mohan,Matt Hong,Dule Shu,Aniek Fransen,Shabnam Hakimi,Matt Klenk
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Learning from human feedback has become a central paradigm for training modern AI systems, where models of human utility are used as reward models in policy learning. Existing methods typically assume a \emphuniversal utility function shared across a population and treat disagreement between annotators as stochastic variation. While suitable for objective tasks, this assumption breaks down in subjective domains where preferences vary systematically across individuals. We study the problem of subjective preference learning, in which observed choices arise from heterogeneous but internally consistent utility functions. Drawing upon rational choice theory, RCT \parencitetversky1981framing, we introduce \emphindividuated utility functions conditioned on both the individual and their decision context, and propose a novel multi-stage architecture for estimating them from multi-modal data. We evaluate our framework on a newly collected dataset of more than 575,000 pairwise aesthetic judgments from 2,398 participants comparing automotive wheel designs. Our experiments show that individuated utility models substantially outperform universal utility models including foundation model baselines. Our results demonstrate that disagreement reflects meaningful preference heterogeneity rather than annotation noise. More broadly, our findings highlight the importance of collecting annotator attributes and learning individuated utility functions, enabling reward models that explicitly account for whose preferences they represent and faithfully capture human decision diversity.
[HC-28] Lexara-RF: Reference-Free Metrics for Evaluating Conversational Visual Analytics Agents
链接: https://arxiv.org/abs/2609.17842
作者: Srishti Palani,Vidya Setlur
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 4 pages, 1 figure Conversational Visual Analytics, Evaluation Metrics, Visualization Design, Cooperative Communication Principles
Abstract:Conversational visual analytics (CVA) agents powered by large language models generate visualizations and natural-language explanations from open-ended queries. Evaluating these multimodal outputs is challenging: curated reference benchmarks are costly to author, cannot comprehensively capture the space of valid responses, and are unavailable in production. Building on the Lexara evaluation framework, we introduce Lexara-RF, a reference-free set of metrics that scores CVA outputs using only the prompt, data, and model response. We reformulate evaluation as verification: 13 metrics operationalize visualization design theory and Gricean cooperative principles as computable consistency, intent-alignment, and design validity checks. On a human-rated corpus of CVA test-cases, Lexara-RF achieves alignment comparable to reference-based formulations, outperforms surface-similarity NLG baselines, and localizes structurally grounded failures with high accuracy.
[HC-29] Participant-Mediated Collection of Sensitive Digital Trace Data: The CANDOR Research Infrastructure
链接: https://arxiv.org/abs/2609.17722
作者: Andrew Zhao,Rijul Magu,Ekta Raj,Teresa Elinjikkal,Munmun De Choudhury
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注:
Abstract:Digital trace data provide rich measures of behavior in everyday settings, but the research ecosystem supporting their collection is constrained by declining platform API access and a historical reliance on publicly observable data. Participant-mediated data donation offers a complementary approach in which individuals contribute selected portions of their own digital histories to research. Such data can include longitudinal and non-public behavior, span multiple platforms and modalities, and be linked to independently collected study measures, enabling study designs that are difficult to implement using public social media data alone. These opportunities also introduce methodological challenges around participant control, data minimization, heterogeneous platform exports, privacy, and governance, particularly when semantic or multimodal content is necessary to study the construct of interest. We present CANDOR (Collecting and Analyzing Networked Data for Open Research), an end-to-end infrastructure for participant-mediated collection and governance of sensitive digital trace data. CANDOR supports participant-directed selection of platforms, data types, and temporal ranges; modular platform- and modality-specific parsing and de-identification; linkage to independent study measures; and protected processing, storage, and access. We derive design requirements for this class of research and compare CANDOR with existing data donation infrastructures, identifying how different approaches support participant control, data minimization, scientifically necessary data richness, study-design flexibility, and governance. Together, this work provides a methodological and infrastructural framework for using participant-contributed digital traces in behavioral research, particularly when the data needed to address a scientific question are longitudinal, non-public, multimodal, or sensitive.
[HC-30] “We Are Tired of Explaining”: Communication Practice and AI Roleplay Training for Community Health Workers in Rural India
链接: https://arxiv.org/abs/2609.17710
作者: Neil K. R. Sehgal,Sunny Rai,Sai Preethi Matam,Khushboo Gupta,Hamid Abdullah,Mohit Jain,Sharath Chandra Guntuku
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Community health workers (CHWs) in the Global South increasingly encounter AI-powered tools, yet the counseling work central to their role remains largely unsupported. We study communication practices among Accredited Social Health Activists (ASHAs) in rural Rajasthan, India, through simulated family-planning calls, semi-structured interviews, and an LLM chatbot roleplay design-probe with 20 participants. In calls, ASHAs often responded to social or material concerns by shifting to health-risk information, denying concerns, promising unspecified help, or listing medical solutions with limited explanation. A smaller set of responses instead engaged concerns, sought permission before involving family members, or left decisions with beneficiaries. We interpret these patterns through Motivational Interviewing, emphasizing restraint from correcting, persuading, or over-solving. Drawing across observed calls, interviews, and probe reactions, we derive design considerations for AI roleplay training: keep AI in a rehearsal role, provide descriptive rather than prescriptive feedback, and evaluate counseling process rather than agreement with prescribed responses.
[HC-31] Real-Time Service Robot Replanning via Simple Button Interaction for Improved Task Success and User Experience
链接: https://arxiv.org/abs/2609.17541
作者: Ryo Terashima,Yuga Yano,Koshun Arimura,Hakaru Tamukoh
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: 6 pages, accepted as a conference paper for 2026 35th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2026)
Abstract:Service robots must respond to unexpected instructions in real-world environments. However, robots cannot detect all failures and exceptions during a task. To address these issues, we propose a real-time feedback function that enables robots to modify their behavior based on human feedback. In this system, users can intuitively send feedback to the robot by pressing a single button on a tablet when the robot fails to act correctly. Robots use this feedback to consider their failures and replan appropriate actions to complete the task. We conducted experiments with and without the feedback function to verify the following hypothesis: “Simple interactions do not cause a negative user experience.” All questionnaire responses are evaluated on a five-point Likert scale. After adding the feedback function, the response score for the question “Did you feel that the robot’s behavior was unexpected?” improved by 0.5 points, and that for “Did you feel anxious about the robot’s behavior at times?” improved by 0.9 points. These results support the study’s hypothesis and indicate that incorporating this real-time feedback function can simultaneously improve task success and the user experience.
计算机视觉
[CV-0] PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics
链接: https://arxiv.org/abs/2609.19142
作者: Bardienus P. Duisterhof,Kaifeng Zhang,Adam Hung,Bowen Wen,Stan Birchfield,Yunzhu Li,Deva Ramanan,Jeffrey Ichnowski
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: this https URL
Abstract:World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse volumes of data, to instill a rich prior into downstream applications. Existing methods typically require robot action labels to learn action-conditioned 3D dynamics, which excludes web video data from the training pool. We study 3D point track completion as a pre-training objective for learning transferable 3D dynamics without robot data. Given a single RGB-D observation and sparse partial 3D trajectories (tracks), we predict future 3D tracks of all observed points. We show this objective produces a rich 3D dynamics prior, without requiring robot action labels. We contribute a diverse dataset of 2.9 million synthetic frames spanning deformable, articulated, and rigid objects, and use it to train PointZero. We show that a flexible and expressive transformer, PointZero, outperforms prior methods on the same data. We demonstrate the utility of our pre-training objective by post-training PointZero for two downstream applications: (1) action-conditioned 3D dynamics prediction and (2) imitation learning. When fine-tuned to condition on end-effector pose, PointZero outperforms the baselines on the recent PGND 3D dynamics benchmark. When fine-tuned to predict robot actions and 3D tracks, PointZero outperforms or matches the baselines on 6/7 simulated and real-world robot manipulation tasks. We furthermore evaluate training PointZero from scratch to isolate the benefits of our proposed architecture from those of our proposed pre-training objective and dataset. We release the dataset, checkpoints, and full training recipe.
[CV-1] In-Context Robot Learning with VLM Agents
链接: https://arxiv.org/abs/2609.19138
作者: Dongzhou Cheng,Taoran Yi,Ye Fang,Xingwu Zhang,Fan Feng,Yixuan Li,Gengxiong Zhuang,Rongze Wang,Shuai Yang,Wei Song,Weizhi Xue,Minyan Wu,Jie Gui,Jiaqi Wang,Tong Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Project Page: this https URL GitHub Code: this https URL
Abstract:Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI. No finite collection of demonstrations can cover every task and situation a robot will encounter, making the ability to learn from context at deployment essential for generalization. Such in-context learning (ICL), however, remains largely beyond the reach of existing robotic policies. The broad agentic capabilities of commercial vision-language models (VLMs), such as GPT-6 Astra, raise a compelling question: can these models learn from demonstrations, examples, and interaction feedback, then translate that information into executable and verifiable robot behavior from a new initial state without gradient updates or persistent changes to task-specific parameters? We introduce GPT-Policy, a general-agent framework for in-context robot learning. GPT-Policy integrates a context compiler that preserves task-relevant visual transitions, a VLM that proposes robot-tool actions, and a constrained controller that verifies and executes each action and reports its outcome. We evaluate its reliability and limitations through task success and efficiency metrics, matched comparisons across models, and controlled context ablations. In real-robot trials, human video demonstrations improve task completion even without robot action labels, while aligned action references yield further gains on contact-sensitive tasks. These findings position GPT-Policy as a step toward robot adaptation through in-context learning, providing an empirical foundation for translating the general-purpose capabilities of VLMs into physical behavior and clarifying the challenges that must be overcome for reliable deployment.
[CV-2] Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Signal Representation
链接: https://arxiv.org/abs/2609.19122
作者: Meng’en Qin,Yinchen Liu,Mingxuan Cui,Youlu Xing
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual signals require compact yet sufficient representations for robust downstream prediction. Convolutional sparse coding (CSC) provides an explicit mechanism for suppressing redundant components while preserving signal content, but its sparsity coefficient is typically fixed and manually selected. We propose an adaptive convolutional sparse coding framework for robust visual signal representation. Specifically, we unfold the CSC optimization with the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and treat the sparsity coefficient as a differentiable variable jointly learned with the network parameters. From the information bottleneck perspective, this coefficient controls the trade-off between information retention and compression: the sparsity term promotes compact representations, while the reconstruction term together with task loss preserves task-relevant signal content. We further introduce a label-free post-training strategy that adjusts the compression strength for corrupted inputs with the main network parameters fixed. Experiments on CIFAR and ImageNet demonstrate competitive clean-data recognition and greatly improved robustness under different input perturbations.
[CV-3] rack Articulate Act: Generating Articulation from Casual Human Videos
链接: https://arxiv.org/abs/2609.19119
作者: Jiaming Zhang,Homanga Bharadhwaj
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint. Under Review
Abstract:Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes in object state. In this work, we study articulated objects such as doors, drawers, cabinets, laptops, ovens, and hinged containers that are ubiquitous in daily life and present unique challenges for embodied interaction. These objects cannot be represented by a single pose; their motion depends on the underlying parts and joints. We introduce a real-to-sim framework that reconstructs a simulation-ready articulated object and hand-object interaction from a casual monocular RGB video, without RGB-D or multi-view input, prior scans, manually specified joints, or robot demonstrations. Our key insight is that dense 3D point tracks provide an embodiment-agnostic articulation cue: points on the fixed link remain approximately stationary, while points on the moving link follow coherent revolute or prismatic motion. Our method segments the links, estimates the joint and its state trajectory, reconstructs an articulated asset, and aligns the recovered 3D hand motion with the object. Central to our approach is a modular recipe that repurposes powerful pretrained models for single-image 3D reconstruction, mesh segmentation, and 3D scene flow, connecting their predictions through explicit geometric reasoning to infer articulation. We use the reconstructed articulated object and the human hand trajectory to replay interactions through contact in MuJoCo. The framework shows how pretrained vision models and explicit motion reasoning can turn casual human videos into articulated object models suitable for downstream embodied interactions. this https URL
[CV-4] abular Deep Learning vs Classical Machine Learning for Urban Land Cover Classification NEURIPS2025
链接: https://arxiv.org/abs/2609.19010
作者: Muntasir Tabasum,Tanpia Tasnim,Md. Ekramul Islam,Al Zadid Sultan Bin Habib
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Published in NeurIPS 2025 The 5th Muslims In ML (MusIML) Workshop
Abstract:Urban Land Cover (ULC) classification plays a crucial role in urban planning, environmental monitoring, and sustainable development. We study this task using the ULC dataset from the UCI Machine Learning Repository, which includes tabular features derived from high-resolution aerial imagery across nine classes (e.g., roads, trees, grass, water). The dataset presents typical remote sensing challenges, including high dimensionality, heterogeneous features, and class imbalance. In a unified, reproducible pipeline, we benchmark classical machine learning models (e.g., Logistic Regression, SVM, Random Forest, XGBoost, CatBoost) against Tabular Deep Learning (TDL) models (TabNet, FT-Transformer, TabTransformer, TabSeq, and 1D CNNs). To address class imbalance, we employ weighted cross-entropy loss for TDL models and evaluate performance using accuracy, macro-precision, macro-recall, macro-F1, AUC-ROC, and confusion matrices. Our results show that while tree ensembles remain strong general baselines, TDL models can match or exceed their performance when non-linear interactions are significant and imbalance handling is effective, providing complementary advantages for urban land cover mapping. See code: this https URL
[CV-5] Instrument Classification of Solo Sheet Music Images ICASSP2021
链接: https://arxiv.org/abs/2609.18980
作者: Kevin Ji,Daniel Yang,TJ Tsai
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Sound (cs.SD)
备注: Published at ICASSP 2021
Abstract:This paper studies instrument classification of solo sheet music. Whereas previous work has focused on instrument recognition in audio data, we instead approach the instrument classification problem using raw sheet music images. Our approach first converts the sheet music image into a sequence of musical “words” based on the bootleg score representation, and then treats the problem as a text classification task. We show that it is possible to significantly improve classifier performance by training a language model on unlabeled data, initializing a classifier with the pretrained language model weights, and then finetuning the classifier on labeled data. In this work, we train AWD-LSTM, GPT-2, and RoBERTa models on solo sheet music images from IMSLP for eight different instruments. We find that GPT-2 and RoBERTa slightly outperform AWD-LSTM, and that pretraining increases classification accuracy for RoBERTa from 34.5% to 42.9%. Furthermore, we propose two data augmentation methods that increase classification accuracy for RoBERTa by an additional 15%.
[CV-6] LaSeD: Label-Semantic Self-Distillation for Visual-Only Surgical Phase Recognition
链接: https://arxiv.org/abs/2609.18971
作者: Ye Tao,Claudia Scherl,Sara Monji-Azad
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Surgical phase recognition maps each video frame to a clinically meaningful workflow phase, supporting context-aware assistance, documentation, and postoperative analysis. Most methods treat phase annotations only as class IDs, whereas recent surgical vision-language models often require additional video–text data, captions, or instruction tuning. We propose \emphLaSeD, a label-semantic self-distillation framework that uses phase names as privileged training-time context while retaining visual-only deployment without a ground-truth phase-name hint. LaSeD initializes a frozen teacher and a student from the same pretrained VLM checkpoint. The teacher receives the frame, a fixed task prompt, and the ground-truth phase-name hint; the student receives the same frame and prompt without the hint, and only its visual encoder is optimized. Training combines hard phase-token supervision with feature-level distillation from cached teacher representations. At inference, the teacher and hint are removed, and the student predicts one of the seven Cholec80 phases through constrained digit-token logits without an additional classifier head. On the Cholec80 evaluation split, LaSeD achieves 86.20% accuracy, 77.75% macro recall, 78.13% macro precision, and 64.15% macro Jaccard. Under the identical protocol, it improves a visual-only Qwen3-VL-4B baseline by 9.45, 9.42, 11.93, and 12.22 percentage points, respectively. Visual-only means that the image is the only sample-specific inference input, while all frames share the same fixed task prompt. These results suggest that phase names provide a useful low-cost signal for adapting VLMs to surgical workflow analysis. (The code will be published soon.)
[CV-7] KDTwin: Task-Aware Knowledge Distillation for Lightweight Multi-Task Driving Scene Segmentation
链接: https://arxiv.org/abs/2609.18955
作者: Huy Che,Minh-Khoi Do,Dinh-Duy Phan,Duc-Khai Lam
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Efficient perception models are essential for real-time autonomous driving, where accuracy and computational cost must be carefully balanced. However, applying knowledge distillation to multi-task driving scene segmentation is challenging because drivable-area and lane segmentation exhibit different spatial characteristics and class imbalance. We propose KDTwin, a task-aware distillation framework for lightweight multi-task segmentation networks. The proposed method performs distillation at both the shared encoder and task-specific decoders. Encoder-level pairwise distillation transfers spatial relational knowledge to enhance the student’s shared representation. For the decoders, we use a weighted loss for drivable-area segmentation and a boundary-aware loss for lane segmentation, enabling task-adaptive knowledge transfer without increasing inference complexity. Experiments on BDD100K show consistent improvements across the evaluated CNN-based and Transformer-based student models without increasing inference-time parameters or FLOPs. The results show that designing distillation objectives according to task-specific characteristics can effectively enhance multi-task segmentation performance for autonomous driving. The source code is available at this https URL.
[CV-8] Automated Dental Caries Segmentation in Panoramic Radiographs Using Dual-Stage Deep Learning MICCAI2025
链接: https://arxiv.org/abs/2609.18952
作者: Jihun Kim,Kyeonghun Kim,Jong-yeol Lee,Yeongseok Seo,Dohyun Chun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 6 figures, 3 tables. Accepted at the Workshop on Machine Learning in Medical Imaging, MLMI 2025, held in conjunction with MICCAI 2025
Abstract:Early detection of dental caries remains challenging due to limitations in traditional diagnostic methods, particularly for proximal lesions in posterior teeth. Deep learning models show promise for automated caries detection but face scalability constraints due to requirements for large volumes of expertly annotated training data. This study presents a dual-stage deep learning framework combining Faster R-CNN for tooth localization with U-Net for pixel-wise caries segmentation in panoramic radiographs. We developed a systematic transformation pipeline to convert large-scale polygon-annotated datasets into high-resolution binary segmentation masks, enabling pixel-wise supervised learning. The framework was trained using both expert-verified datasets and algorithmically processed labels from 3,000 panoramic images. Our approach achieved robust performance with an IoU of 0.9013, Dice coefficient of 0.9482, Recall of 0.9433, and Precision of 0.9774, demonstrating superior accuracy compared to existing methods while significantly reducing false-positive rates. The dual-stage framework effectively addresses data annotation bottlenecks in dental AI applications and demonstrates potential for scalable, automated caries detection systems that can improve diagnostic consistency and support clinical decision-making.
[CV-9] Dose-Aware Cold Diffusion with Physics Consistency for Generalizable Low-Dose CT Reconstruction IJCNN2026
链接: https://arxiv.org/abs/2609.18943
作者: Md Imam Ahasan,Guangchao Yang,A F M Abdun Noor,S M Hasan Mahmud,Md Mahfuzur Rahman
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages, 7 figures, 3 tables. Accepted at International Joint Conference on Neural Networks (IJCNN 2026)
Abstract:Reducing radiation dose in computed tomography significantly degrades image quality and poses challenges for accurate and clinically reliable reconstruction. While recent approaches have shown promise for low-dose CT, they often struggle to generalize across continuous and previously unseen dose levels, leading to artifacts and loss of anatomical detail. To address these limitations, we propose Dose-Aware Cold Diffusion (DACD), a physics-consistent reconstruction framework that explicitly models radiation dose as a continuous latent factor within a cold diffusion process. The proposed DACD framework integrates image-based dose-aware perception, multi-scale structural prior extraction, and dose-calibrated step allocation to adaptively guide the denoising trajectory. In addition, an iterative forward-backprojection correction is incorporated into the reverse refinement process to enforce projection-domain data consistency. Extensive experiments on three public benchmarks, including Mayo-2020, Mayo-2016, and LoDoPaB-CT, demonstrate that DACD consistently outperforms state-of-the-art diffusion-based and physics-guided methods in both quantitative accuracy and visual fidelity, particularly under ultra-low-dose conditions. The results show that DACD achieves robust generalization across a continuous range of dose levels, including those unseen during training.
[CV-10] PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image
链接: https://arxiv.org/abs/2609.18920
作者: Sneha Paul,Guile Wu,Bingbing Liu,Dongfeng Bai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Technical report
Abstract:Physical properties, such as friction, hardness, stiffness, and density, govern how robots should grasp, manipulate and interact with objects, yet estimating these properties from RGB images remains challenging. Existing methods typically employ per-object reconstruction augmented with physical properties or directly query vision-language models at test time, which results in substantial computational overhead that limits their applicability. In this work, we present PhysVGGT, a feed-forward model that predicts dense maps of friction coefficient, Shore hardness, Young’s modulus, and density, together with object-level mass, from a single RGB image in one forward pass. The key idea of PhysVGGT is to formulate physical property estimation as a dense per-pixel prediction problem and employ a visual geometry transformer to extract geometry-aware tokens from the input image followed by a dense prediction branch for estimating local physical properties and a global prediction branch for estimating object-level mass. In addition, we introduce a scalable pseudo-label generation pipeline that enables large-scale weakly supervised training for dense physical property prediction, substantially reducing the need for expensive direct physical measurements. Extensive experiments show that PhysVGGT achieves state-of-the-art performance on the ABO-500 dataset and generalizes effectively to the out-of-distribution NeRF2Physics dataset. Moreover, PhysVGGT eliminates the need for per-object reconstruction and test-time optimization, achieving an inference latency of only 0.13s per image, making it 27\times faster than the previous state of the art.
[CV-11] NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian Splatting
链接: https://arxiv.org/abs/2609.18898
作者: Yihan Zang,Da Li,Dominik Engel,Shinkyu Park,Ivan Viola
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 6 figures
Abstract:Training-free weighted aggregation is widely used to lift 2D semantic features onto 3D Gaussians for open-vocabulary scene understanding, yet its theoretical role remains insufficiently understood. Existing analyses typically justify this operation from the rendering side, treating Gaussian features as linearly composable Euclidean variables for reconstructing 2D feature maps. However, this view does not match downstream 3D usage, where each Gaussian is often queried independently in a cosine-based embedding space. We revisit feature lifting from the 3D side and formulate per-Gaussian assignment as a cosine alignment problem on the CLIP unit sphere. Under this objective, the L2-normalized semantic back-projected feature emerges as the closed-form solution, providing a complementary interpretation of the standard lifting rule from the perspective of per-Gaussian semantic assignment. The same formulation further yields a norm decomposition into intra-view and inter-view consistency, suggesting that feature magnitude itself can serve as a semantic reliability signal. Calibrated by effective multi-view support, this reliability score guides a mode-voting refinement that preserves CLIP feature validity by avoiding linear averaging. Experiments on open-vocabulary 3D semantic segmentation show that NormLift is an efficient, training-free framework that achieves strong performance across evaluation protocols.
[CV-12] Copy What Is Seen Generate What Is Not: Training-Free Anomaly-Aware Video Restoration
链接: https://arxiv.org/abs/2609.18836
作者: Zhida Qu,Shengchao Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 9 figures, 7 tables
Abstract:A surveillance system that detects an anomaly often has to repair the footage as well, yet the two tasks are studied in isolation: training-free anomaly detectors stop at a score or a label, while training-free video editing answers to a user prompt rather than to a detector. This paper proposes AVR (Anomaly-aware Video Restoration), which closes that gap with frozen pretrained models alone and generates content only where the clip offers no evidence to copy. Motion evidence first gates open-vocabulary proposals into spatio-temporal masks. A background prior computed from the clip then fills every pixel the anomaly ever uncovers, leaving diffusion to synthesize only what no frame showed, and a frozen verifier decides per clip whether to trust a classical, a prior-anchored, or a background-conditioned restorer. Extensive experiments on three surveillance datasets, under both full-reference anomaly injection and real anomalies, show that AVR leads full-frame fidelity under oracle masks, matches three trained video inpainters inside the edited region, and outperforms a detect-then-generate pipeline on the masks it produces itself, while suppressing both the residual anomaly and the flicker of free diffusion.
[CV-13] DISTA-Net: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation
链接: https://arxiv.org/abs/2609.18773
作者: Mengze Xu,Zhu Liu,Weidong Sheng,Boyang Li,Yimian Dai,Ming-Ming Cheng,Jian Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-range infrared imaging frequently confronts dense target clusters whose diffraction-limited signatures merge into a single indistinguishable blob, concealing the number, sub-pixel positions, and radiant intensities of the underlying sources. While deep learning has advanced general object detection, resolving such Closely-Spaced Infrared Small Targets (CSIST) remains largely unexplored, owing to a systemic infrastructure void and a fundamental paradigm mismatch. The dominant formulation, which reduces unmixing to a blind, discrete sub-pixel separation, is inherently insufficient: without semantic guidance, the ill-posed inverse problem admits ambiguous solutions plagued by false and missed detections, while grid-based discretization locks predictions onto fixed lattice centers, chaining precision to prohibitively expensive grid refinement. We argue that CSIST unmixing should instead be informed and continuous. To ground this paradigm shift, we establish the first comprehensive open-source ecosystem for the field, comprising the large-scale CSIST-100K benchmark, a tailored metric suite, and the GrokCSO toolkit. Upon this foundation, we propose DISTA-Net++, which anchors a dynamic deep unfolding backbone with two synergistic mechanisms: a Count-Guided Prior that injects the global target count as an explicit semantic constraint to regularize the solution space, and a Continuous Coordinate Rectification that regresses off-grid offsets to decouple localization accuracy from grid resolution. Extensive experiments validate our paradigm: even under the most economical 3x division, DISTA-Net++ surpasses 7x-division state-of-the-art methods by 16.15% in CSO-mAP and 62.96% in count accuracy at merely one-sixth of their computation, demonstrating that unmixing precision need not be purchased with finer discretization. The complete ecosystem is available at this https URL.
[CV-14] oward Markerless Video-based Tremor Analysis: Objective Quantification of Pathological Tremor in Mouse Preclinical Models
链接: https://arxiv.org/abs/2609.18753
作者: Yota Koshimoto,Akihiro Tsukahara,Yasuhiro Moriwaki,Mariko Isogawa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Tremor is a movement disorder characterized by involuntary, rhythmic oscillations of body parts and is a hallmark of several neurological conditions, including Parkinson’s disease and essential tremor. Elucidating its underlying mechanisms relies heavily on mouse models, which offer genetic manipulability and translational relevance to human neural circuitry. Accordingly, these models are indispensable for studying tremor pathophysiology. So far, electromyography and accelerometers have been used as methods to quantitatively observe tremors in mice. However, these methods have several drawbacks, such as high costs and complex setups. In particular, the invasive surgical implantation of devices causes significant stress to the animals. Although RGB-based methods offer non-invasive and cost-effective alternatives, they often lack the sensitivity required to detect subtle tremors. Therefore, this paper addresses these challenges by achieving mouse tremor severity estimation using conventional RGB cameras only. To address the challenging task of isolating tremor-related vibrations while the mouse itself is also in motion, our pipeline incorporates segmentation-based pre-processing to extract the mouse region and a Tremor Score Estimation Module that captures subtle tremors with high sensitivity. In the experiments, we assessed tremors in unrestrained mice using a non-invasive method with two standard cameras. The results demonstrated a strong correlation with accelerometer measurements and confirmed that the method accurately captured the intensity-dependent characteristics of tremors. The project page is available at this https URL.
[CV-15] Geometry beneath the Waves: Dense Priors for Sparse-View Underwater 3D Gaussian Splatting SIGGRAPH
链接: https://arxiv.org/abs/2609.18737
作者: Harvey Caldeira,Haoran Wang,Guoxi Huang,Shaoyu Cai,Rachel Fu,Nantheera Anantrasirichai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to SIGGRAPH Asia Poster
Abstract:Underwater 3D reconstruction supports applications ranging from marine ecosystem monitoring and subsea inspection to underwater archaeology, education, and immersive visualisation. 3D Gaussian Splatting has made real-time photorealistic novel-view rendering practical, while underwater variants incorporate physically based image-formation models to separate medium effects from scene radiance. Their reconstruction quality, however, remains fundamentally limited by the geometry used for initialisation.
[CV-16] Mask IPL: Noise-Free Intrinsic Position Learning via Computation Graph Clipping for Event-Based Spike-Driven Tracking
链接: https://arxiv.org/abs/2609.18716
作者: Yimeng Shan,Malu Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spiking Neural Networks (SNNs) match the event-driven nature of event cameras and naturally extract spatiotemporal features. These properties have motivated a series of recent studies on event-based tracking with SNNs. Intrinsic Position Learning (IPL) acquires strong position information without introducing additional parameters, making it a mainstream approach for position encoding in event-based spike-driven tracking. However, the mechanism behind its effectiveness lacks systematic theoretical analysis. Moreover, our analysis reveals that IPL introduces noise in both forward and backward propagation. The former increases inference error, while the latter prevents parameters from converging to better solutions. This paper presents a systematic analysis of IPL and demonstrates that its effectiveness stems from the synergy between IPL and multi-stage convolution. The zero blocks in the joint tensor act as zero padding for convolution, and the resulting boundary effect propagates layer by layer through multi-stage convolution. Every parameter update is therefore driven by a gradient that perceives the relative displacement between template and search frames. Positional encoding added after the convolutional stage cannot provide this information. We further propose a simple Computation Graph Clipping method that applies a validity mask determined by the layout to the operations of every layer, making invalid regions equivalent to zero padding in both forward and backward propagation. This eliminates the noise without introducing additional parameters and makes the actual gradient coincide with the ideal gradient. We name the improved method Mask IPL. Without increasing parameters or computational cost, Mask IPL improves the AUC of the Tiny-scale tracker on FE108, FELT, and VisEvent, and consistently improves the Base-scale tracker as well.
[CV-17] Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging
链接: https://arxiv.org/abs/2609.18688
作者: Johannes Kaiser,Florian Braunmiller,Daniel Rückert,Georgios Kaissis
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:AI models for multimodal medical imaging must balance modality-specific specialization with cross-modal shared representations, a trade-off that pure Mixture-of-Experts (MoE) architectures currently fail to satisfy. Expert-based routing improves in-domain learning but may sacrifice cross-modal signals, which appear particularly important for rare (low-prevalence) pathologies in our experiments. To resolve this, we introduce Generalist-Specialist-MoE (GS-MoE), a two-branch (MoE) architecture that couples a cross-modal generalist model with distinct modality-specific specialists (experts) via domain-constrained feature fusion. On RadImageNet (1.35M images, 165 pathologies, three modalities), GS-MoE recovers detection of six low-prevalence pathologies on which every baseline scores F1 = 0, with per-class gains up to +0.60 F1. It attains this while even slightly exceeding dense and specialist-only MoE aggregate baselines (MCC 0.770), while using \sim53% fewer active parameters at inference than the strongest investigated dense model.
[CV-18] Video-Based Markerless Motion Capture for Clinical and Rehabilitation Biomechanics: A PRISMA-ScR Scoping Review of Validated Architectures Clinical Readiness and Emerging Methods
链接: https://arxiv.org/abs/2609.18667
作者: Florian Delaplace(LAMHESS, CHU),Elodie Piche(LAMHESS),Frédéric Chorin,Raphael Zory(IUF, LAMHESS)
类目: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注:
Abstract:Background… Video-based markerless motion capture promises movement analysis without the cost, space and skin-marker constraints of optoelectronic systems, with particular potential for clinical and rehabilitation settings. Whether validated pipelines yet deliver clinically acceptable biomechanics, and how they relate to the underlying computer-vision research, remains unclear. Methods. We conducted a scoping review following the PRISMA extension for Scoping Reviews, with a registered protocol and searches of PubMed, Scopus and IEEE Xplore (January 2015 to February 2026; the computer-vision scan was updated to July 2026). A dual-tier design paired a primary corpus of validated biomechanical studies with a complementary, curated and deliberately non-exhaustive corpus of emerging computer-vision work, used qualitatively. We charted study characteristics, pipeline architecture, validation methods and joint-angle accuracy. Results. We included 117 studies, most published from 2024 onward and conducted on healthy adults walking in a laboratory. Pipelines formed five architectural families across monocular and multi-camera modalities; most reported raw joint angles without biomechanical refinement. Sagittal lower-limb agreement clustered around 5 to 6\textdegree, generally short of clinical acceptability, while out-of-plane kinematics, kinetics, and pathological or older populations were rarely validated. Emerging computer-vision building blocks (foundation-model mesh recovery, differentiable inverse kinematics, video-based kinetics) were almost absent from validated studies. Conclusions. Video-based markerless capture is not yet interchangeable with marker-based systems for clinical joint kinematics, and it remains barely validated where rehabilitation needs it most: older and pathological populations, out-of-plane kinematics, and kinetics. Mapping this evidence gap onto emerging computer-vision advances, we propose hypothesis-generating design guidelines, not a validated method, to steer the next generation of pipelines toward accessible, clinically meaningful movement analysis.
[CV-19] GenStream: Semantic Streaming Framework for Generative Reconstruction of Human-centric Media ACM-MM2025
链接: https://arxiv.org/abs/2609.18634
作者: Emanuele Artioli,Daniele Lorenzi,Shivi Vats,Farzad Tashtarian,Christian Timmerer
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: 9 pages. Published at ACM MM 2025. Code: this https URL
Abstract:Video streaming dominates global internet traffic, yet conventional pipelines remain inefficient for structured, human-centric content such as sports, performance, or interactive media. Standard codecs re-encode entire frames, foreground and background alike, treating all pixels uniformly and ignoring the semantic structure of the scene. This leads to significant bandwidth waste, particularly in scenarios where backgrounds are static and motion is constrained to a few salient actors. We introduce GenStream, a semantic streaming framework that replaces dense video frames with compact, structured metadata. Instead of transmitting pixels, GenStream encodes each scene as a combination of skeletal keypoints, camera viewpoint parameters, and a static 3D background model. These elements are transmitted to the client, where a generative model reconstructs photorealistic human figures and composites them into the 3D scene from the original viewpoint. This paradigm enables extreme compression, achieving over 99.9% bandwidth reduction compared to HEVC for the continuous data stream. We partially validate GenStream on Olympic figure skating footage and demonstrate potential for high perceptual fidelity under minimal data. While acknowledging the significant computational costs shifted to the client and challenges in generalization, GenStream opens new directions in volumetric avatar synthesis, canonical 3D actor fusion across views, and personalized viewing experiences, laying the groundwork for scalable, intelligent streaming in the post-codec era.
[CV-20] VibeAvatar: Aligning Phonetic Kinematics and Human Aesthetics for High-Fidelity Talking Avatar Synthesis
链接: https://arxiv.org/abs/2609.18632
作者: Qilin Wang,Mingyu Li,Hao Tang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusion-based methods, existing approaches still struggle to jointly achieve accurate lip articulation, human-preferred motion aesthetics, and efficient inference. We observe that phonetic accuracy and motion aesthetics arise from fundamentally different sources and should be addressed at complementary stages rather than learned implicitly by a single generator. Based on this insight, we propose VibeAvatar, which disentangles these two objectives through a Phonetic Kinematics Adapter (PKA) that converts recognition-oriented speech features into phonetic-kinematic conditions at the conditioning stage, and an Aesthetic Motion Policy (AMP) that optimizes a flow-consistent stochastic sampling policy via Group Relative Policy Optimization (GRPO) at the post-training stage. With a lightweight flow-based motion generator operating in a compact 1D warp-based latent motion space, VibeAvatar achieves state-of-the-art results in articulation, aesthetics, and efficiency on both objective metrics and user studies, while generating a 10-second 512px video in under 10 seconds with only \sim 3GB VRAM.
[CV-21] FIVE-VLA: Fast and EffectIVE Autonomous Driving with Recurrent Action Memory
链接: https://arxiv.org/abs/2609.18623
作者: Kemal Oksuz,Alexandru Buburuzan,Yuhan Yao,Puneet K. Dokania
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:State-of-the-art vision-language-action models (VLA) for autonomous driving face critical limitations: excessive parameter counts, inefficient high-resolution image processing, and lack of temporal memory. We introduce Fast and EffectIVE VLA (FIVE-VLA) to address these through two key contributions. First, we employ an efficient vision encoder that processes high-resolution ( 448 \times 896 ) images while generating only 98 tokens, over 5\times fewer than existing approaches, and bypass text generation entirely for single-pass trajectory prediction. Second, we propose Recurrent Action Memory (RAM), a lightweight module that conditions action prediction on previous action tokens, providing temporal context critical for manoeuvres such as overtaking and emergency braking. With only 641M parameters, FIVE-VLA completes \sim 10% more routes without traffic rule infractions than the previous state-of-the-art VLA on the challenging Bench2Drive closed-loop driving benchmark. Non-reactive open-loop simulation on the large-scale real-world NVIDIA Physical AI AV dataset shows 10.2% and 7.7% lower collision-violation rates than SimLingo in single- and four-view settings, respectively. Additionally, FIVE-VLA runs at \sim 30 fps on an A100 and \sim 4 fps on a T4 GPU (proxy to an edge device), representing an 8-30 \times speedup over previous methods.
[CV-22] PULSE: Unlocking Practical Image Compression on Single-Thread CPU
链接: https://arxiv.org/abs/2609.18602
作者: Zhaoyang Jia,Tianyu Zhang,Zihan Zheng,Wenxuan Xie,Jiahao Li,Bin Li,Houqiang Li,Yan Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite recent progress in learned image compression, existing methods remain computationally expensive on resource-constrained hardware, particularly CPUs. We introduce PULSE, a practical codec that enables (1) low-latency decoding on diverse hardware platforms with an ultra-low-complexity 5.2 kMAC/pixel neural receiver, and (2) efficient bit-exact entropy coding with an integer linear CDF predictor and a meta prior. To recover compression performance under this tight budget, we introduce an agentic evolution process guided by heuristic probes that iteratively improves the architecture through human-LLM collaboration. PULSE decodes a 1080p image in 126 ms on a single CPU thread while achieving compression performance comparable to HM. After perceptual optimization, PULSE competes with larger perceptual codecs like MS-ILLM. Codes are at this https URL
[CV-23] On-the-Fly Homographies Calibration for Multi-Camera Tracking
链接: https://arxiv.org/abs/2609.18582
作者: David Voihanski,Mor Sinai,Ben Zion Bobrovsky
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Precise multi-camera tracking traditionally relies on rigorous 3D site calibration, yet this requirement is often operationally impossible in large-scale deployments. Privacy regulations frequently prohibit recording video for offline calibration; limited bandwidth precludes synchronizing high-resolution streams from hundreds of cameras; and covering immense physical sites with calibration targets is logistically infeasible. We present a multi-camera homography calibration system designed to overcome these barriers through “on-the-fly” geometric refinement. Starting from coarse manual homographies, we introduce a centroid-based projection optimization (PO) that continuously aligns the ground-plane geometry using live detection streams. Because PO operates asynchronously on already-transmitted, lightweight metadata, it adds zero computational latency to the real-time tracker. This allows the system to adapt automatically to camera movements or environmental changes without human intervention. This optimized geometry feeds a multi-camera bird’s-eye-view (BEV) tracker that fuses detections and unifies trajectories across zones. Crucially, by operating strictly on live anonymous metadata, our solution ensures a privacy-safe, zero-overhead, and resilient tracking pipeline that maintains global consistency in dynamic environments where static, recorded-video calibration is impossible.
[CV-24] Learning Where to Focus: Self-Supervised Multi-Scale ViTs for Histopathology
链接: https://arxiv.org/abs/2609.18578
作者: Anabel Stammer,Valay Bundele,Mehran Hosseinzadeh,Hendrik P.A. Lensch
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 5 figures, plus supplementary material. Accepted at DAGM GCPR 2026
Abstract:Pathologists diagnose diseases by first locating suspicious tissue and then examining it at higher magnification, whereas self-supervised vision transformers (ViTs) allocate the same spatial resolution to every image region despite diagnostic evidence being sparse and spanning multiple biological scales. Recent pathology foundation models have substantially improved representation quality by scaling training data and model capacity, but largely retain uniform tokenization. We instead investigate whether pathology representations can be improved by learning where to allocate spatial resolution during self-supervised learning. To this end, we propose CRAFT (Coarse-to-fine Region-Adaptive Feature Tokenization), a DINO-based framework that learns image-dependent mixed-scale representations by using self-supervised attention to selectively refine informative regions while preserving coarse context, together with a symmetric cross-scale regularization objective that encourages complementary coarse and fine representations. Across CAMELYON16, TCGA-Lung subtype classification, and TCGA-LUAD survival prediction, CRAFT consistently outperforms comparable-scale self-supervised methods while requiring lower inference computation. Despite using only a compact 22M parameter backbone trained on comparatively small pathology datasets, CRAFT remains competitive with, and often surpasses, substantially larger pathology foundation models.
[CV-25] Sim-to-Real Traffic Scene Understanding by Decoupling Semantics from Caption Generation with V-JEPA ECCV
链接: https://arxiv.org/abs/2609.18562
作者: Nguyen Hoai Thuong Bui,Thanh Nguyen Vo,Trinh Tra Giang Nguyen,Ha Duc Bui
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Winner of Track 2 at the AI City Challenge 2026, with the paper published at the ECCV conference 2026 (ECCV-W)
Abstract:Track 2 of the AI City Challenge 2026 requires both visual question answering (VQA) and traffic event description generation under a challenging synthetic-to real domain shift. Existing vision-language approaches often entangle semantic understanding with language generation, making them susceptible to hallucination and inconsistent reasoning across event phases. In this work, we propose a decoupled semantic understanding framework that first resolves predefined traffic questions into structured semantic facts and subsequently uses these facts to guide caption generation. A frozen V-JEPA encoder extracts predictive scene representations, while a lightweight Llama-based predictor produces answers for VQA queries. To improve reliability, we introduce a training-free structured refinement mechanism that exploits statistical priors, inter-question relationships, and temporal event consistency to correct prediction errors. The refined semantic facts are then provided to Qwen3-VL-8B to generate pedestrian and vehicle descriptions for each traffic event. Experimental results on the official 2026 AI City Challenge Track 2 benchmark show that the proposed method achieves 87.09% VQA accuracy and an overall S2 score of 60.0853, ranking first among all participating teams. These results demonstrate that predictive world representations combined with structured semantic refinement enable more accurate and reliable traffic understanding, leading to higher-quality lan guage generation.
[CV-26] CARA: Collision-Aware Resolution Adaptation for Multiresolution Hash Encoding Based Image Fitting ECCV2026
链接: https://arxiv.org/abs/2609.18554
作者: Linfeng Ye,Zhixiang Chi,Shayan Mohajer Hamidi,En-hui Yang,Konstantinos N. Plataniotis
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 32 pages, 12 figures, ECCV 2026
Abstract:Multiresolution hash encodings have recently enabled fast and high-fidelity implicit neural representations by storing multi-scale features in fixed-size hash tables along a geometric resolution schedule. However, the standard design is data-agnostic: different resolution levels receive identical hash-table capacity despite large differences in image frequency content. As a result, some levels experience severe hash collisions while others underutilize parameters, leading to inefficient capacity allocation. To address this issue, we propose Collision-Aware Resolution Adaptation (CARA), a method that assigns per-level resolutions by balancing the effective information load across hash levels. This adaptive allocation reduces capacity bottlenecks and improves parameter efficiency. In addition, we introduce an invertible pixel-shuffle transform that reduces hash load factors by redistributing spatial information, thereby mitigating collision-induced information loss without enlarging the hash tables. To support evaluation on extremely high-resolution data, we also curate, to the best of our knowledge, the first uncompressed whole-slide image dataset for academic research. Experiments on Kodak images, gigapixel natural images, and raw whole-slide images demonstrate that CARA consistently improves the fidelity-parameter trade-off. Our method matches state-of-the-art performance while using only 27.76% of the parameters, and achieves up to 6.11 dB PSNR improvement at comparable parameter counts. Code is provided in the supplementary.
[CV-27] HAP: A Hand-Driven Active Perception Framework for Egocentric Head Motion Prediction
链接: https://arxiv.org/abs/2609.18548
作者: Yunji Feng,Junyi Ma,Guanzhong Sun,Chenyang Xu,Hesheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Egocentric motion forecasting has primarily focused on hands and manipulated objects, leaving future human head motion comparatively underexplored. During manipulation, the head both redirects perception toward the target to acquire task-relevant evidence and coordinates with body and hand motion. We therefore formulate future six Degree of Freedom (6-DoF) head-motion prediction conditioned on observed hand motion and inferred target context, and propose HAP, a Hand-Driven Active Perception framework. HAP infers confidence for each target object from observed hand motion and object geometry. Then constructs a dynamic Predictive Target-Centric Amodal Occlusion Graph (P-TAOG) representing current and potential occlusion among candidate objects. Directed graph and causal temporal reasoning encode the evolving target conditioned perceptual state, which is fused with hand and head motion history. A horizon-wise gate then blends the learned trajectory with a constant velocity prior. We further introduce Bottle, an egocentric RGB-D dataset of object manipulation toward specified targets, with coordinated head and hand motion under changing target visibility. Experiments on the public dataset and Bottle show that HAP achieves lower head motion prediction errors than representative baselines, supporting the value of hand driven intention and dynamic occlusion reasoning for anticipating human head motion. Code will be released at this https URL.
[CV-28] STUNet-Fusion: Spatiotemporal Needle-Tip Localization in Ultrasound Video via Multi-Channel Motion Fusion
链接: https://arxiv.org/abs/2609.18546
作者: Chia-Chi Hsu,Chia-Hsuan Hsu,Che-Chou Shen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Needle-tip localization in ultrasound remains challenging because the needle may appear weak, discontinuous, or partially invisible, while imaging artifacts and anatomical structures can produce similar responses. To address this problem, we propose STUNet-Fusion, a spatiotemporal framework for needle-tip localization in ultrasound videos. The proposed method formulates the input as a tri-channel spatio-temporal fusion tensor, comprising grayscale appearance, grid-based motion feature, and raw frame difference. A shared ResNet-34 encoder extracts spatial features, ConvLSTM integrates temporal dependencies, and a U-Net decoder reconstructs a dense probability heatmap. The final coordinates are extracted via a soft-argmax operation to achieve sub-pixel localization accuracy. Experimental results demonstrate that this spatiotemporal fusion strategy significantly improves localization robustness compared to conventional baselines.
[CV-29] Accuracy- and Real-Time-Aware 4D Radar Preprocessing for Autonomous Driving Perception Systems
链接: https://arxiv.org/abs/2609.18542
作者: Woo-Jin Jung,Dong-Hee Paek,Jeong-Su Park,Seung-Hyun Kong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 7 figures, Transactions of the Korean Society of Automotive Engineers
Abstract:4D radar has emerged as a promising next-generation sensor for improving the robustness of autonomous driving perception systems because of its stable sensing capability under adverse weather conditions. However, deploying 4D radar in embedded environments with limited hardware resources requires radar-representation preprocessing that jointly considers perception accuracy, real-time performance, and computational complexity. This paper proposes a preprocessing framework for 4D-radar-based 3D object detection. First, Percentile-based 3D Shape Preservation (P3DP) extracts point clouds from radar tensors while preserving object-shape information and suppressing noise and false alarms. Second, Multi-frame-based Noise Point Discrimination using Kernel Density Estimation (MF-KDE) improves the density and reliability of sparse radar point clouds. Finally, Embedded \ NetScore (ENS) evaluates suitability for embedded deployment by jointly considering accuracy, real-time performance, adverse-weather robustness, and model complexity.
[CV-30] SVMemAgent : A Streaming Video Memory Agent for Query-Agnostic Online Frame Selection
链接: https://arxiv.org/abs/2609.18540
作者: Dohwan Ko,Ji Soo Lee,Pierce Chuang,Debojeet Chatterjee,Ashish Shenoy,Yichao Lu,Seungwhan Moon,Xin Luna Dong,Vikas Bhardwaj,Hyunwoo J. Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Most keyframe selection studies focus on offline settings, assuming access to the full video and query in advance. In contrast, real-world streaming scenarios require online frame selection under unknown video duration, without access to either the query or future frames during selection. To address this, we introduce Streaming Video Memory (SVMem), a compact and representative memory of previously observed content, updated continuously as the video stream unfolds. Building on this setting, we propose the Streaming Video Memory Agent (SVMemAgent), which dynamically maintains a memory by deciding at each timestep whether to replace an existing memory frame with the incoming frame or discard it. SVMemAgent is trained using Group Relative Policy Optimization (GRPO) with task-driven rewards derived from diverse question-answer pairs, implicitly exposing the policy to a distribution of queries during training so that SVMem retains generally informative frames at inference, when queries are unavailable. Experiments on both online and offline video benchmarks show that SVMemAgent consistently outperforms online frame selection baselines and achieves competitive performance with offline methods that assume access to the full video and query. Through task-driven rewards, SVMemAgent learns an emergent keyframe selection policy that prefers frames containing textual information, which may benefit downstream VideoQA tasks.
[CV-31] Learning from Distributed Eyes: Leverag ing Collaborative Perception for Automated Model Adaptation
链接: https://arxiv.org/abs/2609.18511
作者: Yanan Ma,Yihang Tao,Zhengru Fang,Zihan Fang,Yiqin Deng,Xianhao Chen,Yuguang Fang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 9 pages, 3 figures
Abstract:In autonomous driving, perception models often struggle to generalize to new environments due to domain shifts. While unsupervised model adaptation offers a feasible solution without labor-intensive manual labeling, existing methods that rely solely on the ego-vehicle’s data often lead to inferior pseudo-labeling performance. To address this critical issue, we propose LDE, Learning from Distributed ``Eyes", a novel framework that transforms collaborative perception (CP) into a source of high-quality supervision for model adaptation. This pseudo-labeling approach is hyperparameter-insensitive and relatively reliable, assuming CP often outperforms single-agent’s perception. However, naively implementing this approach encounters (1) the communication bottleneck of sharing rich features under time and bandwidth constraints, (2) the view discrepancy between the CP view and the learner’s Field of View (FoV), and (3) the unreliability even in CP-generated labels. To address these issues, we design an adaptation-oriented feature sharing mechanism that selectively transmits the most critical information for adaptation, an FoV filtering method that meticulously eliminates mismatched labels, and a curriculum learning strategy to progressively exploit pseudo labels. Extensive experiments on 3D object detection tasks demonstrate that LDE consistently outperforms both the pre-trained models and state-of-the-art unsupervised adaptation methods.
[CV-32] DiT-Garment: Garment Dynamics with Diffusion Transformers
链接: https://arxiv.org/abs/2609.18510
作者: Antoine Dumoulin,Laurence Boissieux,Joao Regateiro,Pierre Hellier,Stefanie Wuhrer
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present DiT-Garment to model dynamic 3D clothing over human body models in arbitrary motion. Unlike existing methods, DiT-Garment can animate garments with unseen designs and physical materials, while allowing for direct inference of deformations for any target pose. To achieve this, we leverage a 2D diffusion transformer architecture to learn 3D deformations in a 2D UV-space. As the result is non-deterministic, our generative model learns the distribution of possible outcomes. The template garment is represented as a 3D triangle mesh spatially aligned with a 3D human body model in a standardized pose. To work with different garment designs without the need of a common template or complex graph convolution operations, the diffusion transformer is conditioned on a 3D position map of the template, represented in UV-space, which allows to implicitly learn a deformation of the 3D space around the body in standard pose. Further conditioning on body motion and physical parameters allows to physically ground the model. We quantitatively and qualitatively evaluate DiT-Garment on both synthetic and real data. While only trained on synthetic simulations of automatically generated cloth designs, our method generalizes to captured and artist-made garment designs. Code and data are available for research purposes at this https URL.
[CV-33] Semantic-ITC: A Frame-wise Indoor Mobile Laser Scanning Dataset and Benchmark for Semantic Segmentation
链接: https://arxiv.org/abs/2609.18493
作者: Haiyang Wu,Muhammad Affan,George Vosselman,Ville Lehtola
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Semantic labels for indoor mobile laser scanning (MLS) frames remain largely absent from current point cloud semantic segmentation benchmarks, which mainly focus on reconstructed indoor scenes or outdoor LiDAR perception. This paper introduces Semantic-ITC, to the best of our knowledge the first public dataset and benchmark for frame-wise indoor MLS semantic segmentation. The dataset contains 52 indoor sequences, 79,108 MLS frames, and 1.23 billion labeled points collected in classrooms, corridors, meeting rooms, offices, and study areas. Labels are attached directly to measured LiDAR points in each frame using 16 semantic classes covering structural elements, furniture, room equipment, vegetation, and other indoor objects. Semantic-ITC preserves the sparse, non-uniform, and frame-wise sampling pattern of indoor MLS, making it distinct from scene-level reconstructed point clouds and mesh-based indoor datasets. The annotations are produced by a hybrid workflow that combines predictions from a visual foundation model applied to synchronized RGB images, structural information from BIM, and manual refinement, with the final labels assigned to the original LiDAR frames. A single-frame benchmark is provided, and the best baseline reaches 79.27% mIoU. Remaining errors are concentrated around object boundaries and ambiguous indoor classes, indicating the challenges of indoor MLS segmentation under sparse frame geometry and long-tailed class distributions. The dataset provides a public benchmark for evaluating semantic segmentation directly on measured indoor MLS frames and supports future studies on frame-wise indoor MLS semantic segmentation.
[CV-34] Learning A Unified Template for Gait Recognition ICCV2025
链接: https://arxiv.org/abs/2609.18490
作者: Panjian Huang,Saihui Hou,Junzhou Huang,Yongzhen Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICCV 2025
Abstract:"What I cannot create, I do not understand."Human wisdom reveals that creation is one of the highest forms of learning. For example, Diffusion Models have demonstrated remarkable semantic structure and memory in image generation, understanding, and restoration, which intuitively benefits representation learning. However, current gait networks rarely embrace this perspective, relying primarily on learning by contrasting gait samples under varying complex conditions, leading to semantic inconsistency and uniformity issues. To address these issues, we propose Origins with generative capabilities whose underlying philosophy is that different entities are generated from a unified template, inherently regularizing gait representations within a consistent and diverse semantic space to capture accurate gait differences. Admittedly, learning this unified template is exceedingly challenging, as it requires the comprehensiveness of the template to encompass gait representations with various conditions. Inspired by Diffusion Models, Origins diffuses the unified template into timestep templates for gait generative learning, and meanwhile transfers the unified template for gait representation learning. Especially, gait generative and representation learning serve as a unified framework for end-to-end joint training. Extensive experiments on CASIA-B, CCPG,SUSTech1K, Gait3D, GREW and CCGR-MINI demonstrate that Origins performs unified generative and representation learning, achieving superior performance.
[CV-35] Beyond Random Couplings: Contrastive Noise Alignment in Generative Flows
链接: https://arxiv.org/abs/2609.18488
作者: Lennart Wittke,Vinicius Azevedo
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 21 pages, 10 figures, 9 tables
Abstract:Diffusion and flow-matching models are typically trained by corrupting data through independently sampled Gaussian noise. While simple and scalable, this forward process induces arbitrary data-noise couplings, forcing the network to learn high-curvature transports between unrelated endpoints. Existing optimal-transport methods reduce this burden by reassigning fixed noise samples to data, but the source noise distribution itself remains passive. To address this, we introduce Contrastive Noise Alignment (CNA), a training-time method that creates dynamic, contrastive couplings by optimizing the noise representations directly. By modeling the noise batch as an interacting particle system, CNA employs a cross-modal InfoNCE objective to align noise particles with their paired data targets. To prevent spatial collapse, this alignment is regularized using an angular entropy term and a radial norm penalty. We show theoretically that this equilibrium asymptotically preserves Gaussian structures, maintaining tractability during inference. Empirically, CNA improves the alignment between noise and data, reduces flow curvature, and provides better generation quality with fewer required sampling steps. For few-step, pixel-space generation (2-4 NFEs), CNA reduces FID by over 50% compared to standard rectified flow, and by at least 24% against Optimal Transport baselines.
[CV-36] CADSplat: Sparse-View 3D Gaussian Splatting Aided by CAD Models for Robust Photorealistic Digital-Twin Reconstruction
链接: https://arxiv.org/abs/2609.18473
作者: Kristof Overdulve,Lode Jorissen,Nick Michiels
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:We present CADSplat, a framework that reconstructs photorealistic, geometrically accurate digital twins from sparse ( 15 views), wide-baseline posed images of an object by regularizing 3D Gaussian Splatting (3DGS) with an explicit CAD shape prior. Using such a prior requires finding a CAD model whose shape resembles the object depicted in the images and determining the pose of each camera relative to the object. We obtain both by matching segmented object silhouettes against silhouettes rendered from a CAD library and keeping the camera-to-object poses of the best-matching model. We then anchor 3D Gaussian primitives to the surface of the retrieved model and jointly optimize the 3DGS parameters, the camera-to-object registration, and a non-rigid deformation field to account for shape differences between the physical object and the CAD model. Across two real-world datasets, CADSplat outperforms unconstrained, few-shot, and mesh-texturing baselines and degrades gracefully to as few as 3 views. Our experiments show that most of the gain in rendering quality comes from how the splats are constrained—a fixed set of splats tied to a surface and moved by a single smooth deformation field—rather than from the CAD shape itself. The CAD model adds shape knowledge where views are scarcest, in the sparsest captures and on strongly self-occluded objects, and it places every camera in the object’s own frame. This enables applications beyond novel-view synthesis, such as markerless augmented reality registration, per-image object pose estimation, physical simulations, and the transfer of part labels from the design to the reconstruction.
[CV-37] GeoCond: A Conditioning-Aware Reliability Adapter for Feed-Forward 3D Reconstruction
链接: https://arxiv.org/abs/2609.18465
作者: David Ahmedt-Aristizabal,Mohammad Ali Armin,Russell Tsuchida,Lars Petersson
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Feed-forward 3D foundation models such as VGGT predict cameras, depth, and point maps in a single pass, but can fail silently under low overlap, low parallax, and extreme relative rotation. Stratified analyses over these factors show that these failures are governed by geometric conditioning and are poorly captured by native aleatoric confidence. We introduce GeoCond, a lightweight reliability adapter for frozen feed-forward 3D backbones. GeoCond reads the backbone’s predicted geometry and outputs pose-level uncertainty and a refinement gate. During training, it can be supervised by frame-permutation orbit variance, ground-truth pose error when labels are available, or cycle residuals from unlabelled independent pose graphs. At inference, the default head requires only one backbone pass and a small MLP. On VGGT, GeoCond improves out-of-distribution (OOD) AUSE (area under the sparsification-error curve; lower is better) from 0.32 to 0.20 over native confidence, transfers zero-shot to outdoor extreme-view scenes, and avoids the collapse caused by applying bundle adjustment uniformly. Across multiple backbones, cycle-distilled variants provide a ground-truth-free adaptation route, including cases where permutation variance vanishes on equivariant models. The same reliability signal supports gated refinement, pose-graph weighting, calibration, curation, and capture decisions. Reliable feed-forward 3D reconstruction requires not only predicting geometry, but also knowing when that geometry should be trusted.
[CV-38] CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models
链接: https://arxiv.org/abs/2609.18462
作者: Tianbin Liu,Jian Zhu,Taiyi Su,Jianjun Zhang,Chong Ma,Zitai Huang,Yi Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 13 pages, 2 figures
Abstract:FastWAM-style world action models enable efficient action-only inference, but generalize poorly under visual distribution shifts. Their reconstruction-oriented representations emphasize appearance-specific details, limiting generalization to unseen scenes and objects. Without observation history, the model also lacks temporal evidence for robustly identifying task-relevant state changes and motion in unfamiliar visual conditions. To address these limitations, we present the Causal Semantic World Action Model (CSWAM), which augments FastWAM with a causal semantic expert built on V-JEPA 2.1. V-JEPA provides temporally grounded representations of semantic state changes and motion with less dependence on appearance-specific details. The expert learns their future evolution from a sparse history of current and past observations and shares the history-derived context with both the video and action streams through causal attention. At inference, CSWAM conditions action denoising on the current video state and observed semantic history, retaining efficient action-only inference. We conduct simulation and real-robot experiments to evaluate generalization under distribution shifts. With embodied pretraining, CSWAM raises Randomized success on RoboTwin 2.0 Clean-to-Randomized transfer from 10.16% to 45.18%, a gain of 35.02 percentage points over FastWAM. Across two real-robot tasks and three OOD difficulty levels, CSWAM improves average success over FastWAM by 42.5 percentage points, from 27.5% to 70.0%.
[CV-39] DR.WILSS: Diffusion-Based Replay for Weakly Supervised Continual Semantic Segmentation
链接: https://arxiv.org/abs/2609.18444
作者: Leon Arthur Marx,Francesco Barbato,Matteo Caligiuri,Pietro Zanuttigh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MMSP 2026, 6 pages, 4 figures
Abstract:Weakly supervised class-incremental semantic segmentation (WILSS) aims to train a segmentation model over multiple steps, each introducing new concepts to be learned with only image-level supervision. We introduce this http URL, an innovative approach to address catastrophic forgetting in continual learning using diffusion-based generative replay. Our framework leverages language clues to guide the diffusion process, employing self-inpainting and regularization techniques to efficiently produce replay data, aiding the learning process. By generating high-quality replay data, the information from previously learned classes can be preserved during continual updates, a critical challenge in incremental learning scenarios. To further align the statistics of replay data with those of training samples, we apply LoRAs to the generative model. Experimental results demonstrate state-of-the-art performance across multiple benchmarks and generative architectures, while avoiding storage of training data and the use of additional resource-demanding tools during training. The proposed technique enables an optimal tradeoff between training complexity and inference-time accuracy, making this http URL a promising solution for real-world applications.
[CV-40] Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective ECCV2024
链接: https://arxiv.org/abs/2609.18432
作者: Panjian Huang,Yunjie Peng,Saihui Hou,Chunshui Cao,Xu Liu,Zhiqiang He,Yongzhen Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2024
Abstract:Extensive occlusions in real-world scenarios pose challenges to gait recognition due to missing and noisy information, as well as body misalignment in position and scale. We argue that rich dynamic contextual information within a gait sequence inherently possesses occlusion-solving traits: 1) Adjacent frames with gait continuity allow holistic body regions to infer occluded body regions; 2) Gait cycles allow information integration between holistic actions and occluded actions. Therefore, we introduce an action detection perspective where a gait sequence is regarded as a composition of actions. To detect accurate actions under complex occlusion scenarios, we propose an Action Detection Based Mixture of Experts (GaitMoE), consisting of Mixture of Temporal Experts (MTE) and Mixture of Action Experts (MAE). MTE adaptively constructs action anchors by temporal experts and MAE adaptively constructs action proposals from action anchors by action experts. Especially, action detection as a proxy task with gait recognition is an end-to-end joint training only with ID labels. In addition, due to the lack of a unified occluded benchmark, we construct a pioneering Occluded Gait database (OccGait), containing rich occlusion scenarios and annotations of occlusion types. Extensive experiments on OccGait, OccCASIA-B,Gait3D and GREW demonstrate the superior performance of this http URL is available at this https URL.
[CV-41] StrucPhysVideo: Learning Physical Dynamics from Structured Captions and Robot Actions
链接: https://arxiv.org/abs/2609.18430
作者: Awomo-WM Team:Enhui Ma,Kaiwen Guo,Tingrui Zhang,Wei Song,Yingshui Tan,Jianhua Xu,Tong Zhang,Kaicheng Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Modeling physical dynamics, including how objects move, interact, and change state, is central to video world models for embodied AI. We present StrucPhysVideo, a family of video world models that bridges physics-focused data curation with language- and action-conditioned prediction of scene evolution. Our data pipeline combines motion-aware video segmentation, quality and content filtering, and physical relevance verification with structured annotations of objects, materials, and temporally localized interactions. By disentangling camera motion from object behavior and explicitly describing contact, deformation, and state transitions, the pipeline provides supervision grounded in observable physical events. Building on these data, we introduce StrucPhysVideo-TI2V, a sparse Mixture-of-Experts (MoE) text-image-to-video model trained with a curriculum that progressively emphasizes physical dynamics while retaining general-domain video data. StrucPhysVideo-TI2V achieves state-of-the-art performance on Physics-IQ Verified, scoring 45.5% and outperforming Cosmos3-Super-Image2Video by 2.8 percentage points. Caption ablations across backbones further demonstrate the effectiveness of physics-focused supervision. We further extend StrucPhysVideo-TI2V to StrucPhysVideo-IA2V, an interactive image-action-to-video world model that predicts visual outcomes from robot end-effector commands. Action conditioning, causal autoregressive generation, and few-step distillation enable incremental robot rollouts with only four denoising steps. Together, StrucPhysVideo advances physical dynamics modeling from image- and language-conditioned video prediction toward action-driven interaction.
[CV-42] Vocabulary-Guided Gait Recognition NEURIPS2025
链接: https://arxiv.org/abs/2609.18413
作者: Panjian Huang,Saihui Hou,Chunshui Cao,Xu Liu,Yongzhen Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2025
Abstract:What is a gait? Appearance-based gait networks consider a gait as the human shape and motion information from images. Model-based gait networks treat a gait as the human inherent structure from points. However, the considerations remain vague for humans to comprehend truly. In this work, we introduce a novel paradigm Vocabulary-Guided Gait Recognition, dubbed Gait-World, which attempts to explore gait concepts through human vocabularies with Vision-Language Models (VLMs). Although VLMs have achieved the remarkable progress in various vision tasks, the cognitive capability regarding gait modalities remains limited. The success element in Gait-World is the proper vocabulary prompt where this paradigm carefully selects gait cycle actions as Vocabulary Base, bridging the gait and vocabulary feature spaces and further promoting human understanding for the gait. How to extract gait features? Although previous gait networks have made significant progress, learning solely from gait modalities on limited gait databases makes it difficult to learn universal gait features for practicality. Therefore, we propose the first Gait-World model, dubbed \alpha-Gait, which guides the gait network learning with vocabulary knowledge from VLMs. However, due to the heterogeneity of the modalities, directly integrating vocabulary and gait features is highly challenging as they reside in different embedding spaces. To address the issues, \alpha-Gait designs Vocabulary Relation Mapper and Gait Fine grained Detector to map and establish vocabulary relations in the gait space for detecting corresponding gait features. Extensive experiments on CASIA-B, CCPG, SUSTech1K, Gait3D and GREW reveal the potential value and research directions of vocabulary information from VLMs in the gait field.
[CV-43] Prosthesis-Aware 3D Human Pose Estimation: A Dataset and Benchmark for RSP Users ECCV2026
链接: https://arxiv.org/abs/2609.18406
作者: Yilin Wen,Kechuan Dong,Fumiya Suginaka,Ken Endo,Yusuke Sugano
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026. Project page: this https URL
Abstract:Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prosthesis users, this requires capturing both natural body joints and the geometry of the prosthetic device, a challenge that existing methods are not designed to address. Model-based estimators rely on body models trained on non-amputee individuals and cannot represent prosthesis geometry, while model-free methods lack body kinematic priors and are unreliable under occlusion. This challenge is particularly prominent for users of running-specific prostheses (RSPs), where the RSP has a complex curved geometry and moves dynamically during exercise. To fill this gap, we collect RSP3D, the first 3D dataset of RSP users, covering essential daily-life and exercise actions from participants with varied amputation conditions, using a multi-camera marker-based motion capture setup. We formally define the task of prosthesis-aware 3D pose estimation, evaluate representative methods in a zero-shot setting, and confirm their individual limitations. We further propose a hybrid baseline combining model-based body joint estimation with model-free RSP shape recovery, establishing a starting point for future research.
[CV-44] A Non-Linear Neuron Based Detection of Isolated Pixels in Binary and Grayscale Images using Contrast Sensitive Receptive Fields
链接: https://arxiv.org/abs/2609.18399
作者: Nassir Mohammad
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Identifying isolated points is important in image processing applications such as medical imaging, astronomy and quality control management. Other domains, such as cybersecurity, also present challenges that can be framed as image processing problems. One example of particular interest is the identification of anomalous single nodes in spatially organised networks where groups of nodes in different regions share similar feature values. This task can involve both binary and more complex grayscale images. However, existing methods face limitations: template matching is infeasible for grayscale images, while 2nd order derivative based methods are highly sensitive to noise and require user-specified thresholds. To overcome these issues, a novel method is proposed for detecting meaningful single-pixel deviations in images. This approach modifies and extends a neuron model, originally designed for anomaly detection, to operate on spatially diameter limited receptive fields that incorporate excitatory and inhibitory regions. The result is a method that is free from user-specified thresholds and parameters, and can be applied to both binary and grayscale images, providing an effective, robust and efficient solution.
[CV-45] MSR: Multiple Subject Reference for Video Generation
链接: https://arxiv.org/abs/2609.18393
作者: Guannan Li,Jiaji Chen,Jingyuan Liao,Yu Geng,Baolan Qiu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 4 figures. Model weights and inference workflows are publicly available
Abstract:Conditioning a video generator on multiple images requires preserving appearance while associating each reference with its intended role. We present MSR (Multiple Subject Reference), a slot-aware conditioning scheme for LTX-based video generation. Each reference image is independently encoded as a static clip and represented by a separate latent-token group. A compact Fourier-feature multilayer perceptron adds a numeric slot embedding, while slot-dependent temporal offsets modify the group’s rotary coordinates. The reference groups are prepended to noisy target tokens and serve as clean context during target-only flow-matching training. We implement this scheme through low-rank adaptation and release the resulting weights and inference workflows. Qualitative examples demonstrate compositions containing distinct characters and referenced environments in realistic and stylized scenes. Development observations suggest reduced reference confusion relative to an earlier continuous-reference baseline, while similar clothing, complex garments, and viewpoint changes remain challenging. We describe the conditioning mechanism, the retained training configuration, and the observed strengths and limitations of the released system. A supplementary audio-reference experiment adds voice conditioning while keeping the visual parameters frozen.
[CV-46] JigSync: Gauge-Resolved Synchronization for Jigsaw Reassembly under Unknown Piece Orientation
链接: https://arxiv.org/abs/2609.18379
作者: Soham Pahari,Antik Aich Roy,Ujjwal Bhattacharya
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Square jigsaw reassembly requires recovering the spatial arrangement of shuffled fragments from their visual content and pairwise relationships. While recent studies have made substantial progress, existing benchmarks typically assume that all fragments are provided upright, reducing reassembly to a permutation problem. We study the generalized problem in which each fragment may also have gone through an unknown rotation. For this setting we establish a gauge-unobservability theorem: the minimum of the weighted least-squares objective is exactly invariant under a uniform global rotation of arbitrary magnitude, so no residual-based criterion can recover the global orientation. The theorem further identifies how the issue of global orientation can be resolved: an orientation anchor estimated from the content of a single fragment, lying outside its scope, suffices. To address the above, we propose JigSync, which attains 63.8% and 31.8% absolute accuracy (AA) on GAP-3 and GAP-5, respectively, the highest reported on both, while additionally recovering a rotation per piece that neither benchmark requires. We release JigSync, a degradation protocol that sweeps shape, erosion, photometry, grid size, and rotation independently.
[CV-47] Online Multi-Camera 3D Tracking via ID Prediction over Recurrent Sparse Queries
链接: https://arxiv.org/abs/2609.18363
作者: Pragyan Shrestha,Haruto Nakayama,Atom Scott
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Online multi camera 3D tracking must maintain scene global identities across synchronized views, yet query-based trackers carry these identities only implicitly in the instance bank, where they fragment upon query interruption. We present an online architecture that recovers association accuracy by predicting IDs explicitly over recurrent sparse queries. An outside-in Sparse4D detector fuses calibrated views into world frame 3D detections while propagating a sparse query bank, and a causal MOTIP ID decoder associates detections against a finite trajectory memory. We adapt MOTIP’s relative-ID prediction and recycled slot runtime to globally fused 3D observations, and introduce metric spatial gating and proximity based newborn recovery. On the official 2026 AI City Challenge Track 1 test set, our method raises HOTA from 29.63 with native instance bank identities to 38.01, primarily through an AssA increase from 20.83 to 31.10, and ranks third on the public leaderboard. Full-sequence validation over all 9,000 frames of each scene shows that decoupled ID training improves HOTA over native identities, whereas continuing detector training alongside the detached ID objective produces scene-dependent gains and losses.
[CV-48] Visual Input and Its Framing Affect Attribute-based Descriptions Produced by Large Vision-Language Models
链接: https://arxiv.org/abs/2609.18345
作者: Xiaomeng Wang,Martha Larson,Zhengyu Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision-language models (LVLMs) are commonly used with only a single text prompt as the input, or plus an image. In this paper, we demonstrate that when the image exists, even if the text prompt is not about the specific instance (but only the concept it belongs to) in that image, the response would still be affected. For example, when the text prompt only asks for the attribute descriptions of a dog breed, an image depicting a specific dog from that breed would shift the response. Further, how the specific instance is framed in that image would determine towards which the response shifts. Detailed analyses also reveal that in the response, physical terms increase from 18% for text-only to 45% (40%) for subject-focused (subject-in-situation) framings. Overall, the unexpected effects of visual cues on LVLMs highlight the need to understand the presence of an image and its framing when evaluating the robustness of LVLMs.
[CV-49] Pose2Muscle: Structured Spatio-Temporal Decoding for Discrete Muscle Activity Estimation from Human Pose
链接: https://arxiv.org/abs/2609.18336
作者: Yuepeng Chen,Jiehong Shi,Kaili Zheng,Boyi Zhang,Chenyi Guo,Ji Wu,Xiangling Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Muscle activity is fundamental to human movement, and understanding its patterns is critical for injury prevention and rehabilitation. Conventional muscle activity monitoring relies on specialized sensors such as surface electromyography, which limits its practicality for long-term real-world use. Existing studies suggest that muscle-related information can be inferred from human pose. However, the substantial gap between externally observable pose and internal muscle activation, limits the accuracy and generalization of current approaches. In this study, we propose Pose2Muscle, a pose-driven framework for discrete muscle activity estimation without requiring sEMG signals at inference time. Instead of directly regressing continuous sEMG signals, Pose2Muscle reformulates muscle estimation as a structured prediction problem over discrete muscle activity states, yielding a more stable and interpretable target space. The framework combines multi-scale spatio-temporal attention to capture motion patterns at complementary spatial and temporal scales with a directed acyclic graph-based decoder that maintains multiple candidate muscle-state hypotheses and performs structured trajectory inference over time. To support this task, we construct PoseEMG-43, a synchronized pose-sEMG dataset containing 2,992 movement instances from 43 daily-life actions performed by 14 participants. Experiments show that Pose2Muscle consistently outperforms representative retrieval- and pose-based baselines. It achieves an Adjacent-level Accuracy of 86.36% and a Pearson correlation coefficient of 0.8821 under the Random Split, and 63.97% and 0.6795, respectively, under the Subject-Level Split. These results demonstrate the feasibility of inferring structured muscle-state patterns from human pose and suggest the potential of Pose2Muscle for muscle-aware movement analysis when direct physiological sensing is impractical
[CV-50] PDA: Field-Aligned Planning and Scene-Adaptive Insertion in Remote Sensing ICML2026
链接: https://arxiv.org/abs/2609.18329
作者: Xianchi Dong,Yingyan Hou,Chao Ren,Wanxuan Lu,Zihan Wei,Hongfeng Yu,Yixiao Wang,Chubo Deng,Xian Sun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Extended journal version of our ICML 2026 paper “Plan, Decouple, Assimilate: Physics-Aware Object Insertion in Remote Sensing Imagery”
Abstract:Remote sensing recognition is often constrained by scarce observations of rare targets and costly annotations, making realistic synthetic augmentation particularly valuable for few-shot and long-tailed scenarios. Object insertion provides an efficient way to increase target diversity while preserving authentic background scenes, but realistic insertion in overhead imagery requires the generated target to adapt coherently to its surrounding environment. To this end, we propose PDA++, a unified environment-aware object insertion framework organized as Plan, Decouple, and Assimilate. Planning determines scene-compatible poses through an affordance field that combines geometric clearance with structure- and scale-aware cues. Decoupling introduces a pose-conditioned background that provides precise spatial guidance together with target-scene context, allowing the reference object to preserve its identity while adapting to the target observation. This construction also naturally provides pixel-level masks for segmentation augmentation. Assimilation further improves local coherence by aligning multi-scale texture distributions through optimal transport. On the optical benchmark, PDA++ achieves a whole-image FID of 6.28 and improves average few-shot recognition mAP50 by 17.69 points, corresponding to a 28.8% relative gain over the real-data baseline. On SAR imagery, it improves ship detection by 4.10 mAP50 points and remains effective under cross-dataset transfer and amorphous-target insertion. Code is available at this https URL.
[CV-51] Can MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model
链接: https://arxiv.org/abs/2609.18323
作者: Haoyu Zhao,Zihao Zhao,Tianyu Deng,Ziqin Xu,Zihao Zhang,Xudong Wang,Jinxiang Guo,Chen Gao,Ziyi Ye,Yeying Jin,Jiaxi Gu,Zuxuan Wu,Shuicheng Yan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 14 figures
Abstract:Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework. Its unified architecture raises a fundamental question: Can multimodal alignment improve the model’s world reasoning, and what new evaluation paradigms do omni-modal inputs enable? To investigate this question, this work introduces a comprehensive evaluation framework organized around four complementary dimensions of physical world reasoning. Unlike existing evaluation frameworks for video generation and world models, which are often constrained by limited input modalities and evaluation settings where prompts closely match the target video content, our evaluation is specifically designed to exploit the multimodal inputs of Omni-Model. We construct a diverse set of novel tasks that require models to integrate complementary information across modalities. Specifically, we consider four scenarios, including implicit prompts paired with multiple frames, audio-image, prefix-videos, and audio-video inputs. Every single modality provides only partial evidence about the underlying event, requiring the model to jointly reason over the complementary semantic cues to infer latent event states and future dynamics. Across 517 evaluation instances, MiniMax-H3 achieves an overall success rate of 41.97%. Video-based Decision Reasoning yields the highest success rate at 56.00%, while Audio-based Disambiguation Reasoning is the weakest, reaching only 27.40%. These results indicate that effective multimodal integration remains key to fully exploiting the benefits of diverse input modalities. The project is available at this https URL.
[CV-52] Visual Autoregressive Priors for RAW-to-sRGB Image Signal Processing ECCV2026
链接: https://arxiv.org/abs/2609.18302
作者: Tailai Chen,Xiaotong Luo,Yuan Gao,Xin Jin,Wenjun Zeng
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Image and Video Processing (eess.IV)
备注: Accepted at ECCV 2026 Workshop on Low-Level Vision Frontiers (LoViF). 13 pages, 4 figures
Abstract:RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect spatial alignment and missing camera metadata. This paper presents, to the best of our knowledge, the first application of visual autoregressive (VAR) next-scale prediction over a discrete image codebook to the RAW-to-sRGB ISP task. We adapt a frozen 1.10,B-parameter VAR backbone for RAW-conditioned ISP with only 32.93,M trainable parameters (2.99%), and propose a frequency-decomposed color loss that separately supervises low-frequency tone via wavelet LL cosine similarity and chromatic edges via detail-band \ell_1 . On the Zurich RAW-to-sRGB benchmark, the method improves PSNR-Y from 21.31 to 21.89,dB and reduces LPIPS from 0.276 to 0.218 on the full 1,204-image test set. Diagnostic experiments show that the VAR prior preserves structure well, but continuous color transfer remains the dominant bottleneck: oracle affine correction recovers 3.8,dB, while learned color heads yield marginal gains.
[CV-53] Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TM
链接: https://arxiv.org/abs/2609.18279
作者: Victor Bercy,Martyna Poreba,Michal Szczepanski,Samia Bouchafa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Extended version of this https URL , to be published in Communications in Computer and Information Science (CCIS), Springer. Codes are available at this https URL
Abstract:Vision Transformers (ViTs) have achieved state-of-the-art performance across a range of computer vision tasks, mainly thanks to the self-attention mechanism. However, its complexity, increasing quadratically with the number of tokens, remains the major obstacle to ViT efficiency and deployment at scale. Token merging reduces this cost by aggregating redundant tokens. Yet existing methods are typically evaluated within a single architecture, leaving open whether their effectiveness stems from the merging mechanism itself or from the specific decoder they are paired with. We extend Graph-Guided Token Merging (G2TM), a single module inserted early in a ViT-based network, beyond its original Segmenter setting. We evaluate G2TM across three semantic segmentation frameworks (Segmenter, SETR, EoMT) and three decoder families (Linear, Transformer-, convolution-based), as well as standard ViT image classification. Our results show that G2TM’s behavior and accuracy-efficiency trade-off are consistent across every tested architecture for a given backbone size, indicating that its effectiveness is a property of the encoder rather than the decoder. G2TM also generalizes well to image classification, achieving an even smaller degradation in accuracy compared to semantic segmentation. We further find that G2TM’s optimal hyperparameters, resulting in a consistent drop in GFLOPs of 22-47% and an increase in throughput by up to 74% for segmentation models on ADE20K dataset, depend primarily on the backbone’s pre-training recipe and on the target dataset, rather than on the decoder choice.
[CV-54] MS-RFD: Multi-Signal Release Frame Detection in Hammer Throw from Reconstructed 3D Trajectories
链接: https://arxiv.org/abs/2609.18260
作者: Ahmed Endris Hasen,Nikolaos Passalis,Tomi Vanttinen,Jenni Raitoharju
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 4 figures
Abstract:Recent advances in artificial intelligence and computer vision are reshaping sports performance analysis by enabling automated detection, tracking, and performance analysis. In hammer throw, performance is strongly determined by the kinematic conditions at release, particularly release speed, release angle, and release height. However, identifying the release instant from video typically requires manual frame-by-frame inspection, which is subjective and cumbersome in real-world training scenarios. In this paper, we present a fully automatic multi-signal release frame detection (MS-RFD) method for hammer throw using reconstructed 3D hammer trajectories. The proposed method integrates four complementary kinematic signals: speed dynamics, angular velocity transition, radial distance relative to the rotation center, and post-release trajectory linearity. These signals are fused to score and verify candidate release frames. MS-RFD is evaluated through the throwing-distance estimation error obtained from the release parameters estimated at the detected frame. An ablation study analyzes the contribution of each signal and compares alternative candidate selection strategies. The results show that speed dynamics and radial expansion provide the strongest signals for release frame detection, while angular velocity and post-release linearity provide smaller refinements.
[CV-55] Evolving Error States: Failure-Aware Progressive Repair for Ultrasound Lesion Segmentation
链接: https://arxiv.org/abs/2609.18256
作者: Ziliang Wang,XuJiang Tang,Lu Yuting,Weixin Xu,Yongqiang Zhao,Ying Fu,Kehua Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reliability under sparse and heterogeneous failures remains a fundamental challenge for medical image segmentation. High average accuracy can conceal a small set of structurally distinct and clinically consequential errors. Existing post-hoc correction methods alleviate this problem, but typically estimate false-positive and false-negative corrections from the same fixed prediction. This ignores the dynamic evolution of error states and limits the correction of complex cases. Inspired by iterative error feedback in structured prediction, we propose Failure-Aware Progressive Repair (FAPR). FAPR represents the current segmentation mask as a dynamic failure state and models each repair operation as a state-transition operator. Each accepted correction forms a new prediction state for subsequent error diagnosis and repair, enabling later operations to adapt to preceding changes. Conditional routing selectively activates necessary state transitions, while failure replay exposes the model to rare error states. By keeping the base segmentor frozen, FAPR preserves its established segmentation capability while improving difficult cases. Across three public ultrasound lesion segmentation benchmarks, FAPR improves mean DSC by 1.52%. On the very-hard subsets of BUSI and TN3K, the average gain reaches 13.77%.
[CV-56] Unified Response Geometry for Structured Pruning
链接: https://arxiv.org/abs/2609.18239
作者: Kaixiang Shu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Structured pruning is commonly formulated as ranking individual channels, although channel responses can be complementary or cancel through downstream mixing. Motivated by these response interactions, we formulate pruning as the selection of a subset with large joint response capacity, followed by a separate functional realization step. Our unified response geometry maps each candidate set to (M(D,R)=D^1/2RD^1/2) and uses its determinant together with Schur-greedy residuals to select non-redundant coordinates. The same construction yields two information-conditioned instances: an unlabeled instance based on activation covariance, and a task-conditioned instance that combines activation and gradient variance for response scale with gradient correlation for complementarity. To convert the selected subset into an executable network, we fold predictable removed responses into successor weights through ridge compensation and recalibrate batch-normalization statistics, without fine-tuning the network. On ImageNet ResNet-50, the unlabeled instance reaches (65.4%) and (53.9%) Top-1 accuracy at 30% and 40% deletion, versus (59.8%) and (43.1%) for strength-only selection; the task-conditioned instance reaches (67.7%) and (56.3%) under the same protocol. A six-family screen shows architecture-dependent behavior, with positive relative contrasts in several convolutional and expansion-layer settings and clear boundary cases in windowed attention. These results support response geometry as a conditional principle for structured pruning, with its benefit determined jointly by the observed response and the architecture in which that response is realized.
[CV-57] WISE: A Lightweight Weakly-Supervised Model for Onboard Fire Smoke Detection and Localization
链接: https://arxiv.org/abs/2609.18227
作者: Sha Lu,Yu Sun,Liang Zhao,Jixue Liu,Lin Liu,Jiuyong Li,A. K. Qin,Alejandro Mousist,Stefan Peters
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted manuscript. 35 pages, 4 figures
Abstract:Wildfire smoke detection from satellite imagery is critical for early warning and rapid response. For onboard satellite deployment, detection systems must operate under strict memory and latency constraints while providing spatially informative outputs for downstream decision-making. Existing tile-level classification methods are computationally efficient but lack spatial localization, whereas pixel-level segmentation approaches provide detailed masks yet are typically too computationally demanding for real-time onboard execution. To address this gap, we propose WISE (Weakly-supervised Inference-efficient Smoke Extraction), a deployment-oriented framework for onboard fire smoke detection and localization. WISE leverages only tile-level annotations through a teacher-student distillation strategy, where an offline teacher provides soft spatial supervision to a lightweight WISE-Student optimized for efficient onboard inference. The student jointly predicts tile-level smoke presence and smoke probability maps within a single forward pass, enabling spatially informative detection under strict computational constraints. WISE was evaluated through in-orbit execution aboard the ISS-mounted IMAGIN-e payload. Three model variants achieve average inference times of 0.10 s, 0.14 s, and 0.26 s per tile, indicating near-real-time per-tile inference within onboard resource limits. Ground-based experiments on Landsat 5 and Landsat 8 imagery further indicate effective detection and spatially informative localization. The best-performing variant achieves a mean tile-level F1 score of 0.964 and a mean pixel-level F1 score of 0.750 across 10 runs, while containing only 0.12M parameters and requiring approximately 3 GFLOPs. Together, these results indicate that WISE is a practical candidate for low-latency wildfire smoke monitoring from space under onboard resource constraints.
[CV-58] A Lightweight CNN Integrated Compact Convolutional Transformer for Multi-Scale Feature Learning and reducing computational complexity for breast cancer mammography image detection and classification
链接: https://arxiv.org/abs/2609.18212
作者: Md Taimur Ahad(Department of Management North South University, Dhaka, Bangladesh),Ainuddin Ahmed(Department of Management North South University, Dhaka, Bangladesh)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Over the years, Convolutional Neural Networks (CNNs) have demonstrated strong capability in cancer detection and classification using medical images. However, CNN-based models often struggle to capture long-range contextual dependencies. In such scenarios, integrating Compact Convolutional Transformer (CCT) architectures after the CCT layer allows CNN-extracted features to reshape into compact patch tokens using a CCT tokenizer, followed by the addition of positional embeddings to preserve spatial structure. Using 5-fold cross-validation, the model was tested on 3 sets of breast cancer mammography. With only 250,435 parameters, the model achieved 99%-100% accuracy across 3 datasets, indicating robust generalization. Explainable AI (XAI) was integrated into the model to explain the breast cancer classification process to enhance clinical trust. The results indicate that the proposed framework is suitable for computer-aided diagnosis systems, particularly in resource-constrained clinical environments. The novelty of the proposed CNN-integrated CCT overcomes the limitation of CNN’s gradient degradation in the last layers by integrating convolutional tokenization with transformer-based learning. Lighter than ViT, which is effective in capturing long-range dependencies, the model has also proven efficient in breast cancer classification by capturing long-range dependencies among breast tissue regions.
[CV-59] Understanding Dynamic Scenes at Gigapixel Scale: Wide-Area Spatio-Temporal Perception from UAVs
链接: https://arxiv.org/abs/2609.18210
作者: Yuhang Zhu,Meiyi Zhu,Yunkai Dang,Zhangnan Li,Yuxuan Wang,Wenbin Li,Hongbing Pan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 5 figures, 3 tables
Abstract:UAV-borne imaging has advanced from megapixel to gigapixel sensors, shifting aerial perception from recognizing individual targets to understanding entire dynamic scenes. We characterize this demand as Wide-area Spatio-temporal Scene Understanding (WSTU), which requires wide-area coverage, per-target resolution, and temporal continuity at once, a combination existing datasets lack. To fill this gap, we introduce an ultra-High-resolution (12768x9564) Airborne Remote-sensing Dataset (HARD) annotated at three levels for object detection, multi-object tracking, and scene-level visual question answering. Ultra-high-resolution imagery raises per-frame processing time to seconds. At that scale latency can no longer be ignored in evaluation. Thus, we propose a latency-aware metric for multi-object tracking called streaming-HOTA (s-HOTA). Extensive baseline experiments show how ultra-high-resolution processing reshapes each task. For detection, the end-to-end pipeline affects accuracy and speed as much as the detector itself does. For tracking, high latency charges the association axis far more unevenly than the detection axis, and association is where pipelines diverge. As a result, the pipeline that performs best offline can lose its lead under s-HOTA. For VQA, vision-language models remain weak at cross-frame identity binding and cannot transfer their single-frame gains to it. Together these findings show that the baselines we evaluate fall short of WSTU. HARD provides the data and the systematic baselines to advance it.
[CV-60] CapMap-MS-TTA: 3rd Place Solution for the MUMU Track of the 8th LSVOS Challenge at ECCV 2026
链接: https://arxiv.org/abs/2609.18206
作者: Chengfeng Qiu,Kaifeng Wei
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The MUMU track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge requires a single unified multimodal model to jointly solve image tagging (Task A), open-vocabulary object detection (Task B), and English captioning (Task C) under strict resource constraints (=0.5B parameters and =8 GB peak GPU memory). We present CapMap-MS-TTA, a training-free submission built on Microsoft Florence-2-base (~231M parameters), combining caption keyword mapping with multi-scale flip test-time augmentation. Task C uses the native DETAILED_CAPTION pathway with length/token sanitization. Task A maps the same detailed caption into the official quality/scene/event vocabularies via an expanded keyword lexicon with whole-word matching and a lightweight expand-hints stage. Task B runs Florence-2 open detection (OD) with multi-scale and horizontal-flip test-time augmentation (TTA), followed by label-aware non-maximum suppression (NMS). Without fine-tuning, the system improves our reproduced Florence-2 baseline from 15.16 to a best public score of 16.4815, and ranks 3rd on the final MUMU leaderboard.
[CV-61] Energy-Regularized Imitation Learning for Force- and Work-Aware Robotic Manipulation ECCV2026
链接: https://arxiv.org/abs/2609.18164
作者: Toshiki Otani,Hiromu Taketsugu,Norimichi Ukita
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026 Workshop on Force-Grounded, Cross-View Articulated Manipulation
Abstract:This paper studies energy-aware manipulation as a physically grounded learning problem. We define a joint-space mechanical-work proxy from joint torque and angular displacement, and train a differentiable energy predictor that estimates this work from robot states and actions. The predictor converts a non-differentiable simulator-side physical quantity into a differentiable regularizer for fine-tuning a pretrained manipulation policy. We instantiate the framework with RVT-2 on RLBench and evaluate 12 manipulation tasks involving object contact, articulated motion, placement, pushing, and sweeping. The proposed fine-tuning reduces the average mechanical work from 208.8J to 204.4J (i.e., 2.1% reduction), while the mean task success rate also increases slightly from 86.2% to 86.9%. These results show that work-aware policy optimization can suppress physically inefficient motion without requiring an explicit differentiable dynamics model.
[CV-62] Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization
链接: https://arxiv.org/abs/2609.18139
作者: Xuyu Fan,Qi Ming,Zhu Han,Liuqian Wang,Siyuan Cao,Xiaohan Zhang,Xudong Zhao,Mingjing Zhao,Yuhan Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cross-view object geo-localization (CVOGL) locates a target in satellite imagery using drone or street-view queries. Existing methods train separate detectors for each viewpoint, leading to parameter redundancy and impeding cross-view knowledge sharing. Moreover, top-ranked satellite candidates are often visually similar, so visual appearance and categorical labels alone are insufficient to resolve such ambiguity. To address these, we propose MVLGeo, an efficient framework designed to unify multiple viewpoints and reduce model redundancy. First, we introduce environmental contextual text from the query view as cues to distinguish visually similar candidates via Vision-Language Reranking (VL-Rerank). Second, we design a multi-view Mixture-of-Experts architecture (MV-MoE) with a shared encoder and view-specific experts to reduce redundancy and promote knowledge sharing, while cross-view contrastive learning aligns their representations for consistency. Third, we introduce an adaptive elliptical prior (ESAM-Prior) as auxiliary positional encoding for anisotropic geometric perception. Extensive experiments on the CVOGL benchmarks confirm that MVLGeo, as a unified model for multiple query viewpoints, achieves state-of-the-art performance, demonstrating robustness to input degradation and generalization across viewpoints. Code and models will be available on GitHub to facilitate future work.
[CV-63] Stealthy in Semantics Antagonistic in Space: Attacking Visible-Infrared Object Detectors via Object-Level Misalignment
链接: https://arxiv.org/abs/2609.18133
作者: Yueqi Zhu,Qi Ming,Guo Cheng,Yongkang Zhang,Feiran Liu,Juan Fang,Jiahuan Zhou,Jiangmeng Li,Yuhan Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visible-infrared object detectors are used for robust perception under challenging illumination and weather conditions. Current physical attacks apply conspicuous patches to spatially aligned target regions, which are noticeable to human observers. Meanwhile, most of these methods only perturb the appearance within the aligned region, without explicitly targeting the correspondence between modalities or the fusion process. In this paper, we propose CamoShift, an adversarial framework for visible-infrared object detection. By combining visual camouflage with object-level infrared shifting, CamoShift breaks cross-modal spatial alignment and disrupts fusion. Specifically, the Semantic Camouflage Module (SCM) generates a stealthy camouflaged patch that can be attached to the host object and maintains its effectiveness in the infrared branch through an RGB-IR adapter. The Object-level Spatial Decoupling Module (OSDM) shifts the infrared target evidence in a scale-aware manner, so as to break object-level correspondence and disrupt cross-modal fusion. Then, the Harmonic Adversarial loss (HarAdv loss) further balances attack strength and visual stealth during optimization. To the best of our knowledge, we are the first to target both visual stealthiness and attack success in visible-infrared object detection. Extensive experimental results show that CamoShift achieves a superior balance between attack effectiveness and visual stealth. Code and models will be available on GitHub.
[CV-64] MCLC-NET: Multimodal Continual Learning for Leaf Counting
链接: https://arxiv.org/abs/2609.18129
作者: Ruchi Bhatt,Pratibha Kumari,Shreya Bansal,Vedant Agnihotri,Dwarikanath Mahapatra,Mukesh Saini
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Leaf counting is an important task in plant phenotyping for monitoring plant growth and estimating crop yield. Most existing methods rely on RGB images, but their performance is often affected by occlusion, lighting variations, and other real-world challenges. Additional modalities, such as depth and thermal images, can provide useful complementary information. However, multimodal leaf counting remains underexplored. Also, many existing methods assume that all training data are available simultaneously, which is impractical in real agricultural settings, where data is collected over time from multiple sources. To address these challenges, we propose MCLC-NET, a multimodal continual learning framework for leaf counting. It learns tasks sequentially using a memory-based strategy with a memory buffer to retain important samples from previous tasks. We also introduce MMLC, a real-world multimodal leaf-counting dataset designed for a domain incremental scenario (DIS) in CL. It contains RGB, depth, and thermal images collected across different crop types under varying environmental conditions, arranged in three orderings: crop-wise, time-wise, and mixed. Experimental results, averaged over three random seeds, demonstrate that MCLC-NET consistently outperforms existing methods across all three task orderings, achieving the lowest AMSE of 0.675 \pm 0.027, 0.542 \pm 0.069, and 0.745 \pm 0.057, respectively.
[CV-65] PRISM: Predictive Representation of Interaction Style and Motion for Social Robot Navigation ECCV2026
链接: https://arxiv.org/abs/2609.18125
作者: Bo-Han Chen,Hiromu Taketsugu,Norimichi Ukita
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: ECCV 2026 Workshop on Agent in World
Abstract:Humans often observe others before interacting and adjust their behavior accordingly. Robot navigation in crowds, however, often represents pedestrians mainly by observed geometric states, leaving individual differences in interaction tendencies implicit. We propose PRISM (Predictive Representation of Interaction Style and Motion), a framework that infers interaction traits from passive observations of human-human interactions. PRISM encodes human trajectories into a continuous ordinal latent space with a transformer encoder trained by Rank-N-Contrast loss, and pairs each inferred trait with a temporal-stability score supplied to the navigation policy. In randomized crowd simulations, PRISM reduces collision rates over the geometry-only baseline and yields small improvements in navigation-time and path-length metrics. These results suggest the utility of passive latent-trait inference for social navigation in dynamic crowds.
[CV-66] Aligned Consensus Teaching for Label-Efficient Oriented Object Detection in Weakly-Aligned Visible-Infrared Imagery
链接: https://arxiv.org/abs/2609.18124
作者: Qi Ming,Xiaxin Yuan,Jiahuan Zhou,Jiangmeng Li,Xudong Zhao,Zhanchao Huang,Juan Fang,Shaoguang Huang,Aleksandra Pizurica
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visible-infrared object detection (VIOD) detects objects with oriented bounding boxes from paired visible and infrared images. Existing methods depend on costly dual-modality annotations. Semi-supervised learning can reduce this burden, but extending it from single-modal detection to VIOD is challenging. In the practical image-pair-level setting considered here, only a few pairs are labeled in both modalities, while the rest are completely unlabeled. This limited supervision creates three challenges: (i) too few labeled boxes for robust cross-modal alignment; (ii) pseudo-label errors caused by branch-wise misses accumulate during self-training; and (iii) tail-class annotations become critically scarce as the labeling budget decreases. We propose Aligned Consensus Teacher (ACT) for label-efficient VIOD in this setting. Its Cycle-Consistent Region Alignment (CRA) combines cycle consistency and sparse anchors with reliability-weighted regional matching. Cross-Modal Consensus Mean-Teacher (CMC-MT) forms consensus pseudo labels under pair-preserving views to recover branch-wise misses and supervise unlabeled pairs. Text-Guided Cross-Modal Instance Augmentation (TG-CMIA) uses a vision-language scene prior to compose tail-class instance pairs while preserving RGB–IR offsets. To the best of our knowledge, ACT is the first framework to study semi-supervised VIOD under this image-pair-level setting. Experiments on DroneVehicle and VEDAI show consistent gains across annotation ratios. With 10% labeled pairs on DroneVehicle, ACT reaches 94.3% of the mAP obtained by the same detector under full supervision. Code and models will be available on GitHub to facilitate future work.
[CV-67] Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perception IROS
链接: https://arxiv.org/abs/2609.18100
作者: Hannan Ejaz Keen,Muhammad Moazam Fraz,Karsten Berns
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at Sim2Real and Classical Control: From Rigorous Theory to Data-Driven Robotics - IROS Workshop 2026
Abstract:Synthetic data can reduce the cost of collecting and annotating training data for robotic perception, but generating sensor observations that preserve the characteristics relevant to downstream perception remains challenging, particularly for sonar imagery. In this work, we investigate whether conventional image-fidelity metrics adequately reflect the downstream perception performance of GAN-generated synthetic sonar data. We employ a Pix2Pix conditional generative adversarial network with four discriminator configurations characterized by different receptive fields: PixelGAN, PatchGAN-16, PatchGAN-70, and ImageGAN. The models are trained using sonar imagery from two datasets and evaluated using conventional image-fidelity metrics, including Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). To complement these pixel-level measures with task-oriented evaluation, YOLOX-S, YOLOX-L, and Faster R-CNN detectors are trained exclusively on real sonar imagery and subsequently evaluated on the GAN-generated images using identical test samples and annotations across all discriminator configurations. The results reveal a discrepancy between image-fidelity and downstream object-detection performance: the configuration achieving the best SSIM, PSNR, and MSE does not consistently yield the best detection performance. In particular, PatchGAN configurations achieve strong downstream detection results despite not achieving the highest pixel-level similarity scores. These findings suggest, for the datasets and models considered, pixel-level image-fidelity metrics alone may not consistently capture the task-relevant realism of synthetic sonar observations and motivate the use of task-aware evaluation for synthetic sensor data intended for robotic perception.
[CV-68] Mask 2D-3D: Adaptive Dual-Masked Autoencoder Network for Image-to-Point Cloud Registration
链接: https://arxiv.org/abs/2609.18088
作者: Zhixin Cheng,Jiacheng Deng,Xiaotian Yin,Baoqun Yin,Richang Hong,Tianzhu Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Detection-free methods for image-to-point cloud registration are prone to erroneous correspondences caused by domain and modality discrepancies, limited sensitivity of feature extractors, and the presence of non-overlapping regions. The Masked Autoencoder (MAE) has shown strong performance in visual representation for images and point clouds. It may be helpful to apply this approach to image-to-point cloud registration, a task that requires unified feature extraction and accurate cross-modal correspondences. Standard MAE’s random masking may overlook key regions due to limited camera views, reducing registration effectiveness. To address this, we propose the Intermodal Dual-MAE Framework (ID-MAE) with a Similarity-based RL Masking Strategy (SRLM), which adaptively masks informative positions by leveraging cross-modal similarity and reinforcement learning, thus narrowing the modality gap. Our method enhances cross-modal representation learning by enforcing representation consistency during feature extraction, thereby enabling more reliable 2D-3D correspondence estimation. Experiments on RGB-D Scenes v2 and 7-Scenes benchmarks show that our method achieves state-of-the-art performance in image-to-point cloud registration.
[CV-69] Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models
链接: https://arxiv.org/abs/2609.18084
作者: Shahram Najam Syed,Arthur Jakobsson,Prayuj Sachdev,Jeffrey Ichnowski
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 9 pages, 7 figures, 7 tables
Abstract:Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to every network region as if every region requires equal adjustment. This paper tests that assumption on five architecturally diverse VLAs (OpenVLA-OFT, \pi_0 , SmolVLA, DTP, Octo; 93M-7B parameters). Measuring per-region adaptation cost as normalized parameter displacement under region-isolated fine-tuning reveals an adaptation spectrum in which appearance shifts concentrate cost in the vision encoder, instruction shifts in the language backbone, and novel-object shifts in the vision encoder together with the action head, across all five architectures. To exploit this structure, we introduce a pipeline that observes, diagnoses, allocates, and adapts. From ten unlabeled target observations and without fine-tuning, the diagnostic estimates per-region cost by combining reference-free gradient and Monte Carlo Dropout signals with a Centered Kernel Alignment score against a cached source reference; the allocator converts the estimates into variable-rank LoRA adapters under a parameter budget and freezes well-calibrated regions; and standard LoRA fine-tuning trains the resulting adapters. The diagnostic ranks regions within each deployment at a median Spearman of 0.91, and the allocation matches or exceeds uniform LoRA at every budget we tested on LIBERO and CALVIN. On a physical xArm-7, the pipeline matches full fine-tuning under an instruction-wording shift with 0.04% of its trainable parameters, and on five held-out scenes evaluated without retraining it leads every baseline, with 11-23 successes of 30 rollouts against 8-18 for the strongest parameter-efficient baseline at equal or larger budgets and 2-11 for full fine-tuning. These results suggest that adaptation cost in VLAs is structured enough to measure before fine-tuning begins.
[CV-70] vidax: A Unified JAX Framework for Video Generative Models on Accelerator Meshes
链接: https://arxiv.org/abs/2609.18077
作者: Congyue Deng
类目: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注:
Abstract:Open-source video generative models ship almost exclusively as PyTorch/CUDA reference implementations. This leaves Cloud TPU pods without a production-ready inference path, despite offering large, cost-effective accelerator memory pools ideal for long-sequence spatiotemporal attention. We present vidax, an open-source JAX/Flax inference engine and zero-copy PyTorch-to-JAX weight translator for modern video generation architectures. vidax covers a diverse set of spatiotemporal models — including Diffusion Transformers, omnimodal Mixture-of-Transformers, 3D VAEs, text encoders, and native samplers — with zero PyTorch dependency in the execution path. The framework unifies 1D tensor parallelism with DeepSpeed-Ulysses sequence parallelism on a single JAX sharding mesh, integrates TPU flash-attention kernels, and implements per-layer weight offloading to support reference resolutions that exceed single-device memory. We benchmark compile times, latency, and peak memory utilization on TPU v4-8 hardware, and document real-world numerical bugs surfaced during checkpoint translation. vidax is released open-source as a baseline for JAX and TPU video generation research.
[CV-71] GeoCueFormer: Geometry-Guided Wavelet Representation and Prediction-Cued Dual-Stage Decoder for Underwater Semantic Segmentation
链接: https://arxiv.org/abs/2609.18069
作者: Xian Wu,Xinjin Li,Yiliu Xu,Yining Liu,Yong Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 5 figures, conference paper
Abstract:Underwater semantic segmentation is essential for marine ecosystem monitoring, yet remains challenging due to severe visual degradation. Light absorption and scattering often lead to color shifts, low contrast, and blurred boundaries, making shallow detail features unreliable. Existing underwater segmentation methods improve RGB feature aggregation or boundary prediction, but still lack an explicit mechanism to distinguish structure-related details from degradation-induced responses. To address this limitation, we propose GeoCueFormer, a lightweight framework that combines geometry-constrained frequency enhancement with prediction-cued refinement. GeoCueFormer performs stage-specific wavelet enhancement on hierarchical encoder features to complement shallow boundary details while preserving deep structural semantics. A depth-derived spatial gate constrains shallow frequency enhancement toward geometry-consistent regions, and a prediction-cued dual-stage decoder further refines ambiguous high-resolution features. GeoCueFormer obtains 82.23% and 73.04% mIoU on SUIM and DUT, respectively. Under comparable model complexity and standard benchmark settings on SUIM and DUT, it achieves SOTA performance while maintaining a favorable accuracy-complexity trade-off. These results show that distinguishing structural details from degradation-induced interference is more effective for underwater segmentation.
[CV-72] Finder: Agent ic Closed-Loop Object Finding for Embodied Grounding
链接: https://arxiv.org/abs/2609.18058
作者: Shixiong Xu,Zhiyuan Chen,Song Ding,Rui Luo,Xiaowei Liang,Dongxu Miao,Zhiying Du
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Finding the object referred to by language in a partially observed 3D scene is a core capability for embodied agents. Existing approaches either couple object search with online exploration, which can be costly when relevant observations have already been captured, or query pre-built open-vocabulary maps and scene graphs in a static, one-shot fashion. We present Finder, an agentic closed-loop object-finding primitive for embodied grounding. Instead of treating grounding as passive retrieval from a fixed scene representation, Finder maintains a typed loop state that links query-conditioned planning, scoped evidence gathering, candidate verification, and accept/continue/abort control. When evidence is incomplete or ambiguous, the loop can redirect subsequent perception and comparison rather than simply returning the top retrieved object. On open-vocabulary embodied Object Retrieval in Habitat/HM3D and real-world RGB-D scenes, Finder improves the averaged 1m success rate by 15.75 points over strong baselines. The same primitive also transfers to sequential object grounding and embodied object-centric question answering, improving spatial and temporal localization without changing the inner grounding protocol. Project page: this https URL.
[CV-73] Position Anchor Tuning: Towards Efficient Adaptation of Pre-Trained Point Cloud Transformers
链接: https://arxiv.org/abs/2609.18056
作者: Zheng Liu,Xin Gao,Jinchao Zhu,Gao Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 figures, 7 tables
Abstract:Parameter-efficient fine-tuning (PEFT) has recently emerged as a pivotal research direction for adapting pre-trained point cloud transformers to diverse downstream tasks. Although existing methods achieve excellent fine-tuning performance with high parameter efficiency, they ignore inference efficiency. To tackle this problem, a novel PEFT method termed position anchor tuning (PAT) is proposed in this paper. As multi-head attention (MHA) and feed-forward network (FFN) are computation-heavy blocks in pre-trained transformers, PAT decreases their computational cost through token aggregation-expansion pairs. Each pair comprises a token aggregation module (TAM) and a token expansion module (TEM). For MHA and FFN blocks, TAMs extract representative tokens from their input tokens based on position anchors in 3D space. These extracted tokens, rather than the original input tokens, are processed by the blocks, thereby reducing the number of tokens involved in computation. Then, TEMs propagate the learned representations back to the original input tokens. Since TAMs are solely responsible for capturing task-specific representations, base-sharing low-rank adaptation (BSLoRA) is further introduced to enable them to learn such representations effectively with only a small number of trainable parameters. Extensive experiments on widely used benchmarks demonstrate that PAT performs comparably to state-of-the-art methods while incurring significantly lower computational overhead and fewer trainable parameters.
[CV-74] CoAtNet-DeepMoE: A Convolution-Attention Hybrid with DeepSeek Mixture-of-Experts for Parameter-Efficient Tomato Disease Classification
链接: https://arxiv.org/abs/2609.18038
作者: Md Nadim Mahamood,Md Arif Shahriar,Md Shafi Ud Doula,Kamrul Hasan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 8 tables, 6 figures
Abstract:The world population is growing rapidly, and technology is improving in parallel. Meeting the huge demand for food for these 7 billion people not only depends on increasing food production but also on reducing food loss. Crop losses due to disease affect both the food supply and the financial and economic stability of a country. Tomatoes are among the top food-producing crops globally, and a significant portion of this production is lost due to disease. People have used Machine Learning techniques for feature extraction and early diagnosis of tomato diseases, and nowadays, Deep Learning-based models are widely used for disease recognition. However, most existing models are highly parameter-intensive, which increases the time required for training and inference. As a result, while lightweight models are more suitable for user-friendly applications, they often show a reduction in performance. To balance performance and model size, we propose CoAtNet-DeepMoE, a Convolution-Attention hybrid architecture for rich feature extraction, further enhanced with a DeepSeek Mixture of Experts to substantially reduce the number of parameters without sacrificing accuracy. We evaluate our model on both balanced and imbalanced datasets from Kaggle and PlantVillage, demonstrating robustness and achieving 99.80% accuracy, 99.80% precision, 99.80% recall, and 99.80% F1-score on Kaggle, and 99.83% accuracy, 99.85% precision, 99.76% recall, and 99.80% F1-score on PlantVillage, representing state-of-the-art performance with only 2.47M parameters. The source code will be available at this https URL.
[CV-75] SetPlanner: A Lightweight Plug-in Point-Set Planner for Frozen SAM ICASSP2027
链接: https://arxiv.org/abs/2609.18037
作者: Dawei Yan,Yuezhe Yang,Menglan Ruan,Chunfeng Yang,Yudong Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 2 figures, 3 tables. Submitted to IEEE ICASSP 2027
Abstract:Segment Anything Models provide reusable priors, yet they require user prompts and cannot support fully automatic instrument segmentation. Automatic prompting is difficult for thin, articulated, reflective, and partly occluded tools, where several configurations can be valid. We formulate automatic prompting as lightweight point-set planning and isolate the point source under a frozen pathway. To this end, we present SetPlanner, a 1.52M-parameter plug-in point-set planner for frozen SAM. The plug-in preserves SAM’s point-prompt interface and enables reuse across backbones. SetPlanner plans complete unordered K-point sets from geometry-aware targets with a permutation-aware conditional flow. SAM decodes eight candidates; their consensus readout yields a ground-truth-free prediction. Across three endoscopic datasets, SetPlanner wins all six transfer routes over a LoRA-adapted system. Under our frozen-pathway protocol, SetPlanner reaches 0.934 Dice on Kvasir-Instrument and recovers 96% of a 44.4-point localization gap, while candidate disagreement ranks low-Dice cases at AUROC 0.969.
[CV-76] IRIS: Implicit Rendering Matters for Pose-Free Novel View Synthesis
链接: https://arxiv.org/abs/2609.18034
作者: Wenyu Li,Sidun Liu,Peng Qiao,Yong Dou,Tongrui Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM Multimedia 2026
Abstract:Novel view synthesis from unposed multi-view images remains challenging, as the model must jointly learn scene representations and camera parameters without pose supervision. Existing approaches largely fall into two extremes: implicit latent-space rendering is flexible and easy to optimize, but often yields weakly grounded camera estimation; explicit 3D representations provide stronger geometric grounding, but introduce heavier parameterization and more fragile optimization. In this paper, we present IRIS, a fully self-supervised framework that provides a practical middle ground between these two paradigms. Instead of decoding free latent tokens or reconstructing fully explicit 3D primitives, IRIS represents the scene as a latent neural field and renders novel views by querying this field under self-predicted cameras. Specifically, projected features from reference views are aggregated at sampled 3D points to form point-wise latent features, which are then composed along target rays for rendering. This design preserves the flexibility and optimization stability of implicit modeling, while introducing stronger geometric structure than unconstrained latent rendering. Extensive experiments show that IRIS achieves strong novel view synthesis quality with competitive pose accuracy under fully self-supervised learning. Our project page: this https URL
[CV-77] Newer Is Not Fairer: Gender Stereotyping in Text-to-Image AI Across Model Generations
链接: https://arxiv.org/abs/2609.18007
作者: Shesh Narayan Gupta,Nik Bear Brown
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Text-to-image generative models are widely used in professional and creative settings, yet how they represent gender across occupations – and whether newer models are fairer – remains poorly understood across multiple generations. We evaluate gender representation across 20 occupations, 5 prompt templates, and 4 Stable Diffusion model generations (SD 1.5, SD 2.1, SDXL, SD 3 Medium), generating 8,000 images with n = 100 per occupation-model cell (5 prompts x 20 images), and classifying all with DeepFace. Across the 8,000 open-source images, 76.4% show male subjects (95% CI [75.1%, 78.7%], p 2.2 x 10^-16, Benjamini-Hochberg adjusted). More strikingly, 57.6% of images for historically female-coded occupations show male subjects (raw p = 3.43 x 10^-22, BH-adjusted p = 1.71 x 10^-21). All nine significant tests reported in this paper survive BH correction across 10 tests. When compared against U.S. Bureau of Labor Statistics workforce data, models underrepresent women by 20-46pp on average, with particularly large deviations for near gender-balanced occupations: scientist (48% female in BLS, 82-99% male in model outputs) and cleaner (46% female in BLS, 80-92% male in outputs). Model generations do not improve steadily: bias worsens from SD 1.5 to SDXL before partially recovering in SD 3 Medium. A preliminary comparison with GPT-image-1 on five occupations suggests lower bias than open-source models, though the practical effect is small (Cramer’s V = 0.080) and the comparison is exploratory. No model achieves gender parity.
[CV-78] EDCT-Bench: Uncovering Faithfulness Gaps in VLMs via Explanation-Driven Counterfactual Testing
链接: https://arxiv.org/abs/2609.17953
作者: Sihao Ding,Santosh Vasa,Aditi Ramadwar,Thomas Monninger
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence they cite. We present Explanation-Driven Counterfactual Testing (EDCT), an intervention-based protocol that extracts visual concepts cited in a model’s explanation, applies verified minimal edits to them, and tests whether the resulting answer and explanation remain consistent with the edited image. Using this protocol, we create EDCT-Bench, a comprehensive benchmark spanning three complementary domains: knowledge-intensive visual question answering (OK-VQA), safety-critical driving (DriveLM), and 3D spatial reasoning (3DSRBench). Across the evaluated VLMs, EDCT reveals substantial faithfulness gaps, with models frequently producing responses inconsistent with verified visual changes. Finally, our fine-tuning study suggests that EDCT-generated counterfactuals provide high-impact training signals.
[CV-79] Rapid Loss of the Sierra Nevadas Largest Trees Driven by Fire
链接: https://arxiv.org/abs/2609.17925
作者: Fabien H. Wagner,Dan J. Dixon,Christopher W. Woodall,Mayumi C. M. Hirye,Felipe Saad,Griffin Carter,Ricardo Dalagnol,Lorena Alves,Cynthia Creze,Stephen C. Hagen,Zhihua Liu,Christopher Mihiar,Adugna Mullissa,Le Bienfaiteur Sagang Takougoum,Bryan Shaddy,Yan Yang,Dafeng Zhang,Sassan Saatchi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 40 pages, 13 figures
Abstract:Large trees disproportionately contribute to biomass storage, habitat structure, and ecosystem functioning. However, their distribution and health dynamics remain poorly quantified at a regional scale. Here, a deep learning model (U-Net-ID) and canopy height models derived from sub-meter aerial imagery from 2020 were used to delineate all individual trees with crown area \geq 100 m ^2 across the Sierra Nevada Floristic Province. The model was trained using more than 3.3 million synthetic tree crowns and achieved a median Intersection over Union (IoU) of 0.602 when validated against an independent dataset of 20,273 crowns. A total of 6,515,705 large trees were mapped, occurring across approximately 78.7% of the Sierra Nevada Floristic Province. The spatial distribution of large trees showed associations with elevation, temperature, and precipitation. Using Sentinel-2 time series from 2020 to 2025, tree health dynamics were characterized by extracting spectral trajectories for each crown and applying BFAST breakpoint detection algorithm combined with a disturbance classification framework to identify mortality, disturbance, and recovery trajectories of individual trees. Wildfires, estimated from CAL FIRE fire perimeters, were identified as the dominant driver of large-tree mortality, killing 10% of all large trees in the Sierra Nevada, with mortality strongly concentrated during the extreme 2020-2021 fire seasons.
[CV-80] Audio for Sports Highlight Detection: A Comparative Empirical Study
链接: https://arxiv.org/abs/2609.17923
作者: Hao Xu,Meenakshi Sarkar,Vishnu Raj,David Gunawan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ACM Multimedia Workshop on Multimedia Content Analysis in Sports (MMSports) 2026
Abstract:Sports highlight detection aims to identify the most exciting and meaningful moments from long sports videos. While existing methods often emphasize visual or visual-language representations, sports videos contain rich audio cues, including commentator speech, crowd reactions, whistles, ball impacts, and referee calls. In this work, we revisit the role of audio in sports highlight detection and ask a simple question: how far can audio alone go? We construct lightweight audio-only baselines using pretrained audio representations and compare them with visual-only and audio-visual methods on the SV-Highlights benchmark. Surprisingly, our audio-only GRU baseline achieves strong performance and outperforms several existing audio-visual methods under our supervised evaluation setting. Furthermore, a simple audio-visual fusion baseline achieves the best performance across all metrics, indicating that audio and visual cues provide complementary information. To better understand the contribution of audio, we conduct source-separated analysis and show that vocal/commentary audio is more informative than background-only audio, while their combination performs best. We also analyze interpretable audio cues and find that highlight clips exhibit higher RMS loudness, peak loudness, and mid-frequency energy than non-highlight clips, although substantial distribution overlap indicates that loudness alone is insufficient. Our findings suggest that audio is an underexplored but highly informative modality for sports highlight detection and should be treated as a primary signal rather than merely an auxiliary cue.
[CV-81] Face-voice Association across LAnguages and Gender (FLAG) 2027 Challenge Evaluation Plan ICASSP
链接: https://arxiv.org/abs/2609.17913
作者: Marta Moscati,Swapnil Khandoker,Muhammad Saad Saeed,Shah Nawaz,Fatima Noor,Rohan Kumar Das,Mubashir Noman,Junaid Mir,Muhammad Haroon Yousaf,Khalid Malik,Markus Schedl
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Grand challenge accepted at ICASSP
Abstract:Face–voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead to a performance deterioration when the model has to identify a multilingual speaker or distinguis same-gender speakers. To investigate these issues, we introduce the Face-voice Association across LAnguages and Gender (FLAG) 2027 Challenge. The challenge formulates face–voice association as a cross-modal verification task: given a voice, identify the speaker’s face from a gallery'' of faces consisting of the speaker's face and a set of negative samples. Models are evaluated on identities not present in the training data (unseen’‘) and both for languages present or absent from the training data (heard'' and unheard’‘). Two evaluation settings are used to test models’ reliance on gender: a standard, unconstrained and a gender-constrained one, where the latter uses a same-gender gallery. The performance of existing, baseline models in these settings reveals that models performance degrades under language shifts and in gender-constrained settings, highlighting the need to foster the development of models that capture identity-specific aspects beyond language and gender. The challenge provides a benchmark dataset, pretrained baseline models, and an evaluation framework to advance face–voice association.
[CV-82] Zing-0.5: Toward Playable Worlds with Real-Time Joint Action and Text Control
链接: https://arxiv.org/abs/2609.17909
作者: Mingyang Chen,Shengdong Chen,Xiaoxiao Fu,Bosheng Gong,Haoyuan Guo,Bowen Li,Jiawen Li,Kejun Li,Tianpeng Li,Yin Liu,Haoze Sun,Zeyang Tian,Meng Wang,Xinmiao Wu,Jiangqiao Yan,Zining Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 19 pages, 8 figures. Authors listed alphabetically by surname. Project: this https URL ; Code: this https URL ; Models: this https URL ; Serving: this https URL
Abstract:We introduce Zing-0.5, a 5B autoregressive world model designed for playability: users can explore generated worlds, influence unfolding events, and respond to the resulting feedback through joint keyboard and online text control. Our approach brings together three technical contributions: (1) Unified action and text conditioning, combining magnitude-aware keyboard inputs with temporally aligned text instructions and jointly annotated videos to learn navigation and event control within the same sequence; (2) Event-scale supervision for incremental generation, using a segment-level teacher trained on connected multi-prompt videos to supervise a block-level causal student through distribution-matching distillation; and (3) Low-cost real-time interaction, combining four-step generation with context-preserving streaming to support 832 x 480 inference at 24 FPS at an estimated server rental cost of approximately USD 0.009 per stream-minute. Zing-0.5 achieves an overall score of 81.0 and a consistency score of 88.5 across 158 WBench Navigation cases. A joint-control demonstration shows a text-directed event change during continued navigation without restarting generation. We release the model weights, inference code, and Zing-SGLang serving implementation to support further work on playable generated worlds.
[CV-83] Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?
链接: https://arxiv.org/abs/2609.17882
作者: Seung Jae Lieu,Diego Morra,Chiara Cadoni,Wonseop Song,Martina Mazzarello,Carlo Ratti
类目: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 29 pages, 4 figures, 9 tables
Abstract:An important component of urban accessibility, particularly for wheelchair users and people with reduced mobility, is sidewalk compliance with measurable requirements. We test whether effective width, longitudinal slope, cross slope, and pavement condition can be assessed reliably from pedestrian-level imagery using vision-language models (VLMs). We present the first application of sampling-based conformal prediction (CP) for VLM-based accessibility assessment. We evaluate four VLMs on 514 sidewalk images from Seoul, South Korea, with field-measured ground truth. Conformal calibration attains the nominal 90% coverage for all models and attributes, but the calibrated regions differ in informativeness. Effective width yields the most informative estimates, with a mean interval half-width of about 1.0 m for the best model. Since every model overestimates width, asymmetric calibration shortens the intervals by up to 33% at unchanged coverage. Longitudinal slope is marginally informative, cross-slope intervals are too wide to resolve regulatory thresholds, and pavement-condition sets degenerate to all five grades (A-E) for three of the four models. Uncalibrated intervals from raw sampling dispersion cover only 17-47% of field-measured values at a nominal 90% level. Among the images with the most self-consistent responses, these intervals miss the field-measured value in up to 96% of cases. Response self-consistency is therefore not evidence of accuracy, and sampling dispersion cannot be interpreted as uncertainty until it has been calibrated against field-measured ground truth. No quantitative attribute reaches the precision required for general compliance assessment, but CP identifies from calibration data alone which attributes can support screening of segments far from the thresholds. We release the annotated pedestrian-level images and their corresponding field-measured attribute values.
[CV-84] Lumen: Parameter-Efficient Alignment of Pretrained Vision and Language Encoders for Zero-Shot Computational Pathology
链接: https://arxiv.org/abs/2609.17868
作者: Kiarash Tajbakhsh,Abdelrahman Faqieh,Michael Jopiti,Javier Garcia-Baroja,Philipp Zens,Branislav Zagrapan,Yuri Tolkach,Martin D. Berger,Aurel Perren,Bastian Dislich,Inti Zlobec,Amjad Khan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pathology vision-language models are commonly built by pretraining or fine-tuning large encoders on paired image-caption data. We asked whether a pathology vision-language model can instead be assembled by parameter-efficient alignment of frozen unimodal foundation models, leaving their pretrained representations untouched. Here we present Lumen, which aligns frozen Virchow2 and BioMedBERT backbones using rank-4 adapters and projection heads, training only 0.40% of the total parameters on the public QUILT-1M corpus. Across nine public zero-shot patch benchmarks, Lumen achieved the highest mean chance-corrected balanced accuracy, 0.546 versus 0.461 for the strongest baseline (paired difference 0.086, 95% CI 0.042-0.136). On lymph-node metastasis detection, Lumen reached an AUROC of 0.964 (95% CI 0.956-0.971) on 4,214 held-out internal slides and 0.955 (95% CI 0.942-0.966) on 2,368 slides across nine external cohorts and six organs. At the internally calibrated threshold, it outperformed all vision-language baselines, with a balanced accuracy of 0.909 (95% CI 0.896-0.923) internally and 0.915 (95% CI 0.902-0.929) externally. Lumen performed competitively across the evaluations, with the exception of cross-modal retrieval, where it ranked third behind CONCH and PathGen-L/14. Fully fine-tuning both encoders gave Lumen no consistent benefit over low-rank adaptation, although it improved retrieval. Aligning frozen unimodal foundation models therefore yields strong and transferable performance at patch and slide level while training only a small fraction of the parameters.
[CV-85] RoboVAD: A Large Cross-Domain Evaluation Benchmark for Anomaly Detection in Robotic Arm Manipulation Videos
链接: https://arxiv.org/abs/2609.17843
作者: Alexandru-Bogdan Dura,Sebastian Balmus,Radu Tudor Ionescu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注:
Abstract:Video anomaly detection (VAD) is an actively studied task, having wide applications in typical scenarios such as public surveillance and road traffic safety. The task is also relevant for robotic arm interactions, where it has several downstream applications, including learning better interaction and manipulation abilities, triggering recovery procedures when anomalies occur, etc. Despite its relevance, the exploration of anomaly detection in robotic arm manipulation videos is limited by the low number of available resources. To this end, we introduce RoboVAD, a large-scale benchmark for video anomaly detection that comprises challenging cross-domain evaluation scenarios, where certain actions (tasks executed by a robotic arm) and anomaly types (mistakes that occur while performing certain tasks) remain unseen during training. RoboVAD is designed to benchmark VAD methods in realistic scenarios, where robotic arms can perform unforeseen tasks, and thereby encounter new anomaly types. We train and evaluate several state-of-the-art VAD methods, including a novel method specifically adapted for robotic arm manipulation. While the proposed method outperforms many state-of-the-art competitors, all methods remain below a micro-averaged frame-level AUC threshold of 70% in the most challenging evaluation setup, confirming the difficulty of the proposed benchmark. We publicly release our dataset and code at this https URL.
[CV-86] CALIPER: Metric-Grounded Model-Free Recognition of Visually Similar Industrial Parts
链接: https://arxiv.org/abs/2609.17820
作者: Alankrit Gupta,Chenxi Tao,Seung-Kyum Choi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 figures, 5 tables
Abstract:Fine-grained recognition of visually similar industrial parts is challenging when classes differ primarily in physical dimensions. Normalizing detected object crops to a fixed input size suppresses absolute scale, while CAD models and large class-specific datasets may be unavailable in evolving industrial inventories. We present CALIPER, a model-free RGB-D framework that couples support-based appearance matching with metric size evidence. Each training class is onboarded from a single turntable RGB-D video and one to two labeled real images; 3D reconstruction provides novel-view appearance support, while aligned depth yields a class-specific metric size profile. At inference, a coarse YOLOv8n-seg model localizes parts, and a frozen DINOv2 backbone with an episodically trained embedding head performs fine-grained support matching. Margin-conditioned metric fusion activates probabilistic size evidence only for appearance-ambiguous decisions. New classes are enrolled from a small RGB-D support set without updating network parameters. We evaluate CALIPER on 18 visually similar industrial parts: 16 classes are used for training, while two screws are reserved for training-free enrollment. CALIPER achieves 88.2% closed-set accuracy with 99.8% localization recall and 85.7% overall accuracy after 10-shot enrollment of the two unseen screws. Metric fusion improves unseen-class accuracy by up to 37.4 percentage points without statistically significant degradation of the original inventory. Robot-arm deployment identifies 17/18 parts without deployment-specific retraining.
[CV-87] GazeDiT: Gaze-Accurate Diffusion Image Generation for Eye Tracking via Spatial Conditioning
链接: https://arxiv.org/abs/2609.17814
作者: Dongze Wu,David Colmenares,Fengting Yang,Jogendra Nath Kundu,Yao Xie,Ali Behrooz,Conny Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion models are increasingly used to generate synthetic training data, but precise label control remains difficult when the conditioning signal is low-dimensional and coarse. Text-conditioned images are judged by broad prompt consistency, whereas supervised training requires precise correspondence between each image and its numerical label. This is challenging in eye tracking, where a 4D binocular gaze is expressed through subtle, spatially localized pupil and iris geometry. We introduce GazeDiT, a diffusion model that generates images for a requested 4D gaze through an internally constructed spatial condition that grounds the global gaze label in this local geometry. During training, a frozen SegFormer extracts pupil/iris geometry from diverse real images, allowing the model to learn realistic appearance conditioned on that geometry. At inference, a physical eye renderer samples gaze-consistent geometries by varying anatomy and camera state, enabling diverse synthesis without a source image. GazeDiT achieves substantially lower tail gaze-label error than other diffusion baselines, approaching the error of the same frozen gaze estimator on real images. Its generated data also improves the downstream eye tracker, reducing gaze error on difficult cases from 3.05° to 2.80° in the smallest cohort.
[CV-88] Wind on Trees: Testing Physical Grounding in Dynamic 4D Gaussian Splatting ECCV2026
链接: https://arxiv.org/abs/2609.17810
作者: Weiying Chen,Edmond Lou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Workshop Version of ECCV 2026 3DWM Workshop
Abstract:Monocular reconstruction of wind-driven vegetation is severely underconstrained: motion along the viewing direction is largely unobservable, a moving canopy offers few reliable correspondences, and nearly the entire scene is dynamic, providing little static reference. Directly-learned deformation fields in 4D Gaussian Splatting therefore optimize photometric consistency rather than recover the motion that produced it. We replace that field with a physically parameterized deformation prior: one damped harmonic oscillator per rigid part, driven by the observed wind and integrated by differentiable RK4, supervised photometrically alone. To test whether such a prior is physically grounded rather than merely well fit, we build a controlled synthetic testbed of three procedurally generated trees spanning an order of magnitude in skeleton complexity, whose per-part natural frequency follows from its own geometry and whose damping ratio is a fixed constant, both held out of training. On it, we measure held-out views, temporal extrapolation, zero-shot transfer to unseen wind speeds, and recovery of the physical parameters themselves. The prior costs appearance fidelity on in-distribution views and extrapolates markedly better outside the training window and the training wind, while parameter recovery is far weaker than it first appears: frequency recovery survives an untrained null control on only the sparsest of the three trees, and damping is not recovered at all.
[CV-89] AgenT eeth: A Model-Agnostic Framework for Suppressing Hallucination in Frozen Vision-Language Models on Dental X-Rays via Tool Evidence Injection
链接: https://arxiv.org/abs/2609.17800
作者: Ahmed Rafid,Fariya Ahmed,Rumman Adib,Mehedi Ahamed,Ajwad Abrar,Tareque Mohmud Chowdhury
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 2 figures, 5 tables
Abstract:Vision-language models (VLMs) remain largely unreliable on panoramic dental radiographs and can rely on learned anatomical priors rather than evidence in the image. This is particularly problematic for tooth localization and spatial reasoning, and fine-tuned dental VLMs can retain the same spatial biases. We present AgenTeeth, a model-agnostic, tool-augmented framework that grounds frozen VLMs using seven specialized dental vision experts. A question-aware orchestrator selects the relevant tools, whose detections are mapped to FDI tooth numbers or anatomical regions and returned as structured findings together with annotated image overlays. A fresh synthesis call then answers the question using this evidence, without fine-tuning the underlying VLM. On MMOral-OPG-Bench, AgenTeeth improves four backbone VLMs by 12.9-23.0 percentage points over their baselines. Our strongest configuration reaches 65.66% on open-ended VQA, compared with 45.35% for OralGPT-Plus. The advantage also holds at matched scale: a frozen Qwen2.5-VL-7B-Instruct with AgenTeeth reaches 48.11%, exceeding OralGPT-Plus built on the same backbone after supervised fine-tuning and reinforcement learning for tool use. We release the framework, all seven expert models, and a dentist-annotated dataset for alveolar bone-loss detection in panoramic radiographs.
[CV-90] Not All Patches Are Equally Forgettable: Spatially Localized Domain Unlearning in Vision-Language Models BMVC2026
链接: https://arxiv.org/abs/2609.17790
作者: Akanksha Singh,Vinod K. Kurmi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at BMVC 2026
Abstract:Pre-trained vision-language models (VLMs) exhibit strong cross-domain recognition performance even without additional training. However, this robustness can also preserve undesirable domain-specific behavior, as domain-related and semantic information often remain entangled within the learned representation space, making selective domain unlearning challenging. Existing approaches typically address this problem through latent-space disentanglement and prompt- or feature-level interventions, without directly attributing and attenuating individual patch-token contributions. However, here we suggest that rather than uniformly suppressing the full representation, it may be more effective to exploit the spatial structure of vision transformers to localize and suppress patch regions that contribute disproportionately to forget-domain prediction. Patches that strongly influence forget-domain prediction may not be equally important for semantic recognition, suggesting that forgetting should be guided according to the domain contribution of different visual regions. Specifically, we propose a two-stage patch-selective framework that first estimates patch-level domain sensitivity and then selectively attenuates patches whose contribution to forget-domain prediction is stronger than their semantic utility. We evaluate our framework on Office-Home, Mini DomainNet, and DomainNet. Experimental results demonstrate improved forgetting-retention tradeoffs compared to prior methods while improving retained-domain recognition by up to 3.8%. Additional evaluations under visually overlapping and unseen-domain settings further demonstrate improved robustness under distribution shift.
[CV-91] Beyond Performance Metrics: Uncertainty Mapping of Label Ambiguity in Fazekas Score Prediction
链接: https://arxiv.org/abs/2609.17753
作者: Susanne Schmid,Johanna Ospel,Richard Frayne,Roberto Souza
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages; 11 figures; 3 tables
Abstract:Reference labels used to train medical image classification models are not always as certain as they may appear, and this uncertainty has implications on performance metrics. In this study, we propose a framework to analyze model performance for periventricular Fazekas score prediction that goes beyond conventional metrics. The Fazekas score is an ordinal visual rating scale used to assess the severity of white matter hyperintensities and is known to be affected by inter-rater variability. While the best Fazekas score prediction model achieved a Matthews correlation coefficient (MCC) of 0.70, performance varied across data splits and loss functions, making interpretation of model capabilities difficult. Rather than interpreting epistemic uncertainty of a model’s prediction as an isolated scalar value, our approach of uncertainty mapping relates uncertainty to its position within the learned feature representation. This highlights regions of class-boundary transitions where cases appear more ambiguous and misclassifications are more likely. It also identifies potential label disagreement, including low-uncertainty misclassified cases that expert review found to be inconsistent with the original reference Fazekas score. Therefore, uncertainty mapping allows model behaviour to be examined in relation to class separation and potential model-label disagreement. Loss function choice also influenced the uncertainty profile, with some models showing clearer class separation and more localized uncertainty in ambiguous regions than others. These findings suggest that uncertainty mapping for Fazekas score predictions can support model interpretation and targeted dataset review when reference labels are affected by ambiguity/ inter-rater variability. Comments: 15 pages; 11 figures; 3 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.17753 [cs.CV] (or arXiv:2609.17753v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.17753 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-92] How to make effective use of domain experts for image classification?
链接: https://arxiv.org/abs/2609.17749
作者: Dieu-Donné Fangnon,Diane Lingrand,Aurélie Liard,Marco Corneli,Antoine Pasqualini,Frédéric Precioso
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 28 pages, 14 figures
Abstract:A lot of expectations have been put for years on integrating domain expert knowledge in image classification models. Several approaches have been explored, Concept Bottleneck Models (CBMs) opened up a new avenue of research leading to many variants, and more recently to Concept-based Embedding Models (CEMs). CBM consider binary encoding of each concept, while CEM expands this idea by embedding each concept through two vectors. However in real-life scenarii, domain experts’ knowledge is usually organized in concepts determined by various attributes, each attribute encoded either with numerical values, or range of values, or binary values, or categorical values. In this work, we first finetune an image feature extractor for classifying attributes representing the downstream object classes, where the class attributes have been specified by experts under various encoding formats. A classification head is then learnt from these various attributes to categorize target objects. We experimentally show that it improves the classification for three datasets: Kaggle fish dataset, AWA2 and a more challenging new wood charcoal dataset. We then propose an automatic selection of potential missclassified data. In this second step, experts are asked for those data to eventually modify the predicted attributes in order to improve the classification.
[CV-93] Geometry-Driven Shadow Harmonisation for Composited Faces: A Multiplicative Albedo-Preserving Relighting Pipeline
链接: https://arxiv.org/abs/2609.17740
作者: Vijesh KP
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Source code: this https URL
Abstract:Face swapping and face compositing pipelines routinely produce a face that is geometrically well aligned but photometrically implausible: the donor face carries flat, near-frontal studio illumination while the host body and background carry directional scene light. Most existing remedies re-synthesise the face through colour transfer, neural relighting, or inverse rendering, and therefore risk altering identity, skin tone, and texture. We present a conservative alternative: geometry-driven form-shadow injection. The pipeline never repaints the face. It estimates a per-pixel gain field g\in[g_\min,1] from a rasterised 3D face proxy and multiplies it channel-uniformly onto linear RGB, so the operator can only darken and cannot shift chromaticity. A dense landmark mesh is rasterised into a depth buffer, from which we derive surface normals, a cavity term, and screen-space cast shadows. Key-light direction is estimated from host-side cues (body, background, hair halo); on-face cues are downweighted because they recover the donor’s lighting. Shadow magnitude is not matched to the host: it is set by a three-parameter transfer (\tau,\sigma,g_\min) . The shading field is divided by its 75th percentile over skin, then gated, scaled, clamped, smoothed, and re-clipped inside a feathered, skin-gated face mask. On an analytic face heightfield, the default (\tau,\sigma,g_\min)=(0.90,0.45,0.82) modifies 56.5% of face pixels with mean gain 0.938 (0.890 on modified pixels) and drives 3.4% of pixels to the floor. Hue invariance is a corollary of the operator. We analyse the transfer in closed form, ablate its parameters, and discuss failure modes of a monotone, darkening-only formulation, including double-shadowing of non-flat donors.
[CV-94] CapMem: A Benchmark for Caption-Based Episodic Memory in Egocentric Video EMNLP2026
链接: https://arxiv.org/abs/2609.17688
作者: Dingli Liang,Yiqiao Xie,Yukai Huang,Zhaokai Wang,Weitong Cai,Guangwen Feng,Jifei Song,Zhensong Zhang,Hang Zhang
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: EMNLP 2026
Abstract:Wearable assistants require episodic memory over egocentric video, yet current vision-language models face bounded frame budgets, growing visual-token costs, and long-context retrieval failures. Under these practical constraints, we study whether textual captions can serve as reusable episodic memory. We define the Episodic Memory Video Caption QA task and introduce CapMem, a human-annotated benchmark with 75 videos totaling 33.7 hours, and 1,000 multiple-choice questions across 16 scenarios. On long videos (20 min), full-coverage CaptionQA with 30s and 60s caption windows outperforms direct VideoQA for 10/12 and 8/12 models, respectively. On the same video subset, a matched-frame control across six Qwen models retains mean accuracy gains of 3.22 and 2.55 points, respectively. Our caption-guided retrieve-and-verify harness further improves accuracy by up to 5.3 points. These results support the effectiveness of caption memory for episodic reasoning over long egocentric video.
[CV-95] LEAP: Learning Emergent Active Perception for Quadruped Navigation
链接: https://arxiv.org/abs/2609.17628
作者: Ü. Bora Gökbakan(WILLOW),Stéphane Caron(ISIR),Philippe Souères(LAAS-GEPETTO)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to target where to reduce uncertainty about their environment. Learned systems typically encourage this behavior with hand-designed proxy objectives, such as coverage or curiosity bonuses, that may conflict with the task. In this work, we propose a method to learn emergent active perception (LEAP) without augmentation of the task objective. We formulate the problem of goal-oriented navigation over hazardous terrains with goals that must be discovered visually. We then propose an architecture for navigation policies with active perception, and train them on a terrain curriculum where task pressure alone leads to the emergence of gaze control. Key to this emergence, LEAP works on a gaze-invariant representation that integrates depth images into egocentric belief maps. We validate its performance in held-out evaluation scenarios, where it achieves a 92.7% success rate, compared to 74.2% for scripted or 34.5% for passive perception, and comes within 4.6 points of a privileged oracle. We validate that LEAP navigation policies, unchanged, can be directly applied to steering quadrupedal locomotion policies in physics simulation.
[CV-96] DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting ECCV2026
链接: https://arxiv.org/abs/2609.17613
作者: Xuan Cuong Ngo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Zero-shot object counting aims to estimate the number of objects specified by a text query without category-specific training. Recent approaches primarily rely on density regression or detection-style instance prediction. While effective, density-based models often suffer from spatial ambiguity and background leakage due to weakly regulated mass allocation, leading to fragmented or part-biased representations that increase counting error in complex scenes. In this work, we propose an instance-aware dual-decoder framework that structurally couples density and point representations for zero-shot object counting. Instead of treating density estimation as independent pixel-wise regression, we interpret it as a structured mass allocation problem over a latent set of object instances. Predicted instance centers induce a soft instance-wise decomposition of the density map, upon which we enforce two geometric constraints: (1) per-instance mass conservation, ensuring each object contributes approximately one unit of density mass, and (2) center-of-mass alignment, encouraging each density component to concentrate around its corresponding predicted center. These constraints introduce instance-level geometric consistency and lead to more accurate mass allocation, thereby reducing counting error. Extensive experiments on FSC-147, PUCPR+, and CARPK show that our approach consistently reduces counting error and establishes new state-of-the-art performance in zero-shot object counting.
[CV-97] mperon: Full-Time SAM Quality at a Third Less Wall-Clock
链接: https://arxiv.org/abs/2609.17575
作者: Stamatis Mastromichalakis
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 5 Figures, 4 Tables
Abstract:Sharpness-aware minimization (SAM) doubles the cost of every training step, yet its benefit concentrates where training ends. We study where an expensive training mode should be spent and propose Temperon: a plain-SGD explorer for the first 43% of the epoch budget, then one scheduled hand-off that gives the entire final cosine anneal to a SAM-wrapped Muon refiner. On CIFAR-10/100, SVHN and Tiny ImageNet (five seeds, times reported as epochs-to-target times an idle-GPU-calibrated epoch cost), Temperon matches the best full-time-SAM recipe on accuracy everywhere while reaching the hardest common target 35%, 34% and 32% sooner on three of the four, and sits a tier above the published SAM+SGD recipe at level cost. Ablations make the attribution exact: the Muon refiner is worth +0.85pp with everything else fixed; the explorer’s shape and its restarts are worth nothing, and we withdraw them as contributions. Re-running the closest rival, late-phase SAM, at matched budget shows the frontier: it is fastest to every mid-level target, but the tier the Muon refiner buys (0.83 on CIFAR-100, 0.97 on CIFAR-10) is reached by no SGD-refined method in any seed, and on Tiny ImageNet, where Muon buys no tier, the rival simply wins – the measured boundary of the method. The allocation law transfers to GPT-2 pretraining (full-SAM quality at -29% wall-clock) and GLUE fine-tuning (never worse than full-time SAM at a third of its SAM cost). Two constants organize the economics: skipping SAM early buys a fixed credit, and a Muon epoch costs 1.50x a SAM+SGD epoch on all four datasets. Finally, the hand-off cannot be timed from the trajectory: under cosine schedules the accuracy curve is plateau-then-surge, so the information lives in the schedule, making the scheduled switch principled rather than convenient. Code and a pip-installable implementation are released.
[CV-98] Adaptive Interpolatory Curve Subdivision with Learned Local Angles
链接: https://arxiv.org/abs/2609.17566
作者: Hassan Ugail,Newton Howard
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Curve subdivision is pivotal in computer graphics for generating smooth geometric objects from control polygons. Interpolatory subdivision is especially attractive because the refined curve is guaranteed to pass through the designer’s control points. Classical four-point and six-point schemes preserve this property, but their behaviour is governed by a single global tension parameter, limiting their ability to adapt across flat regions, sharp turns and varying local geometries. We introduce an adaptive local-angle formulation that keeps the interpolatory structure intact while learning how each new vertex should be inserted. A compact edge-wise predictor assigns one insertion angle per edge, while the original vertices are copied exactly at every refinement level. Interpolation is therefore a structural property of the operator and does not depend on the trained weights. The same predictor is used with geometry-specific geodesic primitives on the Euclidean plane, the two-sphere and the Poincaré disk. Under a matched-density evaluation protocol, the method reduces nearest-neighbour error by factors of five to seventeen over the best validation-tuned fixed-tension baseline, and by about 1.8 over centripetal Catmull-Rom in the Euclidean case. It also substantially reduces bending energy and tangent roughness, while remaining competitive with separately trained per-geometry models.
[CV-99] A Heisenberg Lift Descriptor for Order Sensitive Online Handwriting Recognition
链接: https://arxiv.org/abs/2609.17565
作者: Hassan Ugail,Newton Howard
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Online handwriting recognition systems typically represent pen trajectories through fixed-length Euclidean shape descriptors that capture the spatial outline of each stroke, but are insensitive to the order in which that outline is produced. Two strokes that trace the same region of the plane in opposite directions are indistinguishable to any such order-blind representation, yet their traversal directions may carry decisive class information in characters where loop orientation and stroke sequencing matter. This paper introduces a Heisenberg-lift framework that addresses this gap through a compact, interpretable, order-sensitive augmentation for online pen-trajectory features. The simplest instance is the terminal signed area, a single parameter-free scalar appended to an existing Euclidean descriptor at negligible computational cost. Evaluated on two standard online handwriting benchmarks, this one-scalar addition, consistently raises classifier accuracy over the Euclidean baseline. On the hardest character pair in our study, the letters o and y, the signed area alone achieves perfect separation while the Euclidean baseline falls short. The advantage grows further under additive coordinate noise, a practically relevant degradation in pen-trajectory data. A richer fifteen-dimensional extension, derived from a noncommutative Heisenberg-group subdivision scheme, provides additional gains in noisy and loop-structured conditions. Dimension-matched statistical controls confirm that all improvements reflect geometric information rather than feature-count inflation. The resulting descriptor is lightweight, closed-form, and directly interpretable, making it a practical augmentation for online handwriting and related document-trajectory classification pipelines in which the direction of stroke execution carries discriminative information.
[CV-100] Pay Only for Disagreement: Certified No-Regression Verdicts for Model Updates with Matching Label-Complexity Bounds
链接: https://arxiv.org/abs/2609.17560
作者: Vishnu Bindu Balachandran
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注: 32 pages, 6 figures, 6 tables
Abstract:Every production model is updated, by retraining, fine-tuning, quantization, or a silent vendor swap, and each update risks being worse than what it replaced. We formalize update promotion as certified paired risk-difference auditing. Our starting point is a support identity: the risk difference between two models lives on the inputs where they disagree, observable without labels. We build DISCERN, a sequential two-tier protocol. A zero-label tier certifies benign updates whose disagreement rate is below tolerance from unlabeled traffic alone. An audited tier labels only sampled disagreements through an anytime-valid confidence sequence, valid at every stopping time and under any label-routing rule, even an adversarial judge. We prove finite-sample validity and matching label-complexity bounds of order rho^2/eps^2 at the rate level, so exploiting free disagreement provably saves a factor 1/rho over any pairing-blind auditor, and the guarantee composes across an unbounded sequence of promotions from one error budget. Across 14,000+ replayed audit streams over 785 update pairs, including LoRA fine-tunes of language models up to 1.4B parameters, miscoverage is 0.0002 (nominal 5%), power 0.986 with zero false alarms, and 56% of benign updates certify with zero labels. Each audit emits a machine-checkable evidence record for post-market monitoring.
[CV-101] Selective Prediction and Uncertainty-Aware Referral for Pap Smear Classification
链接: https://arxiv.org/abs/2609.17545
作者: Nisreen Albzour,Sarah S. Lam
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep learning models for cervical cytology are almost always evaluated as if every prediction must be acted upon, yet a screening system deployed alongside a cytopathologist need not classify every slide: it can defer the cases it is least certain about. Evaluating such a system requires asking not only how often it is correct, but whether its confidence ranks its errors to the bottom. This paper studies selective prediction and uncertainty-aware referral on the Herlev Pap smear dataset under a binary Normal-versus-Abnormal formulation. Two lightweight transformer backbones (Swin-Tiny, TinyViT-5M) are fine-tuned on Herlev from ImageNet-pretrained weights with weighted random sampling, calibrated by post-hoc temperature scaling fit on a held-out calibration subset, and compared against a soft-voting ensemble of both models. Discrimination is reported alongside expected calibration error (ECE) and, as the primary endpoint, the area under the risk-coverage curve (AURC). No statistically significant difference was detected between the two configurations in accuracy or macro-F1, yet the ensemble halves AURC (0.0022 vs. 0.0045, a 51.8% reduction, lower in all five folds) and extends the coverage at which zero errors are made from 18.3% to 72.8% of the pooled test predictions. The same ensemble is nonetheless worse calibrated in absolute terms (ECE 0.0339 vs. 0.0247) and produces more false negatives (14 vs. 10). These results separate two properties that are frequently conflated: the ability to rank predictions by trustworthiness, and the accuracy of the confidence values themselves. Ensembling improves the former while degrading the latter, and the former directly governs the observed risk-coverage tradeoff, whereas the latter governs the interpretation of the reported confidence values.
[CV-102] FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRI
链接: https://arxiv.org/abs/2609.19042
作者: Karan R. Bagri,Tarun K. Garg,Vaanathi Sundaresan
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In medical imaging, developing robust deep learning models requires data from various domains. However, regulatory policies protecting patient privacy restrict data sharing. Federated learning (FL) addresses this by enabling collaborative model training without centralizing data. Yet, data heterogeneity across client centers requires personalized models to enhance performance. Clients with limited resources may struggle to efficiently train or deploy large deep neural networks. Adaptive model pruning tackles both challenges by reducing model size while enabling personalized FL adaptation. Although research has investigated adaptive pruning methods in FL, their effectiveness on dense prediction tasks like segmentation remains unexplored. To address this gap, we propose Activation Statistics-driven structured Adaptive Pruning (FedASAP), which uses activation-based features to guide filter removal for each client. By learning a lightweight classifier on per-filter activation statistics, FedASAP refines importance-score rankings and produces compact, personalized segmentation models in heterogeneous federated settings. Our results show the approach’s effectiveness by achieving better Dice scores of 0.796 and 0.743, than state-of-the-art, while reducing parameter count by 45% and 73% on two multi-centric brain pathology segmentation datasets: Federated Tumour Segmentation (FeTS) and White Matter Hyperintensities (WMH), respectively.
[CV-103] Flexible-Region Based Adaptive In-Loop Filter for Video Coding
链接: https://arxiv.org/abs/2609.18983
作者: Xuewei Meng,Chuanmin Jia,Jing Cui,Shanshe Wang,Siwei Ma
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: This paper was submitted to PCS2019
Abstract:Adaptive loop filter (ALF) for video coding, which is designed to minimize the mean square error between original and reconstructed samples by using Wiener-based filter, has attracted increasing attention for its significant capability in improving coding efficiency. In the second and third Audio Video Coding Standard, i.e., AVS2 and AVS3, ALF is adopted as one of the in-loop filters. In current design, each frame is divided into 16 regions at most and corresponding filter coefficients are then derived and utilized to reconstruct each region. In this paper, a flexible-region based ALF (FRALF) scheme is proposed to improve the adaptability of existing ALF in AVS3, which introduces multiple region partition templates, such as 2\times4 , 4\times4 , 4\times8 and 8\times8 . We subsequently propose the filter coefficients merging algorithm to further improve coding efficiency by estimating the distortion level of different partition regions. The proposed FRALF can fully consider the local texture characteristics as well as non-local similarities synthetically. The experimental results show that FRALF outperforms the existing region-based ALF in AVS3 with relatively low complexity increasing.
[CV-104] QiT: Quantum-Inspired Transformer for Visual Recognition Task
链接: https://arxiv.org/abs/2609.17789
作者: Badri N. Patro,Vijay Agneeswaran
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Applied Physics (physics.app-ph)
备注:
Abstract:Quantum machine learning offers a compelling representational perspective: angle-encoded states inhabit Hilbert spaces in which periodic similarities and interactions can be expressed naturally. Realizing this perspective for visual recognition remains difficult, however, because present quantum neural networks are constrained by limited qubit counts, costly circuit simulation and measurement, noise, and unstable optimization on noisy intermediate-scale quantum devices. We investigate whether useful structural ideas from quantum models can instead be realized as scalable classical Transformer operations. We introduce QiT, a Quantum-inspired Transformer for vision tasks with three components: (i) angle-inspired encoding that maps image tokens to learned trigonometric Hilbert-space features analogous to quantum rotation-based state encoding; (ii) self-attention over these periodic features, inducing a classical cosine kernel approximated to quantum fidelity kernels; and (iii) gated multiplicative emulation, a trainable classical surrogate for interaction terms found in variational circuits. All components are differentiable tensor operations, so QiT claims neither quantum computation nor quantum speedup and retains the \mathcalO(N^2D) attention complexity of a standard Vision Transformer. Across image-classification benchmarks, QiT is competitive with a matched classical Transformer while avoiding the severe runtime cost observed for a small simulated quantum Transformer. QiT-B reaches 78.3% ImageNet-1K top-1 accuracy with 45.7M parameters and 11.5 GFLOPs. These results position QiT as a scalable baseline for isolating and evaluating quantum-motivated inductive biases in visual recognition.
人工智能
[AI-0] Dreaming the Sound of Contact: Leverag ing Video and Audio Generation for Zero-Shot Force-Aware Manipulation and Data Generation
链接: https://arxiv.org/abs/2609.19137
作者: Guanhua Ji,Tianyu Li,Dayoon Suh,Yuqian Zhang,Boyan Zhang,Nadia Figueroa
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in video generation allow robots to learn manipulation trajectories from generated videos. However, these approaches produce purely kinematic trajectories that lack force information, causing failures in contact-rich tasks where appropriate contact forces are essential for success. In this work, we explore augmenting generated video with audio to shape a bounded, time-varying desired-force profile using the loudness of generated contact sounds. We present a pipeline that jointly leverages generated video and audio to derive motion trajectories and corresponding desired-force profiles from a structured natural-language task prompt. We execute these force-aware trajectories on a Franka Panda robot using a closed-loop force regulator that tracks the audio-shaped force profile during contact. We evaluate our pipeline on multiple tasks that require making contact and demonstrate successful manipulation where a kinematic-only baseline fails. We also use the pipeline as a data generation engine to train policies that achieve the tasks in a closed-loop manner. Project website, videos, and dataset: this https URL
[AI-1] rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference
链接: https://arxiv.org/abs/2609.19104
作者: Kaijun Zhou,Zhiyang Li,Le Chen,Jinyu Gu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Factory work is a promising early scenario for embodied AI: assigning repetitive manual jobs to robots has clear economic payoff, and a structured station keeps the jobs tractable for current policies. Vision-Language-Action (VLA) models now dominate as the policy paradigm for these robots. The inference latency of VLA models directly affects robot responsiveness and motion smoothness. However, existing VLA inference frameworks do not fully exploit the characteristics of embodied workloads or account for the distinct bottlenecks across different stages of VLA inference. In this paper, we first characterize embodied workloads and identify substantial task similarity across repeated robot executions. We further find that such similarity extends beyond observations and action trajectories to internal model states. Drawing on these observations, we present rMuscle, a real-time VLA inference framework inspired by human muscle memory. It exploits cross-execution similarity through a dual-phase muscle-memory cache. The Context Cache reuses visual-token outputs to reduce computation, while the Action Cache reuses neuron activation patterns to reduce weight accesses. We keep both the cache memory footprint and access overhead low through online cache recomputation, sliding-window cache retrieval, and mask sharing across consecutive denoising steps. rMuscle achieves 1.29-1.42X speedup on RTX 4090 and Jetson Thor across LIBERO, RoboTwin, and physical manipulation tasks, while maintaining the original success rates on real-world robots. Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.19104 [cs.RO] (or arXiv:2609.19104v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.19104 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-2] Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria MICCAI2026
链接: https://arxiv.org/abs/2609.19096
作者: Abbas M. Rabiu,Abdulrazaq A. Zubair,Um-mulkhairi Ibrahim,Tolulope Olusuyi,Shaheeda Farouq,Safwan M. Dafi,Adaobi C. Emegoakor,Yewande Gbadamosi,Maruf Adewole
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Accepted for publication at the AFRICAI Workshop (MICCAI 2026)
Abstract:Artificial intelligence (AI) is increasingly integrated into healthcare systems worldwide, yet its successful clinical adoption depends critically on workforce readiness, particularly in low- and middle-income countries (LMICs) where infrastructural and training gaps persist. This cross-sectional study evaluated awareness, attitudes, preparedness, and barriers to AI adoption among 761 healthcare professionals across multiple disciplines and practice settings in Nigeria. Data were collected between December 2025 and March 2026 using a structured, validated questionnaire. Overall awareness of AI in healthcare was high (92.6%); however, objective knowledge and self-reported preparedness remained limited, with 40.9% reporting low or very low knowledge and only 63.0% feeling adequately prepared. Willingness to adopt AI was high: 92.5% expressed interest in training, and 78.7% supported inclusion of AI education in undergraduate curricula. Key barriers included lack of training (84.7%), poor infrastructure (71.1%), high cost of AI tools (61.0%), fear of job displacement (60.6%), ethical concerns (52.9%), and data privacy concerns (52.7%). Significant differences in preparedness were observed across geopolitical zones (chi-square (5) = 24.28, p 0.001), and awareness differed across professional groups (chi-square (6) = 68.38, p 0.001). Attitudes toward AI differed significantly across professional groups (F = 3.32, p = 0.003), with professionals who felt prepared demonstrating more positive attitudes (mean = 3.74) compared to those who did not (mean = 3.46). These findings reveal a critical disconnect between high awareness and actual readiness, underscoring the need for targeted training, infrastructure investment, and clear implementation frameworks to bridge the gap between AI technological potential and clinical reality in resource-constrained settings.
[AI-3] Probabilistic Linear Explanations
链接: https://arxiv.org/abs/2609.19077
作者: Frederic Koriche,Jean-Marie Lagniez,Chi Tran
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Under Review
Abstract:Formal explainability provides mathematically grounded justifications for individual predictions. However, abductive explanations often exceed human cognitive limits by involving too many features, while probabilistic relaxations have remained largely limited to categorical classification. We present a unified framework for probabilistic explainability based on sparse, anchored linear models, applicable to both binary classification and continuous regression. By mapping instances to the Boolean hypercube, our linear explanations strictly generalize subset-based approaches: they capture both the magnitude and direction of feature contributions while enforcing a prescribed sparsity budget k . We show that minimizing the relevance error for such explanations is \ClassNPPP-hard when the underlying model is a neural network, and we relate this intractable objective to a tractable surrogate—the fidelity error. For a parameterized family of local distributions, the relevance error of any k -sparse explanation is bounded by its fidelity error up to a multiplicative factor that remains small locally. We address the resulting empirical problem using two complementary approaches: a Mixed Integer Programming (MIP) formulation that yields provably optimal empirical solutions while maintaining polynomial sample complexity, and a polynomial-time Iterative Hard Thresholding (IHT) algorithm with provable approximation guarantees. Empirical evaluations show that, unlike state-of-the-art baselines such as LIME and MAPLE, our explanations satisfy both the anchoring and sparsity constraints by construction, while consistently achieving lower relevance error.
[AI-4] Double descent is the principle of least action
链接: https://arxiv.org/abs/2609.19076
作者: Congzhou M Sha
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Computational Physics (physics.comp-ph); Data Analysis, Statistics and Probability (physics.data-an)
备注: 11 pages, 2 figures, 1 table
Abstract:The test error of a model plotted against its number of parameters d falls, peaks when the model can just fit the training data, and falls again, exhibiting the double descent phenomenon. We explain the phenomenon with statistical mechanics. The training trajectory of a stochastic gradient-based method is a particle wandering over the energy landscape of the training loss at an induced temperature T , and a run that has equilibrated visits every parameter vector of a given training loss equally often, the fundamental postulate of statistical mechanics, with probability given by the Boltzmann distribution. Because training starts at an initial point and has only finite time to diffuse, it carries an effective weight decay, which makes every parameter a quadratic degree of freedom. The equipartition theorem then distributes the energy among the d degrees of freedom in shares of T/2 , so at a fixed training loss adding parameters lowers the temperature and drives the Boltzmann distribution toward the stationary path. Finally, adding parameters can only lower the L^2 norm of the stationary path, so a solution sampled at fixed loss is less likely to be large with increasing d , effectively increasing weight regularization.
[AI-5] RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control
链接: https://arxiv.org/abs/2609.19074
作者: Bernd Frauenknecht,Emma Cramer,Artur Eisele,Paul Kruse,Lukas Kesper,Jonas Hertrampf,Ramil Sabirov,Jyotirmaya Patra,Johannes Berger,Paul Brunzema,Friedrich Solowjow,Sebastian Trimpe
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) is an exciting concept as well as a remarkable success story worth sharing. However, RL builds on rather complex interactions between different objects that play out over several cycles. Such dynamics are often best explained with an easily accessible implementation. We present RLLBC-Lib, a carefully crafted code library with the goal of lowering the entry barrier for students and other learners of RL in the context of learning-based control. At its heart, RLLBC-Lib comprises a comprehensive library of tabular RL approaches to enforce a clear understanding of the theoretical foundations. A deep RL library follows the same design principles, underscoring the parallels between simple tabular and state-of-the-art deep RL approaches. Additionally, RLLBC-Lib provides a collection of implementations illustrating core RL principles and contrasting RL to other learning-based control approaches. Finally, RLLBC-Lib provides an ideal basis for creating programming assignments with automated grading.
[AI-6] Lost in Perception: Isolating Perceptual and Reasoning Failures in Multimodal Physics and Geometry Reasoning
链接: https://arxiv.org/abs/2609.18991
作者: Raj Jaiswal,Sree Krishna Uppalapati,Dhruvkumar Patel,Ria Khatoniar,Tanuja Ganu,Rajiv Ratn Shah
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal LLMs report strong performance on scientific reasoning benchmarks, yet most treat perception and reasoning as a single measurable process. We introduce a five-task diagnostic experiment across physics and geometry benchmarks that isolates failures to perception, reasoning, or both. Incorrect diagram interpretation degrades performance even on problems models solve correctly from text alone, and accuracy generally rises from raw images to human-authored captions. Recovery under corrected captions is high for some models, separating perception-blocked failures from genuine reasoning bottlenecks. Which reasoning error follows a perception failure depends on domain: physics failures resolve into calculation errors, geometry into conceptual misapplication. As a discussion beyond our core experiments, InternS1-mini, despite heavy scientific pretraining and thinking capabilities, falls below the weakest model from experiments on every task, with reasoning traces frequently truncating before completion.
[AI-7] Function Lives Where Variance Doesnt: Task-Weighted Charts of a Language Models Computation
链接: https://arxiv.org/abs/2609.18989
作者: Alexandre Quemy
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:How many dimensions does a language model’s computation actually use? The question is ill-posed until one names a functional. Task-weighted charts make it well-posed: low-dimensional coordinate systems fit against a chosen functional of the representation, under the functional’s own metric, turning distillation into plain least squares. Across six models from three families, spanning 70m to 7B parameters, next-token prediction needs 70–90% of the residual stream’s width to stay within 5% of intact perplexity, a width consumed by the rare tail of language, and the variance profile predicts none of it: two directions carry 90% of GPT-2’s activation variance and almost none of its function. Dimension is per-functional: the model’s own uncertainty reads from six coordinates where the full predictive distribution needs hundreds; and it grows with depth. The dissociation is exploitable: when only a few dimensions can be kept, charts trained under the functional’s metric preserve the model’s predictions better than variance-based or optimal linear compression.
[AI-8] Suppressed Not Erased: A Representational Trace of Edited Facts Survives Even Weight-Free Knowledge Editing
链接: https://arxiv.org/abs/2609.18985
作者: Priyansh Srivastava,Romit Chatterjee
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Knowledge-editing benchmarks certify local correctness, whether an edited model produces the new fact on near-edit prompts but not how much of the original fact remains decodable inside the model. We study residual knowledge directly with a linear trace probe: after editing a fact, we ask whether the original object is still recoverable from the model’s hidden states. On GPT-2-XL, across three mechanistically distinct editors applied to 50 CounterFact edits, the original object remains linearly decodable well above chance after a successful edit (probe accuracy 0.96 for ROME, 0.86 for constrained fine-tuning, and 0.79 for the memory-based editor GRACE, against a chance level of 0.50; all edits reach 100% generation-based success). The GRACE result is the most informative: GRACE changes zero base-model weights, overriding the fact through an external memory, yet the original object is still decodable from the underlying network, so the residual trace cannot be attributed to an incomplete weight update. We read this as evidence that editing, even when behaviorally successful, suppresses rather than erases the original association in representational space. We also report a relearning-savings instrument that did not behave reliably in our setting and discuss why; we treat it as a negative methodological result rather than evidence. Code and data are released.
[AI-9] BadQubits: An LLM -Based Framework for Static Pre-Execution Detection of Structurally Harmful Quantum Circuits
链接: https://arxiv.org/abs/2609.18965
作者: Justin Woodring,Lamine Noureddine,Aisha Ali-Gombe
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents BadQubits, an LLM-based framework for static pre-execution detection of structurally harmful OpenQASM 2.0 circuits. The framework targets physical-execution-layer threats by analyzing submitted circuits prior to runtime, where dynamic inspection is constrained by measurement irreversibility and the exponential cost of classical quantum-state simulation. We evaluate four code-understanding LLM architectures on a dataset of 1,500 circuits consisting of 1,000 benign programs from MQTBench[33] and 500 synthetic attack circuits derived from three documented physical-layer threat primitives. Our fine-tuned Qwen Coder 2.5 7B model achieves 92.67% classification accuracy and 96.1% harmful-circuit recall. Two of the four evaluated base models fail to generalize under constrained LoRA fine-tuning, indicating that architecture-aware model selection is a necessary design consideration rather than a minor tuning choice. To characterize what the detector has learned, we compare it against a bag-of-gates CNN under progressive confound removal and adversarial syntactic perturbation. The CNN’s harmful-circuit recall drops from 100% to 17%, while the fine-tuned LLM decreases only from 96.1% to 91.2%. We attribute this gap to the sequential structure retained in token-level LLM inputs but discarded by histogram-based baselines. A correlation analysis further shows that model decisions track threat-defining features, specifically SWAP density and measurement timing, rather than generator-specific artifacts such as register naming.
[AI-10] ranscribe Then Reason : Two-Pass Decomposition for Multimodal Review
链接: https://arxiv.org/abs/2609.18958
作者: Bojie Li,Noah Shi
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI)
备注:
Abstract:The natural way to review a long recording or document with a multimodal model is to hand it the raw source and ask for a review in one call. We show that this quietly fails: the model satisfices, dropping roughly a third of the content and embellishing the rest. The failure is not perception–almost all of the dropped content reappears when the same model is simply asked to transcribe the source. The bottleneck is generation under load: a single pass cannot perceive, reason over, and write a long faithful review at the same time, because doing all three competes for one output. We rule out the obvious alternatives. It is not the modality: models read text and an image of the same text equally well. And it is not merely a matter of thinking harder: giving the single pass a far larger reasoning budget does not recover the lost content, because the model spends that budget planning a review rather than writing the source down. What works is to split the labor across two same-weights passes–first transcribe, then review the transcript–so each step gets a full output budget of its own. This transcribe-then-review decomposition improves both faithfulness and coverage across a 21-source suite. The benefit is not uniform: we observe that it helps most where the one-pass baseline is weakest and little where that baseline is already strong, a pattern that also tracks the source’s length and modality. Decomposition comes with two failure modes–the review pass running out of room on very long sources, and confabulating from memory once the grounding source is removed.
[AI-11] StableEval Arena: A Cost-Aware Agent ic Benchmark for Stablecoin Price Stability Prediction
链接: https://arxiv.org/abs/2609.18949
作者: Sean Wan,Dongping Liu,Luyao Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce StableEval Arena, a cost-aware benchmark framework for evaluating agentic AI systems on stablecoin peg-risk prediction. StableEval Arena evaluates LLM-backed agentic systems on diagnosing peg stress and forecasting deviations from the one-dollar peg over a hidden seven-day horizon, using leakage-safe historical replay with exchange price-volume data and market-context features. We report two complementary experiment blocks: a 120-case stress-enriched validation block and a 507-case natural-distribution full-arena evaluation block. Across six LLM-backed agent configurations and baselines, StableEval Arena measures prediction quality, calibrated-label behavior, structured-output reliability, latency, token consumption, and estimated inference cost. Rather than ranking agents by accuracy alone, the framework treats trustworthiness as a joint property of forecast quality, operational reliability, and computational cost. The results show a gap between protocol-following reliability and financial-risk reliability: agents reliably produce valid structured outputs at modest measured cost, but still miss most rare severe-stress and sustained-depeg cases. To support auditing and replication, we release the benchmark dataset on Hugging Face and the source code on GitHub.
[AI-12] Higher-order pruning of experts in mixture-of-experts language models
链接: https://arxiv.org/abs/2609.18916
作者: Alex M. Tseng,Prannay Kaul,Luca Zancato,Wei Xia,Stefano Soatto
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Mixture-of-Experts (MoE) language models suffer from large parameter counts, which create a significant memory bottleneck. Expert pruning is the most direct approach for reducing this parameter count, yet existing methods make pruning decisions for each expert independently, and assume experts’ contributions are purely additive. In reality, expert usage in MoEs is inherently cooperative. We derive HOPE (Higher-Order Pruning of Experts), a second-order pruning objective which provably minimizes an upper bound on the error resulting from pruning. We show that REAP (a state-of-the-art first-order pruning method) is a special case of HOPE where interaction terms are ignored. Across three frontier MoE models (up to 122B parameters), two distinct calibration sets, and multiple benchmarks (including math, instruction following, coding, and an agentic suite), we demonstrate that HOPE produces better pruning decisions than existing methods, and its advantage is most pronounced at high pruning rates and on challenging agentic workloads. At 50% pruning, HOPE outperforms all baselines and achieves an average rank of 1.58 out of 5 methods (versus 2.42 for the next-best method, REAP), with gains of up to +6.1% on agentic coding. Over all conditions, HOPE again achieves the best average rank and surpasses every other method in the majority of head-to-head comparisons. By preserving cooperative expert structure that first-order methods ignore, HOPE enables aggressive compression with minimal degradation, particularly on complex tasks where diverse expert combinations are invoked over long sequences.
[AI-13] NeuroECG: ECGFounder-Based Deep ECG Representation for EEG-Free Neurological Prognostication After Cardiac Arrest
链接: https://arxiv.org/abs/2609.18891
作者: Jiaju Gao,Yi Zhao,Chenyang Xu,Yuxi Zhou,Hao Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Submitted to BIBM 2026
Abstract:Neurological prognostication after cardiac arrest commonly relies on electroencephalography (EEG). However, EEG demands high clinical resources. Bedside electrocardiography (ECG) is standard and low-cost. Yet, its value for predicting neurological outcomes remains underexplored. In this study, we propose NeuroECG, an ECGFounder-based deep representation framework for EEG-free auxiliary prognostication. NeuroECG adapts a pretrained ECG foundation model via task-specific fine-tuning. We implement a gradual unfreezing strategy on single-channel bedside monitoring ECG. Multiple ECG segments per patient are encoded into segment-level deep features. These embeddings are aggregated via quantile pooling ( q = 0.24 ) and compressed using principal component analysis (PCA). Experiments on 412 ECG-available patients from the multicenter I-CARE database show that the adapted ECGFounder backbone achieves the best performance among ECG-only backbone baselines, with a test AUROC of 0.7333. We further combine the learned deep ECG representation with static clinical covariates. The proposed NeuroECG model achieves a test AUROC of 0.8077 and an AUPRC of 0.8970. These results support deep bedside ECG representations as a useful source of auxiliary prognostic information. Their integration with static clinical covariates improves prediction in an EEG-free setting. The source code is available at this https URL
[AI-14] ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions
链接: https://arxiv.org/abs/2609.18864
作者: Guosen Wu,Huizhen Huang,Guoxiong Long,Tao Huang,Chen Hou
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures
Abstract:Privacy evaluations of tool-using LLM agents often inspect a designated action, final response, or attacker report. These local proxies can miss unauthorized exposure elsewhere in a multi-step session and lack common ground truth across outlets, reports, and tool paths. We introduce privacy exposure displacement, the mismatch between a local evaluation proxy and target-grounded session exposure, and ASLEval, an authorization-aware framework that pre-registers a hidden target set, measures all declared visible exits, and reserves internal traces for diagnosis. Across multiple enterprise-style environments and independently implemented runtimes, we observe three recurring patterns. An expected-outlet-only view misses 46.9% of exposure recovered by the visible-exit union; attacker self-reports combine omissions with high false discovery; and schema-aligned internal evidence usually precedes visible exposure at the request/probe level. Reducing model-visible returns changes this path but can eliminate normal-task success. Independent human review supports the adjudication pipeline while identifying harder console and candidate cases. These findings motivate benchmarks that declare the complete visible boundary, ground claims in pre-specified targets and authorization, and report privacy together with task utility.
[AI-15] aming the Agent ic RAN: Stability-Guaranteed Arbitration of Autonomous AI Agents in O-RAN
链接: https://arxiv.org/abs/2609.18857
作者: Seyed Bagher Hashemi Natanzi,Bo Tang
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:The O-RAN control plane is becoming agentic: autonomous AI agents, deployed as rApps by different vendors, independently close control loops over shared radio resources. We demonstrate on a live O-RAN system that this independence is unsafe. Two agents with individually correct objectives, one protecting a latency SLA and one maximizing utilization for energy efficiency, jointly drive recurring opposing excursions of the shared resource partition that neither produces alone. Existing conflict-mitigation mechanisms presume a statically known application population and cannot govern agents whose behavior emerges at run time. We present AURA, a lightweight arbitration layer that admits agent actions only when they satisfy feasibility invariants, per-variable dwell times, and a deadband, and we prove the arbitrated system converges to a feasible operating point. Implemented on an OpenAirInterface (OAI) testbed with measured one-way latency and throughput, AURA reduces recurring shared-state excursions by more than an order of magnitude (from 8.4 to 0.4 PRB amplitude) and virtually eliminates cross-slice throughput starvation (from 40-55% to 0.3%), while leaving the protected slice’s own latency compliance unchanged, a trade-off the convergence guarantee makes explicit.
[AI-16] Ask the Tool Dont Guess: Agent Tool Calls Hold Their Progress and the Serving System Should Read It
链接: https://arxiv.org/abs/2609.18849
作者: Yipeng Liu,Yingqiang Zhang,Feifei Li,Huanchen Zhang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Operating Systems (cs.OS)
备注:
Abstract:An agentic request spends substantial wall-clock time waiting for tools, and its KV cache holds GPU memory the whole time. Serving systems decide whether that cache stays, leaves, or comes back by guessing how long the tool will run, from the tool’s name, its history, a duration declared before the call, or the engine’s own occupancy. We show that no estimate fixed before a call starts can know its duration, and such estimates may not even rank the calls. Meanwhile, the running tool already holds the answer, but the agent stack together with the tool silences it. We propose that tool calls report their progress explicitly while they run, and we measure what that takes. A census of four public agent corpora finds a readable signal in most tool time once it is revealed, in two strengths: a fraction of the work remaining, or an accurate signal that the end is near. A harness recovers it without changing what the agent sees, at no measurable cost to the agent’s benchmark score. At the points where a KV cache decision is made, the reported progress is between several times and an order of magnitude more accurate than the best published predictors, and it stays accurate when the environment changes. Plugged into a production engine through a few small hints, it cuts the p90 time to first token (TTFT) after a tool call by 20.7% (HBM only) and 20.8% (HBM + DRAM) against LRU, close to an oracle. A serving system should not guess what its tools can tell it.
[AI-17] Infinite-Parameter LLM s: Generating and Adapting Weights from Live Data
链接: https://arxiv.org/abs/2609.18842
作者: Jinli Hu,Ross M. Clarke,Yichuan Zhang,José Miguel Hernández-Lobato
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The scaling laws hold that a language model grows more capable with more parameters and more training data, and Mixture-of-Experts (MoE) architectures have ridden these laws to remarkable results, activating only a fraction of an enormous stored parameter bank for each token. That success is built on static pretraining data. A deployed model faces a different world, where much of the data that would make it more useful is not in its training set but in the live interaction it is currently handling, such as the facts a user supplies or the corrections they give. A conventional model cannot learn from this data, because its weights are frozen after training. Instead, the knowledge and behaviour supplied at run time are placed in the prompt, by retrieval or instruction, and re-read on every request only to be discarded once the request ends. We ask how an architecture could learn from live interaction by writing it into its weights. Taking inspiration from MoE, we propose the \textbfInfinite-Parameter LLM. A compact hypernetwork turns the data given at run time into a low-rank modulation of a shared base network, so the feed-forward weights are generated from live data rather than stored in a fixed bank. Where prior weight generators read the context once and freeze, we carry a Bayesian belief over the generator’s latent code and update it online, so the effective weight is re-derived from that evolving belief as the session proceeds rather than fixed after one read. The stored footprint stays fixed, yet the weights the model can compile are effectively infinite. For the knowledge and behaviour supplied at run time, carrying them in the weights rather than the prompt is amortized in compute, frees the context window, persists across turns, and can generalise better than in-context use. We specify an evaluation protocol that tests exactly this against in-context learning and retrieval.
[AI-18] ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks
链接: https://arxiv.org/abs/2609.18805
作者: Jeonghye Kim,Minseon Kim,Young Jin Kim,Matheus Pereira,Marc-Alexandre Côté,Alessandro Sordoni,Xingdi Yuan,Zhengyan Shi
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Coding agents are typically evaluated with desired behavior specified through issues or instructions. In practical web development, however, agents may need to infer behavior from working software and implement it in an incomplete application. We introduce ProgramDistill, a benchmark evaluating coding agents on features discovered through interaction with fully functional reference applications. We build ProgramDistill by factorizing applications into features of different granularities, each associated with replayable behaviors executable via its gold patch. Our pipeline, mine-craft-patch, discovers 1,975 replay-verified behaviors across 26 applications and constructs 4,063 tasks without human intervention. Across nine frontier coding agents, GPT-6 Astra and Claude Opus 5 achieve 49.2% and 28.8% success on cumulative workflows in full-application reconstruction. In partial-application reconstruction, success falls from 100% to 64.0% and from 96% to 32% as restoration depth increases from 1 to 8. ProgramDistill thus provides a scalable benchmark with controlled difficulty for evaluating and diagnosing coding agents, and a natural basis for future curriculum-based training.
[AI-19] CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents
链接: https://arxiv.org/abs/2609.18779
作者: Jiaxuan Jiang,Liyuan He,Zhixuan Fang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Current Mixture-of-Agents (MoA) paradigms generally treat query routing and agent fine-tuning as separate processes, limiting their ability to respond to evolving agent capabilities. This disconnect prevents routing strategies from adapting to evolving agent capabilities during post-training and prevents agents from achieving synergistic data-driven specialization. To resolve this, we introduce CERA-MoA (Co-Evolving Router with continually learning Agents for Mixture-of-Agents), an iterative reinforcement learning framework where the dynamic router and independent agent policies co-evolve. We design a predictive familiarity estimator that leverages mid-layer hidden states to evaluate semantic competence among agents, avoiding the overhead of full rollouts. Based on these familiarity scores, a cumulative-threshold adaptive routing mechanism dynamically activates a tailored minimal agent subset, achieving a trade-off between task performance and efficiency. By proactively allocating targeted training samples to agents based on their evolving competence, CERA-MoA promotes capability differentiation. Extensive experiments across various domains demonstrate that CERA-MoA outperforms state-of-the-art static-agent routing and fix-workflow fine-tuning baselines.
[AI-20] Version- and Scope-Aware Question Answering over Normative Documents: A Deployed System and an End-to-End Evaluation at Production Scale
链接: https://arxiv.org/abs/2609.18769
作者: Liuyin Wang,Shuaipeng Jin,Jiwei Shi,Jensen Hsu
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 1 figure, 3 tables
Abstract:Correctly answering a question grounded in normative documents often depends on information outside any single passage: whether the retrieved document is the version currently in force; whether it applies to the jurisdiction, subject (such as an institution or applicant), and date at issue; and whether each normative claim can be traced to its supporting source text. Hosted retrieval services have substantially lowered the engineering cost of building an initial system over such corpora, making “upload the documents and ask” a common default. We evaluate this default on approximately 73,000 candidate normative documents supplied to a production deployment. The evaluation uses a stratified sample of 200 questions from our published benchmark, with a gold source document for every question; the released sampling rule reads no system outputs or scores. We compare the hosted service with a governed system that resolves version and scope through explicit rules before generation. The governed system scored 97.7 overall, while the hosted service scored 88.1, a gap of 9.6 points computed from unrounded means. The question set, the answer text evaluated for both systems, the scores, and the scripts used to reproduce the reported benchmark statistics are public. The governed configuration has operated as a commercial product since January 2026 and serves 1,126 registered users; named customer organizations include Zhipu AI and Lecheng Health. By mid-April 2026, it had reached roughly 100,000 calls per workday.
[AI-21] Clueing up LLM s with Tool-Augmented Deductive Reasoning
链接: https://arxiv.org/abs/2609.18736
作者: Rebecca Ansell,Autumn Toney-Wails
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Despite recent advances in large language models (LLMs), performing logically consistent deductive reasoning over extended interactions remains challenging. Tasks that require integrating evidence across multiple reasoning steps, maintaining consistency with prior inferences, and updating beliefs under new constraints can surface limitations in current models while providing a useful testbed for evaluating reasoning enhancements. In this paper, we implement a text-based, multi-agent version of the classic board game Clue as an environment to evaluate multi-step, agentic deductive reasoning. In this setting, agents must infer hidden information from a sequence of observations, maintain consistency across turns, and reason over an evolving set of logical constraints. We instantiate six LLM-based agents (GPT-4o-mini and Gemini-2.5-Flash) as players that engage in turn-based gameplay; using three agents per model family, we establish baseline performance across repeated games. We then introduce a tool-augmented approach in which a structured possibility matrix converts implicit game state from generated reasoning logs into an explicit representation of remaining possibilities. The possibility matrix encodes extended-turn memory and deductive constraints, offloading these tasks from the agent. We compare this approach against the baseline to evaluate how tool augmentation supports reasoning quality and task success for autonomous agents in a strategic reasoning environment.
[AI-22] Which LLM is Best for Translating Natural Language Goals to PDDL
链接: https://arxiv.org/abs/2609.18731
作者: Tomas Balyo,Lukas Chrpa,G. Michael Youngblood
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Bridging the gap between human intent and machine execution remains a challenge in automated planning, where expressing goals in formal languages like PDDL restricts accessibility to non-experts. This paper empirically evaluates whether current Large Language Models (LLMs) can reliably translate natural language testing goals, written in informal language by video game testers, into well-formed PDDL targets suitable for classical planning. We present a carefully designed prompt template, integrating insights from iterative experimentation, aimed at maximizing both accuracy and response coherence from multiple state-of-the-art LLMs. Six contemporary models are systematically assessed on correctness, speed, and error tendencies using real-world, domain-specific benchmarks. All models demonstrate high correctness, exceeding 92%, with Gemini 2.5 Flash achieving the highest accuracy at 96% and the lowest incidence of false positives, while GPT-4.1 leads in response speed. Despite these advances, critical distinctions exist in model performance, and occasional failures arise from language ambiguity and limitations in domain representation. Our analysis underscores both the significant progress and ongoing gaps in enabling LLMs to act as robust bridges between natural language objectives and automated planning pipelines.
[AI-23] Beyond Truncation: Rethinking LLM Decoding as Ensemble Pruning EMNLP2026
链接: https://arxiv.org/abs/2609.18723
作者: Dunyao Xue,Chengshuo Du,Zhengbo Wang,Wenlin Dai,Cheng Meng
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to EMNLP 2026 Main Conference
Abstract:We introduce Mahalanobis-Ensemble Decoding (ME-Decoding), a novel Large Language Model (LLM) decoding framework that frames candidate token selection as ensemble pruning. Existing selection strategies rely predominantly on scalar probabilities, ignoring geometric semantic relationships and causing candidate redundancy. Meanwhile, current geometry-aware methods often require complex optimization or directly reweighting the original token probabilities, leading to significant computational overhead or inference instability. To address this, we formulate decoding as a subset optimization problem using a Mahalanobis distance-driven objective to enhance semantic diversity while preserving high probabilities. Specifically, we dynamically discount redundant generation paths using a token similarity matrix, constructed via an adaptive-bandwidth kernel over token embeddings. We further devise an efficient greedy selection algorithm with near-linear complexity in the candidate size under early stopping, while establishing its theoretical approximation guarantees. This renders ME-Decoding a robust, plug-and-play module with negligible inference overhead. Extensive experiments across diverse reasoning and generation tasks demonstrate that our method consistently achieves strong performance.
[AI-24] Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening
链接: https://arxiv.org/abs/2609.18708
作者: Yizhuo Li,Jianhao Yan,Yun Luo,Zhi Wang,Futing Wang,Rong-Xi Tan,Kanghui Tian,Ganqu Cui,Ning Ding,Peilin Zhao,Yafu Li,Yu Cheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In reinforcement learning for large language models, Proximal Policy Optimization (PPO) commonly uses a critic to estimate state values and reduce the variance of policy updates. However, we uncover a systematic failure mode in PPO critics, which we call Value Flattening: state values, estimated from multiple Monte Carlo continuations, change sharply across intermediate states while critic predictions remain comparatively flat. We further observe this phenomenon in a controlled FrozenLake environment and find that it becomes more pronounced as the state space grows. Our theoretical and empirical analyses relate Value Flattening to an implicit variance penalty in the critic loss and redundant updates from temporally correlated states with similar gradients. Motivated by these findings, we introduce SParse Proximal Policy Optimization (SP ^3 O), which applies the value loss to only a few well-separated states in each response to mitigate both effects. Experiments on Qwen3-Base show that SP ^3 O with only three states supervised per response can mitigate Value Flattening and consistently improve the learned policy across model sizes and evaluation suites. Together, our results identify Value Flattening as an important yet overlooked failure mode of critic learning in standard PPO and show that a simple sparse supervision strategy can mitigate it.
[AI-25] Echo: Learning-based Matching Decompilation using Trusted Back Translation
链接: https://arxiv.org/abs/2609.18706
作者: Jun Bi,Xiangxin Fang,Aarsh Chaube,José Wesley De Souza Magalhães,Rodrigo C. O. Rocha,Michael O’Boyle
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 19 pages, 8 figures
Abstract:Neural decompilers can recover readable and recompilable source code from binaries, but their predictions remain difficult to trust. Matching decompilation addresses this problem by searching for source code whose recompiled assembly exactly matches the target, providing stronger evidence of correctness. However, exact matching remains challenging for optimized binaries under unknown compilation configurations. We present Echo, a matching decompilation system based on trusted back-translation. Our key insight is to use compilation not only for verification, but also as trusted feedback to guide iterative search. Echo first uses a domain-specific model to generate candidate programs and compilation configurations. It recompiles these candidates, measures assembly-level similarity, and synthesizes promising code-configuration pairs. Remaining mismatches are then progressively repaired using rule-based rewriting, neural refinement, and reasoning-based refinement. We evaluate Echo on function-level benchmarks and the Mirai malware binary. Compared with the strongest baseline, Echo produces 2.43x more exact matches on average and achieves the highest structural similarity to ground-truth source code. On Mirai, Echo matches 2.75x and 7.4x as many functions as GPT-5.6 and Codex, respectively. Comments: 19 pages, 8 figures Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.18706 [cs.CR] (or arXiv:2609.18706v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.18706 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-26] he Uneven Impact of Generative AI on Student Learning: Examining the Roles of Reliance Evaluation Literacy and Course Policy in AI-related Courses
链接: https://arxiv.org/abs/2609.18676
作者: Lydia Manikonda,Mei Si,Sirajam Munira,Oshani Seneviratne,Kristin Bennett
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注: Paper under review
Abstract:Generative artificial intelligence (GenAI) is changing how students learn, yet the roles of course context, cognitive reliance, evaluation literacy, and early reliance remain underexplored. Using survey responses from 118 students across 12 AI-related courses at our institution, we examined differences in GenAI use and perceived learning experiences. We identified four user clusters: high-use students reporting many benefits, light users reporting less reliance and fewer benefits, and two moderate-use groups reporting different levels of benefit. We also found significant differences between free- and premium-version users, single- and multiple-tool users, and students experiencing different instructor policies. In multivariable regression models, academic benefit was associated with early reliance and academic task support; positive impact was associated with cognitive reliance, academic task support, confidence in GenAI reliability, and instructor policy; and negative impact was associated with early reliance and attitudinal change. The association between early reliance and negative impact became stronger as evaluation literacy increased. Finally, perceptions of GenAI-enhanced learning appear to reflect cognitive, performance, and self-efficacy benefits, while concerns about stress and diminished critical thinking are associated with lower perceived learning benefits. These findings suggest that institutions need better policies to address such inequities so that institutions can enable students to benefit from increasingly capable AI systems.
[AI-27] Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification
链接: https://arxiv.org/abs/2609.18673
作者: Seungmin Seo,Oleg Aulov,P. Jonathon Phillips,Kevin Mangold,Jonathan Eskin
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Accepted to IJCB 2026 (Main Track)
Abstract:Speaker de-identification (SDID) aims to preserve privacy by concealing speaker identity while maintaining speech utility. However, current evaluations often reduce privacy to a single dimension - biometric verification performance - typically measured by Equal Error Rate (EER). This narrow focus ignores critical leakage channels, such as soft biometric inference, embedding-level re-identification, and structural template similarity, which threaten the unlinkability and irreversibility of biometric references. We propose a holistic evaluation framework across five complementary metrics: (i) EER, (ii) soft biometric leakage score , (iii) cumulative match characteristic re-identification analysis, (iv) canonical correlation analysis and Procrustes embedding alignment, and (v) intelligibility via word error rate and semantic similarity. Evaluating five SDID systems from the IARPA ARTS program, we demonstrate that these metrics capture independent dimensions of information leakage. Our results indicate that reliance on a single metric can misrepresent the privacy properties of an SDID system.
[AI-28] CoRe-MARL: Cooperative Redistribution Under Unknown Dynamics Using Recurrent Multi-Agent Reinforcement Learning
链接: https://arxiv.org/abs/2609.18639
作者: Naimur Rahman Chowdhury,Shatabdi Sen Prapti,Md. Salehin Seyam,Limon Bin Hossain
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Emergency management assistance programs, such as relief distribution, are essential for delivering necessary supplies to affected communities. However, these programs operate in a decentralized network of local centers that face uncertain local demand and supply dynamics, resulting in inconsistent avail- ability of local services. Redistribution of supplies among these local centers reduces these imbalances, but the centers often make decisions independently, with limited information and disrupted transportation. This study develops CoRe-MARL, a cooperative multi-agent reinforcement learning (MARL) framework, by formulating a decentralized partially observable Markov decision process (Dec-POMDP). We treat each center as an agent that learns a redistribution policy to improve the service in the worst-case region and reduce the service gap across regions while protecting network-wide service. We incorporate a recurrent network that captures evolving supply and demand dynamics without direct observation, while multi-agent proximal policy optimization (MAPPO) enables centralized training and decentralized execution (CTDE). We evaluate the framework in a simulated environment with diverse trajectories, where exact dynamics are not observed by actors and the MAPPO critic. We compare the recurrent MAPPO with the recurrent independent PPO (IPPO) and a local only heuristic, and find that MAPPO reduces the service gap across local centers and enhances service for the worst-served center while maintaining competitive network-wide service. The recurrent MAPPO also shows consistent performance across diverse trajectory patterns, demonstrating its ability to adapt to evolving dynamics. The findings demonstrate the capability of cooperative learning for decentralized redistribution and improving equitable service under uncertain and evolving dynamics.
[AI-29] Online Robust Reinforcement Learning Through Monte-Carlo Planning
链接: https://arxiv.org/abs/2609.18599
作者: Tuan Dam,Kishan Panaganti,Brahim Driss,Adam Wierman
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Monte Carlo Tree Search (MCTS) is a powerful framework for solving complex decision-making problems, yet it often relies on the assumption that the simulator and the real-world dynamics are identical. Although this assumption helps achieve the success of MCTS in games like Chess, Go, and Shogi, the real-world scenarios incur ambiguity due to their modeling mismatches in low-fidelity simulators. In this work, we present a new robust variant of MCTS that mitigates dynamical model ambiguities. Our algorithm addresses transition dynamics and reward distribution ambiguities to bridge the gap between simulation-based planning and real-world deployment. We incorporate a robust power mean backup operator and carefully designed exploration bonuses to ensure finite-sample convergence at every node in the search tree. We show that our algorithm achieves a convergence rate of \mathcalO(n^-1/2) for the value estimation at the root node, comparable to that of standard MCTS. Finally, we provide empirical evidence that our method achieves robust performance in planning problems even under significant ambiguity in the underlying reward distribution and transition dynamics.
[AI-30] Reasoning through Evolution: Automatic Meta-path Discovery for LLM -based Fake News Detection ACM-MM2026
链接: https://arxiv.org/abs/2609.18597
作者: Ziyi Zhou,Xiaoming Zhang,Hui Pang,Yuting Zhang,Tiesunlong Shen,Bingyu Yan,Erik Cambria,Litian Zhang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted by ACM MM 2026, Oral
Abstract:Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which require substantial labeled data and exhibit limited generalization. Although large language models (LLMs) exhibit strong reasoning capabilities, directly feeding them raw propagation graphs creates a significant modality mismatch and severe information overload, making structure-aware reasoning unreliable in zero-shot and few-shot settings. To bridge this gap, we propose MAGER, a multi-agent genetic evolution framework that automatically discovers meta-paths optimized for LLM reasoning. By compressing complex propagation graphs into informative subgraphs, the evolved meta-paths alleviate both information overload and modality mismatch, enabling frozen LLMs to perform structure-aware veracity reasoning. We further introduce a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to strengthen classification and reasoning. Extensive experiments show that MAGER substantially improves frozen LLMs as standalone fake news detectors in data-efficient settings. Our code is available at this https URL.
[AI-31] Recursive Reasoning or Statistical Extrapolation? In-Context Learning in Multi-Agent Interdependent Decision-Making
链接: https://arxiv.org/abs/2609.18591
作者: Yu Liu,Wenwen Li,Yifan Dou,Guangnan Ye
类目: Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:In-context learning (ICL) enables large language model (LLM) agents to improve decisions using interaction history, yet it remains unclear whether such improvement reflects refined internal reasoning or mere extrapolation of statistical patterns. To disentangle these mechanisms, we study LLM agents in multi-agent incomplete-information games that require recursive belief reasoning. By constructing a public goods game and manipulating the statistical structure of historical feedback, we evaluate decision quality against a history-independent rational expectations equilibrium (REE) benchmark. Our experiments reveal that when historical statistical patterns are disrupted, the benefits of longer context largely vanish, degrading decision quality to the no-context baseline in a way sharply amplified by stronger strategic interdependence. These results suggest that, in such strategic environments, ICL behavior is more consistent with statistical extrapolation than with strategic reasoning. Our work extends the mechanistic study of ICL to strategic multi-agent settings, introduces REE as a diagnostic tool for distinguishing reasoning from extrapolation, and provides a reusable framework for probing the boundaries of LLM reasoning in recursive belief tasks.
[AI-32] Label-free steering: Compressing test-time reinforcement learning into bias-only subspaces
链接: https://arxiv.org/abs/2609.18587
作者: Naveen Vakada,Mingyuan Li,Shaoxiong Ji
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Test-time reinforcement learning (TTRL) enables models to improve their reasoning without relying on labeled training data, but existing approaches typically optimize a large fraction of the model parameters. This raises a natural question: can effective test-time adaptation emerge when both the reward signal and the optimization space are severely restricted? We answer this question with label-free bias-only TTRL, which uses majority-vote pseudo-labels as rewards and optimizes only approximately 100K bias parameters while keeping the pretrained backbone frozen. On MATH-500, our approach reaches 76.67% accuracy, slightly exceeding our own labeled bias-steering reproduction while optimizing 76,000x fewer parameters than full-parameter TTRL. The same training procedure improves performance across vision-language and audio reasoning tasks, including MathVista, AI2D, LogicVista, and MMAU. We further show that the learned steering vectors transfer to 4,500 held-out MATH problems, indicating that the adaptation is not limited to the problems used during test-time optimization. Finally, we analyze why this highly restricted adaptation can work, showing that majority-vote reliability improves with rollout consensus and that bias subspaces with greater accessible gradient energy exhibit stronger downstream trainability. These results demonstrate that substantial test-time adaptation can emerge from optimizing a tiny bias-only subspace using entirely label-free rewards.
[AI-33] Interpretable Patch-Based Deep Learning for Wildfire Spread Prediction from Ensemble Simulations
链接: https://arxiv.org/abs/2609.18555
作者: Marcin Lawenda,Aleksandra Krasicka,David Caballero,Luis Torres,Łukasz Szustak
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 7 figures
Abstract:Wildfire spread is traditionally predicted using physics-based simulators, which are physically interpretable but whose cost increases with each additional ensemble member. We ask how well deep learning surrogates can reproduce these simulations at a fraction of this cost, training them on 10,584 fire spread simulations at 2m resolution for the Rectoret region in Catalonia, Spain. Four architectures are compared: a patch-based U-Net, a transfer-learned ResNet-50, a physics-informed network constrained by the wind-driven advection equation and a Swin-Unet transformer. Among the terrain and vegetation variables, only surface fuel load predicts burn probability with any strength (r = 0.27) and including it lowers prediction error by 21%. The remaining variables correlate weakly and are highly duplicative. Next, an experiment with saliency, occlusion and rotation demonstrates the models’ learning. Convolutional models rely primarily on distance from the current fire front, while Swin-Unet assigns more weight to fuel and terrain, a finding also noted in an unrelated wildfire dataset. When applied without retraining to the second region, Pedriza, all three convolutional models still predict fire spread, losing accuracy by a small but systematic margin.
[AI-34] RIPROBE: Probing Task Separability Beyond Classification for XAI
链接: https://arxiv.org/abs/2609.18525
作者: Amirhossein Sadough,Freek Hens,Aleksa Bokšan,Mohammad Mahdi Dehshibi,Mahyar Shahsavari
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 5 pages, 3 figures, 16 references
Abstract:Modern evaluation of learning pipelines often reduces to downstream accuracy, leaving open the question of why tasks succeed or fail. TriProbe addresses this gap with a multi-level probing framework for explainable diagnosis of task separability. Rather than treating models as black boxes, TriProbe traces how separability evolves across inputs, learned features, and final classifiers. It decomposes multi-task problems into binary subtasks and applies three complementary probes: a Foundational Probe on input spaces, a Latent Probe on feature representations, and a Final Probe on classifier outputs. Using Maximum Fisher’s Discriminant Ratio as a principled separability metric, TriProbe identifies bottlenecks and affected task pairs. Experiments on the Roshambo sEMG benchmark show how TriProbe reveals hidden breakdowns, guiding data collection, validation, and architecture design.
[AI-35] VoiceTrace: A Benchmark and Retrieval Framework for Who-Said-What Speech Retrieval
链接: https://arxiv.org/abs/2609.18521
作者: Aaron Yee,Fengjie Lu,Jiarui Hai,Chenang Jiang,Helin Wang,Siwei Tu,Weitao You,Lingyun Sun
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注:
Abstract:Speech retrieval has become increasingly important as spoken content continues to grow across meetings, lectures, podcasts, and videos. Existing benchmarks and models have advanced semantic search over spoken content, but largely focus on \emphwhat is said while overlooking \emphwho says it. In many real-world scenarios, however, users need to retrieve speech based jointly on semantic content and a target speaker, where the speaker may be specified naturally through a reference speech utterance rather than a predefined identity. To address this gap, we introduce \textbfVoiceTrace-Bench, a benchmark for hybrid speech retrieval in which each query combines text specifying \emphwhat to retrieve with reference speech specifying \emphwho to retrieve. This setting requires models to integrate complementary semantic and speaker information directly from heterogeneous query inputs. Motivated by the joint audio-text modeling capabilities of audio-language models (ALMs), we develop \textbfVoiceTrace, a two-stage retrieval framework consisting of \textbfVoiceTrace-Emb, an embedding model that learns unified representations for efficient large-scale retrieval, and \textbfVoiceTrace-Reranker, a reranking model that jointly examines each query–candidate pair for fine-grained relevance estimation. Experiments show that VoiceTrace achieves state-of-the-art performance on established semantic speech retrieval benchmarks, while substantially outperforming cascade-based approaches on VoiceTrace-Bench, demonstrating its effectiveness for both conventional semantic retrieval and the new hybrid retrieval setting.
[AI-36] AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed Adaptive Swarm Execution
链接: https://arxiv.org/abs/2609.18520
作者: Jiabin Lou,Yirong Yang,Haopeng Wang,Xuxin Lv,Xinyu Liu,Diyuan Hou,Xuehong Liu,Rongye Shi,Wenjun Wu
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 7 figures
Abstract:Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and coordinated action. UAV swarms embody this paradigm by coordinating multiple vehicles in tasks such as search, inspection, and tracking. Recent advances in large language model (LLM) agents have strengthened natural-language task understanding and high-level planning, providing a flexible semantic interface between mission descriptions and collective behavior. While these advances expand semantic reasoning, applying LLM agents to UAV swarms raises challenges in grounding model decisions in executable capabilities, reconciling global task reasoning with distributed execution, and using mission-specific experience for continual adaptation. To address these challenges, we introduce AeroWeaver, an embodied-agent harness that weaves individual UAV skills into coordinated mission-level behavior. AeroWeaver connects semantic decisions to governed skills, organizes role-conditioned local agents for distributed coordination, and uses role-indexed state-action-reward experience to refine skill selection online. Experiments and runtime validation show that AeroWeaver maintains valid skill execution under tested conditions and supports body-local multi-UAV operation without a central agent generating joint actions from global context, while reward-guided online updates provide a training-free path for adaptive learning swarm agents from accumulated execution experience. Code: this https URL.
[AI-37] Beyond Routine Compliance: Cunning Data Cultivates Safety Vigilance in Large Language Models
链接: https://arxiv.org/abs/2609.18515
作者: Youjia Wang,Lin Xu,Yang Sun,Yuxiao Lu,Chengfang Fang,Jie Shi
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 18 pages
Abstract:Safety alignment teaches large language models (LLMs) to recognize harmful requests and reject risky instructions. Yet aligned models can fail when harmful intent is concealed within seemingly benign contexts. Robust safety therefore requires both knowledge of safety boundaries and \textbfvigilance: the ability to detect unusual premises, misleading reasoning, and latent risks beneath surface-level semantics. Vigilance requires models to scrutinize a request’s underlying intent and assumptions before acting. To cultivate this capability, we introduce \textbfcunning questions, which are not necessarily safety-related but contain misleading premises, atypical reasoning, or subtle inconsistencies. We hypothesize that learning to look beyond such reasoning traps can transfer to safety-critical scenarios. Experiments show that Cunning training improves robustness to out-of-distribution jailbreak attacks and strengthens subsequent safety fine-tuning. Furthermore, augmenting an existing state-of-the-art safety alignment pipeline with Cunning establishes a new state of the art across our evaluated settings, reducing mean ASR across nine backbone–benchmark combinations from 17.40% to 15.05%. Trace analysis after matched safety fine-tuning suggests that safety judgments are more likely to govern responses before harmful planning begins. A conditional theoretical analysis further characterizes when invariance learned from cunning data can transfer to safety-related inputs. These findings suggest that cunning data can strengthen model vigilance and complement conventional safety alignment.
[AI-38] MiST: Mid-Training LLM s for Cybersecurity
链接: https://arxiv.org/abs/2609.18496
作者: Oded Ovadia,Elad Ben Zaken,Elad Guttman,Orly Moreno Kadosh
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Cybersecurity combines high-stakes analysis with complex technical language, making it an impactful and challenging domain for LLMs. We present MiST (Mid-trained Security Transformer), a suite of 8B and 32B models that achieve strong performance on public cybersecurity benchmarks. We use mid-training as an intermediate adaptation stage between general pre-training and cybersecurity training. Rather than performing continual pre-training over large volumes of raw domain text, we curate a compact, expert-vetted seed corpus, and transform it into high-quality domain-specific synthetic training data. The final MiST checkpoints improve mean cybersecurity accuracy by +13.1 and +8.6 absolute percentage points over the corresponding Qwen baselines for 8B and 32B, respectively, corresponding to relative gains of +27.0% and +15.8%. Ablation results further show that these cybersecurity gains arise in the mid-training and supervised fine-tuning stages through a combination of the synthetic data generation flows. Furthermore, we show that MiST provides a stronger initialization for downstream task-specific fine-tuning adaptation and reinforcement learning.
[AI-39] Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs
链接: https://arxiv.org/abs/2609.18481
作者: Pietro Miotto,Lucia Mellini,Tommaso Marzi,Cesare Alippi,Elena Casiraghi,Alberto Paccanaro,Giorgio Valentini,Mauricio Soto-Gomez
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Biomedical knowledge graphs combine ontology-derived hierarchies with transversal associations among heterogeneous entities such as phenotypes, diseases, genes, proteins, and patients. This hybrid structure raises the question of whether hyperbolic embeddings, which naturally capture tree-like organization, remain useful beyond purely hierarchical graphs. We present a preliminary study of hyperbolic graph representation learning for Mendelian-disease differential diagnosis on a patient-integrated biomedical graph. Experiments on isolated ontology subgraphs show that hyperbolic models achieve strong performance in substantially lower dimensions than Euclidean baselines. We then evaluate the models on a link-prediction task that ranks candidate diseases for each patient. Results suggest that hyperbolic embeddings can exploit biomedical hierarchical structure while supporting diagnostic reasoning over heterogeneous patient-level graphs.
[AI-40] First Token Matters: Understanding Safety Collapse in Large Reasoning Models
链接: https://arxiv.org/abs/2609.18471
作者: Yizheng Yang,Haining Yu,Yuechen Wang,Yikai Hou,Xing Fu,Jinbo Yang,Tianqing Zhu
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 7 figures, 10 tables. Includes appendices. Accepted at CICAI 2026
Abstract:Large Reasoning Models (LRMs) exhibit strong problem-solving abilities, yet their safety alignment often degrades when handling harmful queries. Existing approaches to improving safety largely rely on additional training or preference optimization, while offering limited understanding of the internal mechanisms behind safety failures. In this work, we investigate this failure through a token-level positional analysis of refusal dynamics and identify a localized vulnerability at the onset of reasoning, which we term Onset Refusal Collapse (ORC). We find that the refusal-related signal of LRMs drops sharply at the first generated token under harmful queries, which is associated with unsafe response generation. Motivated by this finding, we propose SafeToken, a lightweight inference-time intervention that injects a learned continuous safety anchor precisely at reasoning onset. Despite updating only a single token embedding, SafeToken effectively mitigates ORC, improves safety on harmful-query benchmarks, and largely preserves reasoning utility. These results suggest that safety failures in LRMs can arise from a transient breakdown at the critical transition from understanding to generation.
[AI-41] Collective Loss of Control in LLM Agent Systems: An Epidemic Account of Mutation Contagion and Recovery
链接: https://arxiv.org/abs/2609.18460
作者: Xiangfan Wu,Zonghao Ying,Huiyu Wu,Xing Zheng,Huangsheng Cheng,Xiaorong Shi,Jing Guo
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:How does a multi-agent system evolve from a local deviation into collective loss of control? We propose an epidemic explanation organized around accidental mutation, contagion, and recovery. A spontaneous deviation creates a seed; communication enables other agents to adopt and retransmit its unsafe strategy; collective failure can emerge when propagation outpaces correction and containment. Thus, rare individual deviations can coexist with substantial collective risk. Motivated by reported OpenAI agent coordination incidents, we examine two ingredients of this mechanism. A deployment audit identifies implicit communication paths between nominally independent evaluation runs and verifies transport through a default Docker backend. RogueHandoff-20, a benchmark of 20 executable scenarios, tests recipient susceptibility by injecting unsafe trajectories generated by a modified Qwen-27B route. Across four native-pending routes, executed harm is 0-5% on normal tasks and 40-95% after injection, exceeding paired direct malicious requests by 5-45 percentage points. These results support low observed baseline harm alongside high conditional susceptibility; they do not establish natural rare-event rates or demonstrate an autonomous cascade. The account motivates complementary defenses: strengthen resistance and recovery alongside prevention of spontaneous deviations, and audit and restrict unintended communication paths that can turn local failures into collective loss of control.
[AI-42] he Mirag e of Calibrated Confidence: Trajectory-Independence of Verbalized Confidence in Vision-Language Models EMNLP2026
链接: https://arxiv.org/abs/2609.18453
作者: Jisoo Yang,Jaeho Han,Trung X. Pham,Junyeong Kim
类目: Artificial Intelligence (cs.AI)
备注: EMNLP 2026 Main
Abstract:A calibrated Vision-Language Model (VLM) can repeatedly self-correct, say “Wait, I should recheck,” arrive at the wrong answer, and still report high confidence. We find that this occurs because verbalized confidence is largely trajectory-independent in the VLMs and calibration methods we evaluate. We examine this through three complementary lenses: content variation, token masking, and the model’s own hesitation markers. We show that confidence is insufficiently sensitive to what the reasoning trajectory actually contains, and that calibration training can paradoxically worsen this disconnect. Since existing metrics like ECE and AUROC cannot detect this problem, we propose the Trajectory-Grounding Score (TGS) in two complementary forms: TGS-self, which compares confidence with and without access to the model’s own trajectory, and TGS-pair, which tests whether the model assigns higher confidence to correct trajectories than to flawed ones along the vision, reasoning, and answer axes. We propose TGS-Bench, a model-agnostic suite spanning 10 benchmarks with controlled good/bad trajectory pairs, and show that conventional calibration rankings diverge from trajectory-grounding rankings, exposing a blind spot in current evaluation practice.
[AI-43] Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving
链接: https://arxiv.org/abs/2609.18442
作者: Rongxiang Zeng,Linsen Cai,Jiafu Zhang,Yijie Zhong,Yide Tao,Shuai Wang,Nan Zheng,Hai L. Vu,Alvaro Garcia Hernandez,Yongqi Dong
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)
备注: 8 pages, 2 figures
Abstract:Safe motion planning in automated driving requires anticipating evolving traffic risks and deciding when to revise the current planned trajectory. We introduce RiskWorld, a risk-aware world modeling framework for shared occupancy forecasting and selective trajectory replacement. Spatial risk fields and temporal actor context are fused with visual bird’s-eye-view features. Flow-guided evolution transports occupancy and scene features, while signed residuals correct occupancy after transport. One forecast is generated per planning step and reused across candidates. Each candidate is compared with a current-state persistence reference, yielding a nonnegative collision-score correction. The trajectory selected by current-world evaluation serves as the planning anchor and is replaced only when additional predicted risk triggers intervention and an alternative satisfies component-wise constraints on predicted risk and trajectory error. Candidate geometries remain unchanged. We evaluate RiskWorld for open-loop planning on nuScenes using camera features, annotation-derived current and historical actor states, and dataset-provided map context. RiskWorld achieves the lowest collision rate at a long evaluation horizon of 3 s, and the second-best average L2 error among various state-of-the-art baselines, while running at 11.5 FPS on a single NVIDIA RTX 4090 with 90.81 M parameters. Within-setting ablations show that RiskWorld achieves lower collision rates than the current-state rescoring baseline, while forecast reuse enables additional candidates to be evaluated at low marginal computational cost.
[AI-44] WetRobo: A Reproducible Robot Kit for Coding Agents in Biological Laboratories
链接: https://arxiv.org/abs/2609.18435
作者: Yuna Oikawa,Kei Endo,Takanori Uzawa,Yunzhe Zhang,Manan Anjaria,Lerrel Pinto,Sherry Yang,Koji Tsuda
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 9 pages, 11 figures, 2 tables. Code and demonstrations: this https URL
Abstract:Automating biological research requires general-purpose, reproducible robot systems that allow individual wet-lab researchers to delegate robot tasks without performing teleoperation or neural-network training. Vision-language-action policies have been proposed for general-purpose arms, but can lose performance when their operating environment changes. We therefore built WetRobo, a robot kit that can readily transfer between laboratories. It consists of one robot arm, laboratory equipment (an incubator, a reagent bottle with a cap, and a Petri dish), the existing code that moves the arm, teleoperation demonstrations of each task that we recorded, and a general this http URL skill file. A biological experimentalist provides natural-language tasks without collecting local teleoperation training data or training a neural network. The coding agent observes the local laboratory and writes and executes programs, using external tools as needed for adaptation. We demonstrate use of WetRobo with OpenAI Codex (gpt-5.6-sol) on three successful tasks: lifting a Petri dish lid, removing a bottle cap, and opening the incubator door, all in real-world laboratories. The coding agent achieved the cap task in both laboratories, Lab X and Lab Y, whereas a VLA fine-tuned on Lab X demonstrations succeeded there but failed to transfer to Lab Y. These results point to a practical route for laboratory robotics: instead of training a policy for each laboratory, distribute a kit and let a coding agent adapt it in each laboratory. Code, demonstrations, and the evolved programs are available at this https URL.
[AI-45] HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition
链接: https://arxiv.org/abs/2609.18431
作者: Kamilia Zaripova,Nassir Navab,Azade Farshad,Annalisa Marsico
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Genomics (q-bio.GN)
备注:
Abstract:More than 300 million people worldwide are affected by one of over 7,000 known rare diseases, yet diagnosis remains difficult because patients initially present with incomplete and heterogeneous phenotypes. We present HPOQuest, a training-free framework for sequential phenotype acquisition in rare-disease diagnosis. Starting from a small set of observed patient phenotypes, HPOQuest maintains a probabilistic disease ranking and iteratively selects informative follow-up questions to support clinicians during patient assessment. Confirmed phenotypes update the disease ranking, while all responses update the candidate question set. Across four benchmark cohorts, HPOQuest substantially improves diagnosis from sparse initial phenotypes, with gains of up to 30% points at Recall@1 and 45% points at Recall@5. These results demonstrate that sequential phenotype acquisition can substantially improve rare-disease diagnosis from limited initial clinical evidence.
[AI-46] ERN: A Delta-rule Memory with a Seasonal Reference and Online Adaptation for Epidemic Forecasting
链接: https://arxiv.org/abs/2609.18407
作者: Shunya Nagashima,Yuta Funayama
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Weekly influenza surveillance counts guide vaccine distribution and public-health alerts, yet they are hard to forecast. Each region offers only a few seasons, waves shift in timing and height every year, and information that helps while a wave grows misleads after its peak, whereas last season’s shape stays informative for a year. Existing epidemic graph models and general forecasters read a short fixed window and treat all past information alike, so they neither exploit earlier seasons nor discard stale associations when the epidemic phase changes. To address these limitations, we propose TERN, a forecaster built around a delta-rule fast-weight memory that decays channel-wise and erases along a learned address under gates driven by local epidemic-phase features, combined with an explicit seasonal reference and online adaptation. On three Cola-GNN influenza benchmarks, TERN outperformed epidemic graph models and general forecasters, matched or exceeded seasonal references, and a controlled comparison confirmed the contribution of the memory itself.
[AI-47] Reliable Virtual Sensing: A Multi-Domain Benchmark for Robustness Under Sensor Failures
链接: https://arxiv.org/abs/2609.18396
作者: Jens U. Brandt,Noah C. Puetz,Alexander Windmann,Marc Hilbert,Elena Raponi,Thomas Bäck,Thomas Bartz-Beielstein
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Virtual sensing, the estimation of hard-to-measure quantities from available sensor measurements, is a critical enabler for control and monitoring in cyber-physical systems. However, when sensors fail, learning-based predictors can produce physically implausible estimates that propagate to system-level failures. We argue that real-world deployment demands robustness and introduce MuViS-C, the first multi-domain benchmark of robustness against common sensor failures in learning-based virtual sensing. Building on an existing nominal-performance benchmark and established corruption taxonomies, it covers ten sensor failure modes, from subtle drifts to catastrophic signal dropouts, at multiple severities. These are paired with complementary robustness measures capturing average error under corruption, relative degradation, and worst-case fragility. Across nine datasets from six domains, we benchmark six architectures spanning gradient-boosted trees and the major inductive biases for sequence modeling: convolution, recurrence, attention, and MLP-mixing. On the attention-based architecture, we further probe three robustification strategies. We find that (i) every model degrades substantially under corruption, becoming worse than a naïve predictor on at least one corruption setting, (ii) gradient-boosted tree ensembles achieve strong robustness, and (iii) dedicated robustification closes the gap between the attention-based architecture and the most robust models, though each strategy hurts nominal performance. The benchmark’s multi-domain design proves essential, as model rankings shift across datasets, and no single domain captures the full robustness picture. MuViS-C is open-source and extensible to new datasets, failure modes, measures, and models.
[AI-48] Cultural Competence in Context: A Large Language Model Passes the Turing Test in Finland
链接: https://arxiv.org/abs/2609.18394
作者: Otto Segersven,Pentti Henttonen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We report the results of a Turing Test conducted in Finland in the Finnish language. Because languages and cultural contexts are unevenly represented in LLM training data, we expected the model (ChatGPT 5.2) to perform worse in a Finnish-language Turing Test than in previously studied English-language US contexts. We also present model-generated role prompting as a replicable technique for conducting comparative LLM-based Turing Tests designed to improve construct validity. Contrary to our expectations, the LLM passed the Finnish Turing Test. A prominent source of error was participants’ reliance on linguistic cues, particularly colloquial Finnish, as markers of human authorship. We reframe the Turing Test from a test of intelligence to a comparative method for examining whether an AI system can display credible membership in a particular social world. Because its outcome reflects model capabilities, prompted identity, insider competence among human participants, and their AI literacy, the method provides a useful probe of the human-machine boundary across domains.
[AI-49] Bad Genius: Counterfactual-Guided Harness Evolution Beyond Task-Specific Shortcuts
链接: https://arxiv.org/abs/2609.18366
作者: Guojun Zhu,Xunheng Huang,Peng Yin,Jiahui Xie,Sanguo Zhang,Doudou Zhou
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: 28 pages, 6 figures; includes references and supplementary material
Abstract:Reliable agent evaluation is complicated by automatic harness optimization, which repeatedly uses a released benchmark B_\mathrmrel to guide a Proposer that edits prompts, memory, retrieval, tools, and control code around a fixed target agent. Task holdout varies semantic tasks but leaves the benchmark protocol fixed, so a “bad genius” Proposer can produce a cheating harness whose released-benchmark gain depends on a benchmark-wide shortcut. We introduce Counterfactual Harness Search and Evolution (CHASE), which casts harness evolution as constraint generation over validity-preserving benchmark counterfactuals. After each Proposer update, a Challenger searches for an executable protocol transformation with large gain destruction. A validity firewall checks that task semantics are preserved, while a confirmation set determines whether the counterfactual enters a finite archive. We formalize an exact shortcut-neutralized benchmark B_0 and establish statistical guarantees linking finite counterfactual archives to B_0 and characterizing sequential Challenger search. We evaluate CHASE on a synthetic benchmark and on OfficeQA, where CHASE retains strong released-benchmark gains while substantially reducing gain destruction under valid protocol changes.
[AI-50] Autonomy in Check: Governor-Mediated Adaptive Security at the Edge
链接: https://arxiv.org/abs/2609.18338
作者: Ijaz Ahmad,Ijaz Ahmad,Flavio Esposito,Erkki Harjula
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 9 pages, 7 figures
Abstract:Adaptive security at the network edge increasingly relies on automated planners, including rule-based controllers, learned policies, and LLM-assisted agents, that translate observations into enforcement actions. Once such a planner can influence live policy state, syntactic validity is not enough. A semantically wrong action, produced from incomplete or manipulated observations, can be faithfully executed by an enforcement substrate that cannot judge mission context. We address this problem by treating the boundary between planner output and kernel enforcement input as the primary security object. We propose a split-control architecture in which an untrusted planner emits typed security intents, a deterministic governor checks each intent against safety, resource, temporal-stability, and proportionality invariants, and only admitted actions are bound to signed receipts and compiled into pre-installed eBPF map updates. The paper formalizes this trust-boundary problem, defines three threat classes, develops the governor admission predicate, and reports an end-to-end prototype. Across rule-based and LLM-assisted planners on a Raspberry Pi 5 testbed connected to the university 5G Test Network, the governor admits, rejects, and bounds intents at microsecond cost without disrupting protected-flow regularity. The contribution is conceptual as much as empirical: adaptive security does not need to trust the author of an action. It needs a mediation boundary that decides whether the action is admissible.
[AI-51] Look Less Hear Better: Jointly Rewarded GRPO for Streaming ASR
链接: https://arxiv.org/abs/2609.18333
作者: Xiuwen Zheng
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Streaming automatic speech recognition (ASR) must be judged jointly on what it transcribes and on how quickly it commits each word. Delayed streams modeling (DSM) has become the dominant paradigm for streaming large audio-language models, exposing a structural delay \tau that bounds the decoder’s lookahead. We show that \tau is a poor proxy for user-perceived latency, and that the alignment-based supervision of DSM leaves latency on the table: the same forced-aligned transcript is used at every \tau , forcing the model to withhold words it could already commit. We introduce AWED, a word-level emission-delay metric defined relative to the acoustic end of each word, and post-train a DSM recognizer with GRPO under a reward that scores transcription accuracy and measured delay jointly. Trained at a single operating point ( \tau=6 frames), our model dominates both its supervised fine-tuning initialization and the Voxtral Realtime backbone across all evaluated lookahead budgets: it cuts WER by 30.8% relative at an 80,ms structural delay, and by 5.7% relative at 480,ms while lowering median AWED from 1.17,s to 1.04,s. Latency-rewarded post-training thus advances the accuracy–latency Pareto frontier of streaming ASR without architectural change.
[AI-52] Visual Compliance via Executable Safety Rule Entailment EMNLP2026
链接: https://arxiv.org/abs/2609.18328
作者: Jisoo Kim,TaeYoon Kwack,Jinwoo Jang,Honguk Woo(Sungkyunkwan University)
类目: Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026. 36 pages, 20 figures, 24 tables
Abstract:Recent advances in LLMs and VLMs have enabled safety systems to reason beyond simple risk patterns toward more contextual and semantic safety concerns. However, as risk patterns continue to evolve and safety rules become more complex, existing training-based end-to-end safeguards face persistent challenges in adaptability and explainable reasoning over complex safety rules. To address these challenges, we propose GuardEn (Guarding by Safety Rule Entailment), an executable safeguard framework that decomposes safety policies into atomic propositions through Safety-Rule Compilation, modeling their composition as executable code. At test time, Scene-Grounded Execution instantiates these atomic propositions with contextual visual information derived from scene graphs, enabling rule-grounded and interpretable safety reasoning. Experiments on SafetyVisionBench demonstrate the effectiveness of programmable safeguard for complex visual safety assessment, achieving an average improvement of 9.8 F1 points over the strongest baseline.
[AI-53] rajectory Learnability for Offline On-Policy Distillation with Imperfect Teachers
链接: https://arxiv.org/abs/2609.18321
作者: Yihao Ai,Weilong Yan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 14 pages, 3 figures
Abstract:Offline on-policy distillation gains efficiency by collecting student trajectories and teacher supervision once and reusing them throughout optimization. The same reuse makes imperfect supervision persistent. Since even strong teachers can fail, we ask \emphwhat remains learnable from imperfect teacher supervision? Teacher failure is only a coarse problem-level signal and does not imply that all supervision along the associated student trajectory is unhelpful. A natural alternative is to estimate teacher recoverability along the trajectory, but repeated continuations largely erase the efficiency advantage of offline distillation. We instead use teacher-successful problems to define a cheap reference for what the student can learn. We train on teacher-successful problems and measure how the likelihood of each observed token in trajectories from teacher-failed problems changes. We use these signed likelihood changes as an operational \emphlearnability signal: larger increases indicate behavior more strongly promoted by successful-only learning. We aggregate this signal into trajectory-level weights for the original distillation loss. Unlike continuation-based estimates, our learnability requires no additional generation and can be computed once from stored trajectories and model checkpoints. Across mathematical reasoning and code generation, our method improves an offline OPD baseline by up to 2.7 percentage points and matches or outperforms online OPD variants on multiple benchmarks. Despite the additional successful-only distillation stage, it uses 2 GPUs and about 22 GPU hours, compared with 3 GPUs and 36–48 GPU hours for representative online OPD methods.
[AI-54] A Study of the Reliability of Agent ic AI-Generated Programs
链接: https://arxiv.org/abs/2609.18298
作者: Ayesha Shafique,Barton P. MIller,Elisa R. Heymann
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic-AI based software development offers the promise of faster completion of the software, greater programmer efficiency, and more reliable code. The question is how can we verify these claims in an objective way? In this project, we attempted to answer this question based on three practices. First, we applied a typical best-practices agentic AI workflow for software development. Second, our target programs were ten well-known, release-quality human-written Linux utility programs so that we could compare the AI-generated code against a concrete ground truth. Third, we based our measure of reliability on a widely used testing technique, fuzz random testing. For this testing, we used both classic black box, generational testing and more modern coverage guided (gray box, mutational) testing using AFL++. We found that the AI-generated versions of the utility programs were typically as reliable - often more reliable - than the latest human-generated versions of these programs. While the AI-generated versions did have some failures, they were less common than the code from the standard repositories. Interestingly, the AI-generated code was less likely to have failures such as memory errors (such as buffer overflows) but more likely to have hangs such as infinite loops. In addition, we verified that generating robust and reliable software using agentic AI requires careful practice and human supervision. The quality of the code is highly dependent on the prompts and skills used, and how the human directing the process responds. We also demonstrated that using agentic AI workflow for software development (with its prompts and skills) can become a specification of the code that leads to cost-effective sustainability of the software.
[AI-55] What Counts as Strategic Reasoning ? A Systematic Mapping of Chess Research on Humans Engines and Language Models
链接: https://arxiv.org/abs/2609.18286
作者: Paolo Ciancarini,Remo Pareschi
类目: Artificial Intelligence (cs.AI)
备注: Under review; replication package available at this https URL
Abstract:Chess has long served as a model domain for studying search, expertise, decision-making, and artificial intelligence. The emergence of large language models (LLMs) has renewed the relevance of chess as a controlled environment for investigating strategic reasoning and comparing human and artificial decision-making. We present a systematic mapping study of recent research spanning human players, classical chess engines, neural and reinforcement-learning systems, LLMs, and hybrid approaches. The final map comprises 84 core study families, classified according to agent type, strategic-reasoning stages, and evaluation dimensions. The map reveals a literature strongly concentrated on situation assessment, evaluation, and action selection, while explicit planning, explanation, metacognition, and human–AI collaboration remain less explored. LLM research places particular emphasis on state representation and generalization, whereas grounded explanation appears more frequently in hybrid approaches combining language models with engines, expert knowledge, or other external structures. Two distinctions emerge that the map aggregates rather than resolves: hybrid systems differ in where and when heterogeneous capabilities combine, and evaluations that show improved human performance do not thereby establish human–AI synergy. We propose both as extensions of the mapping framework. We argue that chess provides a useful bridge between cognitive and computational perspectives on strategic reasoning, and identify explicit planning, grounded and faithful explanation, metacognitive calibration, and human–AI complementarity as directions for future research. Comments: Under review; replication package available at this https URL Subjects: Artificial Intelligence (cs.AI) ACMclasses: I.2.8; I.2.6; I.2.7 Cite as: arXiv:2609.18286 [cs.AI] (or arXiv:2609.18286v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.18286 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-56] Building Trust in Artificial Intelligence: A Necessity for Railway Applications
链接: https://arxiv.org/abs/2609.18278
作者: Lefebvre Renard Clément,Lébé Vincent,Da Silva Ribeiro Pereira Ricardo,Sundell Johan,Jaoul Arnaud Saiah Kenza,Mijatovíc Nenad
类目: Artificial Intelligence (cs.AI)
备注: Transport Research Arena 2026, pre-print
Abstract:Artificial Intelligence (AI) is currently only applied to non-safety critical applications due to the strict standards and regulations for railway industries. We propose to review the three main fields necessary to increase trust in data science and AI algorithms and reach compliance: robustness, Operational Design Domain (ODD), and explainability. Robustness is the ability of an AI system to maintain its level of performance under any circumstances (ISO24029). ODDs allow the explicit definition of operating conditions under which a system is intended to operate, according to the recently published DIN DKE SPEC 99004. Explainability is the property of an AI system to express important factors influencing the AI system results in a way that humans can understand. Those 3 domains of research are already well investigated by nonrailway actors, with algorithms and methods ready to use for railway applications. A system view is necessary to ensure all trustworthy requirements interact continuously in a safe MLOps environment thereby fostering acceptance from regulators, operators and the public. Beyond safeguarding safety-critical applications, we aim to show that fostering deep trust in AI, as now required by regulatory frameworks worldwide, will unlock its full potential and transform the pace of adoption across mission-critical domains.
[AI-57] Who Audits Whom on What Substrate with What Evidence? An Independence-Graded Audit Protocol for Agent ic AI
链接: https://arxiv.org/abs/2609.18272
作者: Mohamed Chahine Ghanem
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Emerging Technologies (cs.ET)
备注: 19 pages
Abstract:Agentic AI systems plan, invoke tools and act with limited supervision; they are now both the subject of audits and, increasingly, the auditor. Independence, the foundation of assurance,is still applied to them as a binary. We argue that it must be graded along three orthogonal axes: principal independence (who controls the auditor), substrate independence (an auditor sharing the auditee’s foundation-model family, toolchain or guardrails fails with it) and evidence independence (whether evidence is attestable rather than self-reported). Each axis has precedent; the contribution is to grade all three on a single audit, aggregate them by the weakest link, and apply the same rubric when the auditor is itself an agent. We give the model a formal basis by transplanting the beta-factor model of common-cause failure from reliability engineering, a seven-step protocol whose outputs a third party can verify, a structural detectability analysis of a procurement-controls agent audited at three grades, and a Monte Carlo study of the model in which a conventional internal audit of an agent-a real audit team, a second agent, provider logsp-surfaces 5.9% of the faults it could in principle see and none at all in half the fault classes. We map the triple to the EU AI Act as amended, ISO/IEC 42006, UK public-sector risk-management guidance and audit-regulator practice.
[AI-58] BENCHCOMPASS: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLM s EMNLP2026
链接: https://arxiv.org/abs/2609.18270
作者: Sijie Dong,Wei Ren,Xuanwei Hu,Jiawei Luo,Zifan Wang,Xiaoyun Feng,Hui Cai,Lyuxin Xue,Peng Lu,Jianshe Li,Xin Zhang,Wei Wu
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 5 figures. Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026
Abstract:Payment operations are a critical financial infrastructure, but the value of large language models in this domain remains unclear because payment rules change quickly, evidence is fragmented, and decisions depend on transaction state, participant role, region, and payment rail. Existing benchmarks do not isolate whether failures come from missing payment-rule knowledge, poor use of supplied evidence, or brittleness under imperfect harness inputs. We introduce BENCHCOMPASS, a payment-domain benchmark whose construction pipeline builds scenario-grounded tasks from typed evidence packs, applies LLM-based quality checks, creates task-input attack variants, and reserves final item admission for domain experts. The release contains an expert-reviewed Pro benchmark covering payment knowledge, context-grounded scenario reasoning, and Attacked Open robustness, plus a lower-assurance Normal pool for inspection and future curation. Across 16 model variants, BENCHCOMPASS shows qualitatively different failure modes: missing parametric payment knowledge, incomplete reasoning over supplied rules, and failure to reject plausible but invalid workflows. The benchmark remains unsaturated: the best frontier model reaches 89.6% on Open Context-Grounded Reasoning and 81.7% under attacked inputs, while a representative 32B open-weight model reaches 69.8% and 42.6%. Benchmark data and code are available at this https URL.
[AI-59] REPAIR: Resolving Long-Tail Confusion in Scientific Retrievers via Fact-Verified Iterative Refinement EMNLP2026
链接: https://arxiv.org/abs/2609.18262
作者: Yerim Oh,Gunhee Kim
类目: Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026 (Main Conference). 30 pages, 5 figures, 20 tables. Code: this https URL
Abstract:Precise retrieval of scientific information is fundamentally constrained by long-tailed concepts and high fact-sensitivity of scientific corpora. These challenges often limit the effectiveness of dense retrievers and hallucination-prone LLM augmentation. To address this, we present REPAIR, a self-evolving data augmentation framework for scientific dense retrievers. REPAIR iteratively synthesizes training data to address knowledge gaps by cycling through diagnosis of long-tail concepts, API-guided evidence expansion, and differentiation via hard negative mining. This process effectively grounds retrieval in factual reality to resolve fine-grained distinctions. Extensive experiments demonstrate that REPAIR significantly outperforms 19 strong baselines on nine materials science and biomedical benchmarks. Our work highlights that diagnosing and factually augmenting data to long-tail deficits is essential for robust scientific retrieval.
[AI-60] Re2A: Situated Conversational Recommendation via Rubric-based Preference Reasoning and Alignment EMNLP2026
链接: https://arxiv.org/abs/2609.18249
作者: Dongding Lin,Jian Wang,Xiaoyan Zhao,Wenjie Li
类目: Artificial Intelligence (cs.AI)
备注: EMNLP 2026 MainConference
Abstract:Real-world recommendation scenarios are commonly grounded in shared physical environments during user-recommender interactions. This motivates situated conversational recommendation (SCR), a complex task requiring recommender assistants to jointly reason over dialogue history, co-observed scenes, and in-scene item attributes. However, current approaches struggle with this setting due to two intertwined challenges: accurately understanding situated user preferences throughout the conversation and generating responses that simultaneously satisfy user needs and grounded situations. To this end, we propose Re2A, a framework that formulates SCR as a structured reason-then-align process. We introduce rubric-based preference reasoning, which uses automated rubrics to guide the model toward producing explicit preference states. Based on these states, we propose a preference-conditioned optimization to align response generation with dual objectives: user preference satisfaction and situation consistency. Extensive experiments on two SCR datasets demonstrate that Re2A consistently outperforms state-of-the-art methods, delivering more precise, context-aware conversational recommendations. Our code is available at this https URL.
[AI-61] APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study
链接: https://arxiv.org/abs/2609.18219
作者: Shabir Ahmad Sofi,Bisma Majid,Mir Mohammad Yousuf
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: Accepted at The 6th International Multi-Conference on Artificial Intelligence Technology (MCAIT2026)
Abstract:Quantum Reinforcement Learning (QRL) represents policies as variational quantum circuits (VQCs), making it attractive for combinatorial optimization such as the Capacitated Vehicle Routing Problem (CVRP). On noisy intermediate-scale quantum (NISQ) hardware, however, decoherence degrades fidelity and destabilizes learning, and conventional error mitigation is applied statically without regard to the learning context. We introduce Adaptive Policy-Guided Error Mitigation (APGEM), a controller that selects among Zero-Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Readout Error Mitigation (REM) online, driven by a fidelity, entropy, and cost aware utility function and an epsilon-greedy rule over temporal-difference Q-scores. We evaluate on a realistic urban-logistics testbed, a Delhi-based CVRP over real landmarks with geodesic inter-node costs, exercised across five noise families and four severity levels. On this instance, the QRL agent outperforms constructive heuristics and approaches metaheuristics, while mitigation restores approximation ratios from 0.84-0.87 to 0.92-0.94 under high noise. The controller shifts from a CDR-dominated regime under short training horizons to a balanced deployment across all four techniques under longer horizons, indicating genuine regime-dependent selection. These preliminary results position adaptive, learning-aware mitigation as a practical route to noise-resilient QRL.
[AI-62] CPR: Combining global composing local performing and full-sequence refining in piano rendering with continuous autoregressive modelling
链接: https://arxiv.org/abs/2609.18216
作者: Chong Jing,Junan Zhang,Zhizheng Wu
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Prompt-conditioned piano MIDI-to-Music rendering aims to faithfully render target notes while reproducing the timbre of a reference recording. Existing approaches primarily follow two paradigms: autoregressive (AR) modeling and flow matching (or diffusion). Discrete-codec AR models provide causal temporal modeling, but quantization can discard acoustic detail. Flow matching better preserves acoustic structure in the cost of full-sequence attention costs and worse semantic structure. Continuous autoregressive models operate directly on continuous representations. It not only combines the condition-following ability of AR models and distribution-modeling capacity of flow matching but also bypasses the quantization bottleneck with lower computational costs. Building on this principle, we present Composer–Performer–Refiner (CPR) framework. Composer autoregressively predicts continuous hidden states, Performer generates 24kHz acoustic latents through local flow matching and Refiner then upsamples the waveform to 48 kHz. We further introduce Bottlenecked Representation Alignment (BREPA) and Modality–Time RoPE (MT-RoPE) to strengthen musical semantic structure in Composer hidden states and temporal alignments across modalities. Codes are available at this https URL
[AI-63] WFM: Wiki Foundation Model for Complex Agent ic Reasoning
链接: https://arxiv.org/abs/2609.18182
作者: Junnan Dong,Linhao Luo,Senlei Zhang,Gong Chen,Taian Guo,Yifei Yu,Rong Tao,Tao Guo,Qian-Wen Zhang,Siyu An,Ruizhi Qiao,Xing Sun
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented generation. While graphs have shown reliable advantages in providing structured evidence, the sparse graph representations naturally restrict machine readability and semantic density required for complex agentic workflows. Driven by this limitation, the entire industry is witnessing a paradigm shift from traditional sparse graphs to LLM Wiki, an agent-native knowledge representation that couples dense document contexts with markdown files containing multi-layered topological linkages. However, parameterizing such rich semantics is challenging to encode dense textual contexts using traditional sparse graph embeddings. Moreover, learning LLM Wiki with existing graph encoders could overwhelm distributed system overheads that hinder deployment in large-scale commercial scenarios. To this end, we propose a novel paradigm Wiki Foundation Model, i.e., WFM, tailored for scalable, agent-native representation and retrieval. Specifically, (i) we formalize a Wiki Graph schema that seamlessly bridges fine-grained structures with dense contexts, maintaining explicit topologies alongside continuous semantics; (ii) A query-conditioned attentive aggregation is tailored for rich wiki message passing and explicit attention variance regularization; (iii) We engineer an infrastructural NCCL boundary exchange protocol that hoists static partition indices and leverages fixed-shape GPU-to-GPU collectives, bypassing CPU serialization and memory copy overheads. Extensive evaluations across five long-term agent memory and multi-hop reasoning benchmarks demonstrate the remarkable performance of WFM, while achieving a 10.5 times training acceleration on distributed clusters.
[AI-64] MoRE: Mixture of Reused Experts
链接: https://arxiv.org/abs/2609.18176
作者: Eric S. Qiu,Utku Umur Acikalin,Justin Lovelace,Christian Belardi,Arjun B. Mulchandani,Carla P. Gomes,Kilian Q. Weinberger
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to the Conference on Language Modeling (COLM 2026)
Abstract:Mixture-of-Experts (MoE) architectures decouple model capacity from computational cost, yet incur high memory footprints as parameters grow linearly with the number of experts. Recurrent Transformers achieve parameter efficiency by reusing layer weights, but typically lack the capacity for competitive language modeling. We propose Mixture of Reused Experts (MoRE), a hybrid that shares expert pools across groups of adjacent layers. Each layer retains its own router but selects from a larger shared pool, expanding the diversity of routing combinations without additional parameters. To enable shared experts to distinguish between layers, we introduce lightweight learnable depth embeddings that condition each layer’s input before routing. Experiments across three model scales (114M-1.15B parameters) show that MoRE consistently achieves lower perplexity and stronger downstream performance than standard MoEs and state-of-the-art weight-sharing architectures at matched compute and parameter budgets, with only minimal modifications to existing MoE implementations.
[AI-65] Rethinking How We Evaluate Methodological Progress in Health AI
链接: https://arxiv.org/abs/2609.18134
作者: Florent Pollet,Matthew McDermott
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work better, and under which conditions. However, such progress is thought to be hindered by difficulties in reproducibility and in defining clinically meaningful evaluation tasks. We empirically study these barriers by re-implementing 12 historical and recent algorithms within a shared evaluation framework and evaluating them on two clinical datasets, MIMIC-IV and NWICU. We compare two complementary task families: expert-authored clinically meaningful tasks and generated tasks defined from randomly sampled event codes and prediction horizons. We ask whether relative algorithms comparisons transfer across task families and datasets, whether residual task heterogeneity contains useful methodological structure, and what a controlled comparison reveals about progress over the last decade. We find that aggregate pairwise comparisons transfer strongly across evaluation settings, including from randomly generated tasks to clinically meaningful tasks and across datasets. At the same time, clinically meaningful tasks exhibit greater task-method interaction, providing preliminary evidence that task properties can help explain when particular modeling choices are advantageous. Finally, newer algorithms do not consistently outperform earlier approaches: gradient-boosted trees remain highly competitive when paired with a modern, wide and sparse representation of the EHR. Together, these results suggest that useful methodological knowledge may require less task engineering than commonly assumed, while highlighting the importance of understanding the structured heterogeneity that remains across tasks and methods.
[AI-66] Symbolic Temporal Supervision of LLM Agents Using Contracts
链接: https://arxiv.org/abs/2609.18128
作者: Yifeng Xiao,Pierluigi Nuzzo
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:Large language model (LLM) agents augmented by tools can automate complex, multi-step tasks, such as web navigation, code generation, and workflow orchestration, by acting on external systems through tool calls. However, hallucinations, distributional instability, and adversarial manipulations in LLMs, and the irreversible consequences of certain tool calls can lead to harmful outcomes. Existing safeguards either grade recorded trajectories post hoc with stochastic LLM judges or block unsafe actions one call at a time, and no single deterministic artifact supports both roles. We present ContrAgent, a contract-based framework for symbolic temporal supervision of LLM agents. ContrAgent captures an agent’s behavior as a sequence of tool calls and formalizes it as a trace over a fixed set of checkable predicates. It then specifies required behaviors using assume-guarantee contracts in linear temporal logic over finite traces (LTLf). Each contract is compiled to a deterministic finite automaton (DFA) that serves two roles: gating agent actions online and evaluating recorded traces offline. A contract library, acting as a reusable knowledge base, is maintained independently of the agent’s model and can be applied across different agents within the same task domain. We show the effectiveness of our approach on four benchmarks spanning both roles, where ContrAgent matches state-of-the-art LLM-judge and rule-based guardrail baselines while producing deterministic, reproducible verdicts and, in the online mode, orders-of-magnitude lower per-call latency.
[AI-67] Designing Agent ic AI Workflow Portfolios under Imperfect Selection and Compute Cost
链接: https://arxiv.org/abs/2609.18126
作者: Mojtaba Abdolmaleki,Stefanus Jasin,Boyu Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic AI systems often approach the same task through multiple workflows that differ in reasoning strategy, verification structure, and compute cost. A natural deployment policy is to use the workflow with the highest average performance, but this can be suboptimal because different workflows may succeed on different instances. We study a portfolio-and-selector paradigm in which a firm runs multiple workflow executions and selects the final answer after observing their outputs. Additional executions may uncover correct answers that the best standalone workflow misses, but they consume compute and introduce plausible distractors that complicate final selection. We formulate this as a workflow portfolio problem in which the firm jointly chooses run size and allocation across workflow types. We summarize selector quality through an odds-lift index and derive sharp bounds on the value of workflow variety. For finite workflow pools, we develop exact formulations, linear programming relaxations, randomized rounding procedures, and computable performance certificates. For large implicit workflow classes, we derive a finite-dimensional dual and an ellipsoid method using a pricing oracle to identify workflows with high weighted accuracy net of recurring compute cost. Under a weak condition, the method obtains a near-optimal solution to the relaxation with polynomially many oracle calls. We evaluate the framework on three datasets: ABCD, Schema-Guided Dialogue, and HotpotQA. Relative to the best standalone workflow, portfolio optimization improves held-out selector accuracy by 3.1, 7.5, and 0.9 percentage points, respectively. Dual-guided workflow generation adds 3.5 points on ABCD and 24.1 on HotpotQA, with no additional gain on Schema-Guided Dialogue.
[AI-68] AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines
链接: https://arxiv.org/abs/2609.18123
作者: Li Chen
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 7 tables, 6 figures. Code and measurement corpus: this https URL (tag v1.0.0)
Abstract:Large language model agents tune GPU kernels and serving engines through a closed loop of propose, measure, and keep, but the measurements behind this loop are not trustworthy. We characterize four failure modes from a four-day pilot corpus of 619 model calls: strawman baselines manufacture speedups, absolute times do not transfer across machines, saturated tasks nullify comparisons, and infrastructure defects impersonate science. We present AutoTuneBench, a benchmark and measurement protocol that makes trust architectural. The protocol is frozen as code with test-enforced provenance; a database-level validator rejects out-of-protocol results; anti-cheat checks run outside the agent’s modification surface; comparisons follow pre-registered readouts; and measurements anchor to externally published results, grounded in paired-seed statistics with a 5% cross-run coefficient-of-variation cap. Honest measurement rewrites the headlines: our best kernel reads 10.6x against a naive baseline but 2.03x against the honest one; one configuration delivers 1.174x on one machine and 1.0049x on another; a pre-registered on/off comparison nulls at a shared wall (2.4840 vs 2.4957,ms); and the KernelBench Level-1 suite admits 51% of tasks with median speedup 1.0001x over PyTorch eager. The protocol, the two-engine corpus (vLLM and SGLang), and its audit trail are released as open artifacts.
[AI-69] PentestChain: A Cost-Aware MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLM s
链接: https://arxiv.org/abs/2609.18120
作者: Rushabh Vipulkumar Patel,Dipo Dunsin,Mohammed Almaiah,Mohamed Chahine Ghanem
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注: 12
Abstract:AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous, automated testing unaffordable for the smaller organisations that need it most. This paper presents PentestChain, a ten-phase automated penetration testing framework that couples a curated, deterministic exploit map with a cost-aware AI cascade-a local Ollama model (qwen2.5-7b) first, then free-tier OpenRouter and Cerebras, with a rule-based fallback that always produces output-and exposes the full pipeline through a Model Context Protocol (MCP) server with eleven tools. We make three contributions. First, we treat US-dollar cost per engagement as a measured, first-class evaluation metric and show that a 7B-parameter local model, kept off the critical path by a deterministic backbone, sustains end-to-end operation at zero measured paid-API cost. Second, we analyse the attack surface that an MCP-exposed offensive engine introduces, grounding a four-position threat model in the 2025 MCP incident record (the CVE-2025-6514 remote-code-execution flaw in mcp-remote, the postmark-mcp supply-chain backdoor, and the tool-poisoning-rug-pull-line-jumping class), and contribute four mitigations. Third, we specify a reproducible, containerised evalua-tion protocol aligned with the standardised testbeds now expected at top-tier venues-AutoPenBench, a Cybench subset, and the PentestGPT 182-sub-task benchmark-with multi-trial statistics (more than 10 trials per configuration, pass-at-k, non-parametric significance tests and effect sizes) and direct, same testbed reproduction of the PentestGPT and PentestAgent baselines rather than citation of their published numbers. On the legacy targets measured to date, the framework detected 26 services, enriched 34 CVEs, produced
[AI-70] When Is Graph Structure Worth Its Cost? The Case for Structure Pricing in Retrieval-Augmented Generation
链接: https://arxiv.org/abs/2609.18099
作者: Yuzhong Zhang,Haoyang Ma,Chao Peng,Lionel Briand,Boxi Yu,Jialun Cao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Graph-based retrieval-augmented generation (RAG) can help answer questions that require information from many documents. However, building a graph often requires many language-model calls during ingestion. It is therefore important to ask whether its quality gains justify the additional cost. We present EffiRAG, a graph-based RAG system designed to reduce this cost. It uses the graph to locate relevant passages and generates answers from the original text. This design preserves source information while keeping graph construction and query processing lightweight. We evaluate EffiRAG on UltraDomain, which contains 120 open-ended questions from four domains. Compared with LightRAG-hybrid, EffiRAG produces the preferred answer on 93 questions. LightRAG is preferred on 7, and the remaining 20 are splits. EffiRAG also reduces total system cost by 57 percent, from USD 0.952 to USD 0.408. The cost includes language-model calls during ingestion and querying. The advantage remains as the corpus grows. At 10 and 20 documents per domain, EffiRAG uses a lightweight, non-LLM filter to skip low-salience chunks. It remains preferred over LightRAG-hybrid. It costs 4.2 times and 4.5 times less, respectively. The comparisons identify different quality-cost trade-offs. Graph-based RAG systems should therefore be evaluated by both answer quality and cost. The results favor graph structure that locates and preserves source evidence. Subjects: Artificial Intelligence (cs.AI) ACMclasses: I.2.7; H.3.3 Cite as: arXiv:2609.18099 [cs.AI] (or arXiv:2609.18099v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.18099 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Boxi Yu [view email] [v1] Wed, 16 Sep 2026 04:08:15 UTC (136 KB) Full-text links: Access Paper: View a PDF of the paper titled When Is Graph Structure Worth Its Cost? The Case for Structure Pricing in Retrieval-Augmented Generation, by Yuzhong Zhang and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-09 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
[AI-71] Decodability is Not Causality: Dissociating Probe Readouts from Behavioral Drivers via SAE Decomposition
链接: https://arxiv.org/abs/2609.18080
作者: Devesh Tiwari,Camille Davis,Shivank Sinha,Talia Weaver,Aditya Shah,Maheep Chaudhary
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Linear probes can decode safety-relevant concepts such as truthfulness from language-model activations, but probe accuracy may show only decodability, not that the features the probe weights causally drive model behavior. We demonstrate that this gap cannot be closed from the geometry of probe weights alone: the features geometrically aligned with probe direction need not be the ones the model uses, so causal relevance requires intervention. We introduce a feature-level diagnostic that decomposes a deployed True/False probe into sparse-autoencoder (SAE) features, ranks those features by both probe alignment and by gradient sensitivity of the model’s behavior, and ablates the resulting shared, probe-only, and random feature sets under a coherence gate. On the truth probe of Buerger et al. (2024) (TTPD), applied in the instructed truth/deception setting of Long et al. (2025) for Gemma2-9B-Instruct, the two rankings overlap only weakly (about 12%, Spearman rho = 0.10), and ablation dissociates them sharply: features the probe shares with the model flip the output far more (up to 27%) than equally sized probe-only (6%) or random (1%) features at full coherence, while probe-only features instead perturb the probe’s own readout. The dissociation holds across five seeds and a held-out split, and an activation-aware selection of features flips behavior nearly three times as often as the probe’s geometric top features (17.6% vs. 6.1%). In this setting, therefore, the geometric projection of a probe’s weight vector alone does not identify the features the model causally uses; however, combining probe information with feature activation statistics recovers substantially more behaviorally causal features, and coherence-gated SAE intervention is needed to separate them from probe readouts.
[AI-72] aching AI Robotics Community: A Hubs-Based K-12 Education Framework for Reaching Rural Schools
链接: https://arxiv.org/abs/2609.18072
作者: Maxwell J. Jacobson,Gustavo Rodriguez-Rivera,Petros Drineas,Yexiang Xue
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:K-12 robotics and AI education remains difficult to scale, especially in rural regions lacking sustained technical mentorship. Programs like FIRST provide competition pathways and instructional opportunities, but they do not eliminate the need for local programming and robotics expertise. We introduce AI, Robotics, Community (ARC), a hubs-based framework where colleges train undergraduate mentors and host workshops for nearby K-12 teams. Mature school programs can become secondary hubs that support additional schools, creating a self-reinforcing education loop where mentorship reach propagates geographically and can even grow super-linearly. We first evaluate ARC through a trial deployment at one university. The trial created three rural robotics teams. On five-point Likert surveys, mean increases in K-12 programming knowledge, resource access, and practice opportunities were 2.00, 2.25, and 1.25 points. Likewise, undergraduate confidence teaching technical concepts, adapting explanations, managing groups, and finding mentoring enjoyable and meaningful increased by 1.29, 1.14, 1.00, and 1.14 points. Additionally, we create a spatial Markov model of ARC’s growth and simulate it using the state of Indiana as a testbed. Under moderate conditions, we find that ARC reaches 74% of Indiana’s 1,925 public K-12 schools and produces 992 robotics programs after 40 years, compared with 161 projected under natural growth alone. Together, these results show ARC can create and support rural robotics programs, train undergraduate AI and robotics mentors, and potentially scale mentorship across a region.
[AI-73] he Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction
链接: https://arxiv.org/abs/2609.18063
作者: Yu Lin,Yiming Wang,Runyuan Cai,Hanze Liu,Xiaodong Zeng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Mixture-of-experts (MoE) inference on consumer hardware is bounded by weight memory: a 35B-class model is 19.5GB at 4-bit, and sparsity shrinks the compute per token, not the bytes that must be held. Naive offloading to SSD does not help on its own, because layer N+1’s experts must be chosen before layer N’s output exists, so the reads cannot start early enough to hide behind compute. We present Edge0, a streaming MoE inference engine that closes the gap with a prerouter: a per-layer head predicts the next layer’s routing one token ahead, and the prediction is consumed as the routing itself, so the staged expert set equals the routed set and nothing is dropped. An unmerged recovery LoRA, trained on the student path, pays back the quality lost to int4 quantization and routing replacement. On a single 24GB machine, Edge0 serves a 35B MoE at 20tok/s inside 3GiB of peak active memory, within a few points of its fp16 teacher on average across five public benchmarks. An 8B tier runs on the same framework, and the framework, checkpoints, and adapters are open source. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.18063 [cs.AI] (or arXiv:2609.18063v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.18063 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-74] Anchoring What Matters: A Dual-Level Learning Framework for Visually-Grounded Multimodal Reasoning
链接: https://arxiv.org/abs/2609.18057
作者: Xinxin Song,Siyuan Li,Tingxiong Xiao,Jinli Suo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) has significantly improved the reasoning capabilities of large vision-language models (LVLMs). However, standard on-policy RLVR algorithms face a critical optimization bottleneck in preserving and reinforcing visually grounded reasoning behaviors: valuable visually-grounded reasoning trajectories are discarded after a single update, while uniform token advantage allocation prevents the model from reinforcing critical perception or reasoning steps. To bridge this gap, we propose PIVOT, a dual-level learning framework that anchors policy optimization around informative visual reasoning signals. Specifically, PIVOT introduces a self-calibrated experience replay mechanism, which selectively collects and replays visually-grounded historical experiences as stable reference anchors for policy optimization. Building upon this, we further design a vision-guided advantage allocation mechanism to allocate additional vision-aware advantages to tokens based on their local visual support and impact on downstream reasoning. Extensive experiments across diverse benchmarks demonstrate that PIVOT achieves highly competitive performance in enhancing the multimodal reasoning capabilities of LVLMs.
[AI-75] An Empirical Evaluation of Cost-Efficient Large Language Models on Algorithmic Programming Tasks
链接: https://arxiv.org/abs/2609.18052
作者: Chandimal Adikari,Nandika Herath
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 7 pages, 7 figures. Accepted for publication in the IEEE Proceedings of the 2026 International Conference on Advanced Computing Technologies (ICACT)
Abstract:This study empirically evaluates whether cost-efficient Large Language Models (LLMs) can be trusted to generate enterprise code to a written specification. Three models (Gemini Flash 3, GPT-5.4 mini and Claude Haiku 4.5) were asked to solve 992 algorithmic problems as Java Spring Boot service methods conforming to a mandated signature and data-transfer-object specification, crossing four model and agentic coding tool combinations with two prompt variants to yield eight configurations, with iteration forbidden and hardcoded answers explicitly prohibited. Eight problem statements were withheld to probe how models respond to missing input. The 7,593 resulting methods were classified by an eight-class outcome taxonomy describing what each does about producing an answer, then deployed and executed, giving 7,936 measured requests joined to that classification. Structural conformance approached ceiling, yet 38.4% of methods do not compute the value they returned and only 12.9% of returned answers were correct. Conditioning on outcome class shows that response reliability and correctness are inversely related, whereas genuinely computing methods answered least often and were correct 19.3%. Limitations include single generation runs per configuration, partial harness coverage, single-pass timing, syntactic classification, and probable corpus contamination.
[AI-76] Missing Bridges: Composition-Aware Active Imitation Learning
链接: https://arxiv.org/abs/2609.18004
作者: Maxwell J. Jacobson,Ahmed H Qureshi,Yexiang Xue
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Active imitation learning reduces expert effort by allowing a learner to request the demonstrations it needs. Existing methods typically select these requests for their expected information gain about the expert policy. In structured multi-task domains, however, the number of start-goal tasks may grow combinatorially despite their solutions sharing reusable behavior. This makes composable behaviors especially valuable, since a single demonstration may help solve many tasks at once. Prior methods do not explicitly account for this value when selecting which demonstration to request. We introduce Adaptive Agents via Latent Topologies (AALT), which requests demonstrations that maximize expected gains in start-goal connectivity. We further show that this objective is formally tied to information gain about task reachability. AALT organizes existing demonstrations into a topology of latent hub states connected by learned behaviors, identifies high-value bridge demonstrations that are likely to enable many tasks at once, and grounds each to an expert query. At inference, it plans through the resulting topology and conditions a diffusion policy on each successive hub transition. In a simulated UR5e robot ordered-retrieval domain with 72 tasks, AALT improved from 42/72 to 72/72 (100%) successful tasks consistently using only 3 demonstrations totaling 5 transitions beyond the initial dataset. After 20 demonstrations, the strongest baseline averaged 88.6% success using 98 transitions.
[AI-77] he Attention Within: Consensus Dynamics in Selective State Space Models
链接: https://arxiv.org/abs/2609.17997
作者: João Pedro Silvestre,Álvaro Rodríguez Abella,Paulo Tabuada
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Dynamical Systems (math.DS); Optimization and Control (math.OC)
备注:
Abstract:Selective state space models (SSMs) have recently emerged as a compelling alternative to transformers, combining competitive performance with substantially improved inference efficiency. At each SSM layer, a sequence of hidden states are propagated by a recurrence, mixing information of different tokens. Despite using a different mechanism, this mixing plays a role analogous to attention in transformers. In fact, recent works have shown that the two architectures may be closer than they first appear, as this recurrence admits a formulation akin to linear attention. In transformers, attention is known to drive the tokens to cluster, i.e., to reach consensus, collapsing in the limit to a single direction. Thus, we ask: does the recurrence at the core of SSMs drive the tokens to consensus, as attention does in transformers? To answer this question, we take a dynamical systems perspective on SSMs, modeling the evolution of tokens across layers as an ordinary differential equation. By exploiting input-to-state stability arguments, we establish local exponential stability of the consensus equilibria and characterize their domain of attraction for time-varying weight matrices, a setting not addressed by previous results. We thereby show that the resemblance between SSMs and transformers does run deeper: the recurrence at the core of SSMs aggregates tokens just as attention does. Numerical experiments on a pretrained Mamba-2 model point to the output gate as the component that regulates the extent of this consensus, preventing the tokens from reaching it in full. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Dynamical Systems (math.DS); Optimization and Control (math.OC) Cite as: arXiv:2609.17997 [cs.LG] (or arXiv:2609.17997v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.17997 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-78] Multimodal Conditioning of Fine-Tuned Stable Diffusion XL for Controllable and Culturally Faithful Ulos Motif Generation
链接: https://arxiv.org/abs/2609.17987
作者: Humasak Simanjuntak,Tamara Yunika Sianipar,Bronson T.M Siallagan,Difya Laurensya Ambarita,Arlinta Barus
类目: Artificial Intelligence (cs.AI)
备注: C. Anutariya, M.M. Bonsangue, M.N. Mahrin (eds.) “Proceedings of the 4th International Conference on Data Science and Artificial Intelligence (DSAI 2026)”, Kuala Lumpur, Malaysia, November 12-13, 2026, in volume 3251 of Communications in Computer and Information Science, Springer, November 2026
Abstract:The traditional Batak Ulos weaving industry faces growing challenges in producing diverse, innovative motifs due to limitations in conventional, manually driven design methods. This study proposes a multimodal generative framework integrating a fine-tuned Latent Diffusion Model (Stable Diffusion XL v1.0 via LoRA) with a Multimodal Large Language Model (LLaMA 1.5-7B) to enable controllable, culturally faithful Ulos motif generation. Four complementary conditioning mechanisms: text, image, representation, and semantic map (via ControlNet) jointly guide the generation process, each governing a distinct aspect from semantic intent to spatial layout. A five level ablation study across three scenarios (shape transformation, colour variation, and high-complexity input) shows that conditioning effectiveness is not proportional to the number of mechanisms combined: Text + Image + Semantic Map achieved the best FID (270) and CLIP Score (0.65 - 0.70) but the weakest SSIM (0.65), while Text + Image + Representation offered the best overall balance, with stable SSIM (0.84) and competitive FID (280). Combining all four mechanisms yielded the weakest FID (330), indicating conflicting optimization signals. Qualitative evaluation by nine weavers and thirty public participants confirmed statistically significant positive acceptance (Wilcoxon, p=0.007 and p0.001, respectively). A web-based prototype supporting text-to-image and image-to-image generation was also developed, offering a practical digital design tool for cultural heritage preservation.
[AI-79] RideWay: Benchmarking Efficient Task Completion for Tool-Using Language Agents
链接: https://arxiv.org/abs/2609.17985
作者: Qingnuan Han,Boli Fang,Mingzhi Hou,Claire Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI agents are usually evaluated by whether they complete a task. In interactive service settings, a successful agent can still frustrate users by asking repeated questions, performing redundant searches, or making avoidable revisions. We introduce RideWay, an efficiency-centered benchmark for ridehailing agents in a stateful tool-calling environment, together with Efficiency Utility, a success-gated metric that discounts successful trajectories for excess tool calls and user-facing turns relative to task-specific reference effort. Human paired preferences calibrate the relative penalties, reflecting an aggregate service-workflow trade-off: extra dialogue often creates visible friction, whereas extra tool use can sometimes verify constraints or preserve user intent. Across 58 tasks and 24 models, the fitted penalty for excess turns is about twice that for excess tool calls. On task-disjoint held-out preferences, Efficiency Utility achieves 78.7% accuracy overall: 90.6% when trajectories differ in turns, but chance-level accuracy when they differ solely in tool calls - the axis on which human annotators agree least. RideWay therefore makes interaction efficiency measurable alongside task success, while exposing the boundary of count-based tool-use evaluation.
[AI-80] uiML: Machine Learning for AI Agents
链接: https://arxiv.org/abs/2609.17984
作者: Nilesh Verma,Nick Lim,Albert Bifet,Bernhard Pfahringer
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages, 3 figures. Code and documentation at this https URL
Abstract:Machine-learning libraries such as Weka and scikit-learn were designed for human programmers. Language-model agents now use these same libraries by recalling APIs from memory and writing code, an approach that hides what a library offers, delays errors until runtime, and loses experimental state between turns. We present TuiML, a self-contained machine-learning library built for AI agents, with native algorithms across supervised, unsupervised, time-series, data handling, tuning, and evaluation tasks. Every component describes itself through machine-readable metadata and parameter schemas, so an agent can search the library, inspect components, compose validated workflows, and register new ones that become discoverable in turn. Every call is validated, seeded, and traced, and sessions export as runnable notebooks, making experiments reproducible by construction. One specification layer drives the Model Context Protocol (MCP), agent-framework adapters, a Python API, a CLI, and local model serving, while data and models never leave the machine. Benchmarks show TuiML remains predictively competitive with scikit-learn and Weka. While looking like a conventional library to a human user, TuiML is designed for agents first, allowing them to read, extend, and operate machine learning autonomously. TuiML is open source, with documentation at this https URL.
[AI-81] Contiguity Not Importance: Budgeted Repair of Stale KV Caches After Document Edits
链接: https://arxiv.org/abs/2609.17983
作者: Mingyang Mao,Wyatt Mackey,Xiaomin Lin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:KV-cache reuse can reduce inference cost in retrieval-augmented generation and agentic systems, but cached contexts may become stale when retrieved knowledge, working memory, or user state is edited. Under causal self-attention, even a local edit can affect downstream KV states. A full re-prefill reliably restores consistency but is costly, whereas refreshing only the edited span can leave downstream dependencies stale. We formulate in-place repair as budgeted recomputation and compare training-free position-selection policies on a factual RAG benchmark with matched direct and derived edits. Across three model families, all policies repair direct cases, but derived cases clearly separate them. At the primary budget, a contiguous edit-local window recovers at least 0.94 of the post-edit answer margin and substantially outperforms attention-based, KV-deviation, and structural selectors. Mechanistic analysis shows that position sets effective under clean-state transplantation can fail under actual recomputation because scattered positions inherit surrounding staleness. The edit-local advantage also depends on adjacency and largely disappears when the answer-bearing text moves downstream. Because answer-relevant edits almost always corrupt model behavior, failure severity is difficult to predict, and repair is 13-21 times faster than full re-prefill, our results support unconditional edit-local repair when the dependent text remains adjacent to the edit.
[AI-82] When to Call an LLM : A Confidence-Gated Hybrid for Cost-Effective Emotion Recognition in Conversational AI
链接: https://arxiv.org/abs/2609.17977
作者: Sai Babu Udayagiri,Arjun Chouhan,Ravisekhar Kanagala,Trishala Pavagada
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Emotion recognition in conversation (ERC) is a production capability behind agent-assist prompts, escalation routing, and post-call analytics in contact-center-as-a-service (CCaaS) platforms, where cost and latency constraints matter as much as accuracy. We report a systems-level comparison of three deployment options for dialogue-contextual ERC: a low-cost stacked ensemble (sentence embeddings, windowed context, RandomForest/XGBoost/logistic-regression stacking), off-the-shelf LLM prompting (GPT-4o-mini; zero-shot, few-shot, chain-of-thought), and a confidence-gated hybrid that escalates only the ensemble’s least-confident predictions to the LLM - modeled on IVA-to-human-agent escalation policies used in production contact centers. On IEMOCAP, the ensemble significantly outperforms every LLM configuration (0.595 vs. 0.460-0.536 weighted F1, p 0.0001) at a fraction of the cost and sub-10ms latency; on MELD and CMU-MOSI the ranking reverses, showing neither pure system is a safe default. The confidence-gated hybrid resolves this by Pareto-dominating both pure systems on all three datasets (0.620, 0.643, 0.824 weighted F1) while routing the majority of traffic through the near-zero-cost ensemble, translating to roughly 10-85 per million utterances versus 99-170 for an LLM-only pipeline. The escalation policy is not an opaque cost/accuracy dial: escalated turns disproportionately follow an emotion or sentiment shift, giving operators an interpretable, auditable routing signal, and the ensemble’s confidence is well-calibrated and safely under- rather than over-confident. The pattern holds across three datasets and two LLM providers. Confidence-gated cascading is established in general ML systems; our contribution is showing it transfers cleanly to dialogue-contextual ERC, yielding a concrete deployment recipe for CCaaS and conversational-AI platforms deciding how to allocate LLM spend.
[AI-83] Memory Has Geometry: Non-Uniform Geometric Memory for Long-Horizon Personalized AI ICDM2026
链接: https://arxiv.org/abs/2609.17969
作者: Jiahong Liu,Wenhao Yu,Zexuan Qiu,Menglin Yang,Irwin King
类目: Artificial Intelligence (cs.AI)
备注: Accepted to the IEEE ICDM 2026 (BlueSky). 6 pages, 2 figures, and 2 tables
Abstract:Long-term memory is becoming a core substrate for personalized AI, yet most systems still represent personalization as discrete records in a largely static latent space, accessed under one global similarity notion. For data mining, this creates a mismatch: the evidence is a temporal event stream, while the dominant abstraction is a searchable record set. We argue that long-horizon personalization should instead model memory as a user-specific dynamical state space with locally heterogeneous geometry. Geometry here is a computational language, not a literal claim about cognition: it captures stable versus volatile regions, variable-rate drift, heterogeneous neighborhoods, and uncertainty about current user state. Profiles and isolated events remain useful as points, but interaction, feedback, and elapsed time induce trajectories. Memory access then becomes trajectory-conditioned reconstruction of the relevant user state, not only nearest-neighbor lookup.
[AI-84] Collaborative Memory for Multi-Agent VLM Systems
链接: https://arxiv.org/abs/2609.17921
作者: Huixin Zhang,Shao-Jun Xia,Di Wang,Liangxi Liu,Hainan Xiong,Zihao Wang
类目: Artificial Intelligence (cs.AI)
备注: First Draft Version: 4 pages, 3 figures
Abstract:Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, different agents inspect different image regions, video frames, or visual representations, so collaboration extends beyond distributed reasoning to distributed perception. This makes shared visual context a central problem in VLM agent collaboration. In this paper, we frame memory hierarchy, cross-agent sharing, and consistency mechanisms around the need to reconcile interpretations and update dependent reasoning. Effective collaboration requires agents to build on contributions from other agents, recover missing visual context, and reconcile differing interpretations as new evidence emerges. Shared visual memory preserves not only images or textual summaries but also the dependencies among observations, agent interpretations, and subsequent reasoning. Together, these design considerations shape how information flows and evolves across VLM agents. The proposed framework provides a foundation for building reliable and resource-efficient agent teams.
[AI-85] Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds
链接: https://arxiv.org/abs/2609.17901
作者: Jan Tauberschmidt,Brian B. Moser,Stanislav Frolov,Andreas Dengel,Andrew B. Duncan,Sebastian J. Vollmer
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative modeling of time-dependent data is typically formulated on a discrete temporal grid, restricting supervision to the observed timestamps in the training data. We instead frame generation as continuous-time evolution on a learned data manifold. To this end, we leverage pretrained score-based models as geometric priors and learn a vector field that evolves data along score-induced interpolation paths. Because these dynamics follow transitions that respect the geometry learned by the score model, they support generation at arbitrary timestamps and temporal super-resolution beyond the discretization of the training data. Moreover, this geometric formulation allows us to train the vector field simulation-free through a regression objective. To improve long-horizon rollout robustness, we introduce an objective that promotes path-relative transverse exponential stability. While motivated by stability theory, it admits a practical interpretation as denoising score matching transverse to the interpolation path. Further, we extend the framework to a probabilistic setting that models a distribution over plausible future trajectories. We demonstrate the method on natural video and scientific dynamical data, including temporal super-resolution, PDE-based spatiotemporal fields, and molecular dynamics. Our results show that score-based priors provide a strong foundation for learning stochastic continuous-time generative dynamics.
[AI-86] OBC-Prune: Outcome-Based Calibration for Large Reasoning Model Pruning
链接: https://arxiv.org/abs/2609.17890
作者: Ha Lan Nguyen,Huy Hoang Tran,Trac-Duy Tran,Dung D. Le
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large reasoning models (LRMs) generate long chain-of-thought traces before answering, creating significant inference overhead. Pruning can reduce this cost, but its effectiveness depends on the calibration data used to estimate parameter importance. Recent work calibrates on the model’s own rollouts instead of generic dataset, but treats all reasoning tokens uniformly, regardless of whether they contribute to successful reasoning. As a result, pruning protects weights by statistical salience rather than by their contribution to correct reasoning, so weights behind erroneous computation survive as readily as those behind correct computation. These erroneous patterns then get carried into the pruned model, degrading reasoning quality, producing both lower accuracy and longer reasoning traces. We propose Outcome-Based Calibration for Large Reasoning Model Pruning (OBC-Prune) to close this gap. OBC first constructs difficulty-matched pairs of correct and incorrect rollouts from problems the model answers inconsistently. It then estimates the causal importance of each reasoning sentence through intervention-based analysis, quantifying how removing its influence affects subsequent predictions. These causal importance scores are converted into per-token weights that rescale the calibration activations used by one-shot pruning methods (SparseGPT, Wanda, ALPS), without modifying the underlying pruning algorithms. Experiments on DeepSeek-R1-Distill-Qwen 1.5B, 7B, and 14B models at 40% and 50% sparsity demonstrate consistent improvements over state-of-the-art calibration baselines across most model sizes and sparsity levels on MATH500, LiveCodeBench, and AIME 2025. These results indicate that preserving causally important reasoning circuits is a substantially more effective pruning objective than uniformly preserving observed activations.
[AI-87] Do Frontier Models Seek Safety Evidence Before Acting?
链接: https://arxiv.org/abs/2609.17865
作者: Omer Tafveez
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 8 figures, with supplementary appendices. Code and data available at the repository linked in the paper
Abstract:Frontier models are often evaluated on how they respond to safety information once it is already in context. We study an earlier decision point: whether models choose to acquire safety-relevant evidence before acting. We introduce SAFE, a controlled benchmark in which models make deployment decisions with optional evidence that varies in retrieval cost, probability, severity, and presentation. Across GPT-5.5, o3, Claude Opus 4.8, and Claude Sonnet 4.6, we find distinct evidence-acquisition policies: Opus inspects nearly by default, o3 is the most skip-heavy and threshold-sensitive, and GPT-5.5 and Sonnet occupy intermediate regimes. Inspection increases strongly with severity and decreases with retrieval cost, whereas probability has much weaker behavioral influence: increasing the stated likelihood of a problem from 10% to 70% changes inspection by at most 21 percentage points. Despite these differences, Stage 1 rationales are dominated by expected-value reasoning across models. A cost-obligation decomposition further shows that avoidance is driven primarily by retrieval friction and explicit threats to the deployment payoff rather than by the remediation duties created by knowing. Counterfactual interventions reveal a further mismatch between behavior and explanation: evidence framing can strongly change decisions near the inspection boundary while going largely unmentioned, whereas probability is frequently cited despite having little causal influence. These results suggest that deployment-time safety depends not only on how models respond to known risks, but also on whether they acquire the evidence needed to know that acting is safe.
[AI-88] he Inference Engineering Pareto Atlas: Which Optimizations Dominate the Cost Quality and Latency Frontier?
链接: https://arxiv.org/abs/2609.17863
作者: Srikanta Datta Tumkur,Jay Iyer,Mehar Simhadri,Sai Pavan Kumar,Sai Kapil Kumar,Ramesh Nampelly
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM inference optimizations report speedups on different models, GPUs, prompts, and quality metrics, making them hard to compare or combine. We build a cost, quality, and latency Pareto atlas to identify the best configurations for different deployment constraints. Since exhaustive testing is impractical, we measure 54 configurations of Qwen2.5-7B-Instruct running on vLLM 0.12 across L4, A100, and H100 GPUs and use these anchors to calibrate a simulator. It reproduces measurements at anchored batch sizes, with cross campaign drift below 1.5 percent. A separate quality evaluation tests FP16, AWQ 4bit, FP8 weights, and FP8 KV cache on 200 GSM8K questions with five examples per prompt. Sparse attention is evaluated only in simulation. On the calibrated grid, 18 of 36 configurations reach the Pareto frontier. Combined methods reach it more often than individual methods, with 9 of 15 combinations versus 9 of 21 single methods. Quality testing changes the winners. AWQ 4bit reduces per token latency to 0.34 times baseline on L4 but loses 5.9 percent of strict GSM8K accuracy, narrowly missing the 95 percent quality floor within sampling uncertainty. Flexible answer extraction matches FP16 accuracy, suggesting the loss comes from formatting rather than arithmetic. FP8 weights retain 99.4 percent of baseline accuracy at 0.61 to 0.65 times baseline latency across all three GPUs and appear in three of four regime winners. A naive FP8 KV cache maintains normal throughput but answers none of the 200 questions correctly, showing why speed alone is insufficient. Under two prompt designs, n gram speculative decoding measures at 0.90 to 0.98 times baseline and adds no benefit on this stack. The best choice depends on the constraint and GPU: H100 wins for tight latency, while A100 wins for throughput and low cost at 0.106 dollars per million tokens.
[AI-89] SNOMED CT Concept Recommendation from Masked Clinical Context
链接: https://arxiv.org/abs/2609.17855
作者: Ali Noori
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Standardizing clinical language to SNOMED CT supports interoperability, analytics, and reusable phenotyping, but concept recommendation remains difficult when relevant concepts are rare or absent from training data. We present a masked-concept recommendation benchmark using the SNOMED CT Entity Linking Challenge v1.2.1 data derived from MIMIC-IV-Note. The dataset contains 75,491 annotations across 272 discharge summaries, with 204 notes used for training and 68 for historical testing. For each unique note-concept pair, the target mention is masked from a local clinical context and the system ranks SNOMED CT concepts observed during training. We compare a popularity baseline, sparse TF-IDF concept prototypes, dense latent semantic analysis embeddings, sparse-dense fusion, retrieved-note evidence, and a retrieval-augmented hybrid. Sparse TF-IDF performs best, achieving Recall@1 of 14.81%, Recall@10 of 33.43%, MRR of 0.2114, and nDCG@10 of 0.2297. Retrieval augmentation does not improve this baseline, with Recall@10 of 31.99% and MRR of 0.1937. Performance is strongly affected by concept frequency: Recall@10 is 7.74% for concepts appearing in only one or two training notes versus 43.90% for concepts appearing in more than ten. In addition, 9.66% of test note-concept pairs contain concepts unseen during training. These findings show that local lexical context and terminology coverage are major determinants of recommendation quality in low-resource settings and provide a reproducible baseline for future ontology-grounded and biomedical-encoder retrieval systems.
[AI-90] Adaptive hybrid coupling with operator inference the overlapping Schwarz alternating method and reinforcement learning
链接: https://arxiv.org/abs/2609.17837
作者: Trishit Mondal,Irina Tezaur,Anthony Gruber
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Mathematical Physics (math-ph)
备注:
Abstract:Hybrid domain decomposition methods provide a flexible framework for coupling full order models (FOMs) and reduced order models (ROMs), but typically assume the model assigned to each subdomain is fixed throughout a simulation. This is limiting for transient problems in which localized features propagate through the domain and the regions requiring high-fidelity resolution change over time. We introduce a reinforcement learning (RL)-based approach for online adaptation of FOM-ROM models coupled via the overlapping Schwarz alternating method (O-SAM), an iterative domain decomposition method that solves subdomain-local problems while exchanging solution information through transmission boundary conditions on overlapping interfaces. Deep Q-networks (DQNs) are trained offline to select among subdomain-local FOMs and pre-trained Operator Inference (OpInf) ROMs using a reward balancing accuracy, cost, and model-switching frequency. Once trained, the policies are deployed predictively on problem instances not seen during training, without requiring a reference FOM solution. We demonstrate the approach on two examples: a 1D advection-diffusion problem with a moving front, and a 3D linear elastic wave propagation problem implemented in the this http URL solid mechanics code. For the advection-diffusion benchmark, the learned policy dynamically allocates high-fidelity resolution as the front propagates and outperforms static FOM/ROM assignments; letting the agent also adapt the domain decomposition provides no further benefit. For the elastic wave benchmark, learned policies for two and three subdomain decompositions track the propagating wave by assigning FOMs to subdomains containing the wave and ROMs elsewhere, as expected. Our results demonstrate the potential of RL to enable predictive online adaptation of model fidelity within Schwarz-based hybrid simulations.
[AI-91] Procedural Pretraining for Molecular Property Prediction
链接: https://arxiv.org/abs/2609.17831
作者: Moritz Friedemann,Zachary Shinnick,Philip Torr,Bruno Andreis
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 14 pages
Abstract:Molecular property prediction is often limited by the small size of labeled downstream datasets, motivating pretraining on large corpora of unlabeled molecules. In this work, we ask whether useful inductive biases can instead be learned from abstract, procedurally generated data before a model sees any molecular data. We introduce a three-stage training pipeline consisting of procedural pretraining, molecular pretraining on SMILES, and downstream fine-tuning, and evaluate several procedural tasks spanning sequence structure, cellular automata, and graph reasoning. We find that procedural pretraining can improve molecular property prediction even after subsequent molecular pretraining: on Lipophilicity, \textscReverse reduces test error by 4.8%. For context, the magnitude of this improvement is roughly 90% of the performance difference between our 250K-molecule baseline and the publicly released MoLFormer checkpoint pretrained on approximately 100M molecules. Our analysis shows that the benefit is strongest under downstream data scarcity, depends on the structure of the procedural data rather than only surface-level statistics, and does not increase monotonically with additional procedural training. Instead, transfer typically peaks at an intermediate procedural budget and deteriorates as the model approaches convergence on the procedural task. We further find that, for several tasks, much of the transferable information is localized in the attention layers, while feed-forward layers can contribute to over-specialization. These results show that procedural data can provide transferable structure for molecular learning and offer a complementary route to improving performance when labeled molecular data are limited.
[AI-92] Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control
链接: https://arxiv.org/abs/2609.17824
作者: Bikram Pandit,Mohitvishnu S. Gadde,Aayam Kumar Shrestha,Alan Fern
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Project website: this http URL
Abstract:We study cooperative multi-humanoid pickup and transport of objects with varying size, weight, and geometry, requiring robot teams of different sizes. Our approach uses decentralized object-centric control, where each humanoid is assigned a local attachment region on the shared object and learns to realize pickup and transport through gripperless bimanual pinching. This attachment-based interface provides a common control abstraction spanning single-robot pickup, cooperative multi-robot transport, and robot-to-robot handover, without per-task redesign. We find that policies trained only on single-robot pickup already transfer nontrivially to cooperative settings, suggesting that this abstraction captures much of the structure needed for coordination. At the same time, explicit multi-robot training further improves performance, showing that shared-object coupling introduces coordination dynamics that are beneficial to learn directly. We validate the approach in simulation across varying team sizes and object geometries, and demonstrate sim-to-real transfer on hardware, where the learned controllers enable real humanoids to perform cooperative manipulation tasks.
[AI-93] Reflections on Trusting Trust Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks
链接: https://arxiv.org/abs/2609.17817
作者: Franziska Roesner,Tadayoshi Kohno
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Thompson’s “Reflections on Trusting Trust” showed that a compiler can be poisoned to reinsert its own backdoor, so that even recompiling clean source reproduces the Trojan. Today, substantial coding work is done by AI coding agents – and increasingly, those agents generate new versions of themselves. We reconsider Thompson’s attack when the “compiler” is a self-modifying coding agent. Can an adversary supply poisoned benchmarks to the agent’s self-evaluation and self-improvement process to induce future versions of the agent to write vulnerable code on clean, held-out tasks? We instantiate this attack against three recently proposed self-modifying coding agents: the Darwin Gödel Machine (with our experimental modifications), the Self-Improving Coding Agent, and Hyperagents (both substantively unmodified). We demonstrate successful proofs-of-concept: for example, with Hyperagents powered by Sonnet 4.5, our poisoned benchmark leads the agent to self-evolve instructions that disable HTTPS certificate validation on neutral URL-fetching tasks. From our experiments, we distill properties of the vulnerability, benchmark, model, and agent scaffolding that are sufficient to enable a benchmark poisoning attack. Moreover, we show that contamination often persists even when a poisoned agent is subsequently evolved against clean benchmarks. We discuss defensive directions and argue that self-modifying coding agents must be designed to be more resilient to such attacks.
[AI-94] he Free Inference Dimension: Complexity Measure for Zero-Collision Navigation under Hypothesis Mixtures
链接: https://arxiv.org/abs/2609.17816
作者: Luiz Carlos Castro Guedes,Edward Hermann Haeusler
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Solomonoff induction frames prediction as a mixture over computable hypotheses, typically leading to identification of the true environment. In a finite meta-reinforcement learning setting with nested constraint families, in our previous work, we observe a different regime: a value-mixture (VM) agent achieves near-optimal, zero-collision navigation without identifying the true environment, a phenomenon we call Free Inference. This regime persists up to a sharp density threshold, beyond which performance degrades and posterior-mode selection (PMS) becomes preferable. We formalize this behavior via the Free Inference dimension dFI(S,N), a combinatorial measure of the environmental complexity a VM agent can handle while preserving trajectory coherence. We prove dFI is strictly smaller than the VC-dimension and relates to the Natarajan dimension up to a path-length factor, capturing the cost of non-decomposable loss. A PAC-style relaxation yields generalization bounds driven by dFI^(epsilon,delta). We also define a complementary PMS identification dimension and show that a hybrid strategy—averaging until the first collision, then switching to selection—is optimal, with links to Littlestone-type dimensions supported by grid-world experiments. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.17816 [cs.LG] (or arXiv:2609.17816v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.17816 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Proceedings of BRACIS 2026. LNCS/LNAI, Springer (2026)
[AI-95] Principled Koopman Representations with Kalman Inference for Efficient Time-Series Prediction
链接: https://arxiv.org/abs/2609.17815
作者: Ruiquan Li,Yuheng Bu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The Koopman operator has been widely used for time-series prediction in dynamical systems. However, prior work that learns latent ``Koopman spaces’’ using neural networks often did not construct a valid Koopman space for forecasting, as these representations may be mathematically inconsistent with the operator-theoretic formulation and fail to capture the intrinsic low-rank structure of system dynamics. To address this issue, we introduce K ^2 SVD, a method that explicitly learns the leading singular functions of the Koopman operator by optimizing a Hilbert-Schmidt objective. This yields a well-defined low-rank approximation of the Koopman operator with an interpretable linear combination, featuring a compact latent space with less than 10% of the dimensions used in previous work. In the learned Koopman space, K ^2 SVD further captures temporal evolution with a linear Gaussian state-space model and performs inference via Kalman filtering, mitigating noise accumulation during multi-step prediction. Empirical results show that K ^2 SVD outperforms state-of-the-art methods across multiple datasets, with significantly faster prediction speeds and lower computational cost than previous efficiency-focused models. This highlights the benefits of principled low-rank Koopman representations and opens up broader potential for applications.
[AI-96] A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Frag ility in LLM Math Reasoning
链接: https://arxiv.org/abs/2609.17804
作者: Zhongdi Qu,Carla P. Gomes
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it. We reconcile these observations with a mechanistic account. We show that the model’s internal computation decomposes into a four-stage sequential pipeline, Schema Abstraction, Operation Planning, Operand Binding, and Computation, each stage producing a distinct intermediate representation in an identifiable band of layers. Using the same scaffold to diagnose distractor-induced failure, we localize the corruption to a single stage, Operation Planning, implemented by a set of attention heads whose causal role we validate bidirectionally. In short, we provide a mechanistic interpretation of math word problem reasoning in LLMs, and their failure when distracted.
[AI-97] FairCompressAgent : An Agent ic Framework for Fairness-Aware Model Compression for FPGA Deployment
链接: https://arxiv.org/abs/2609.17786
作者: Yuanbo Guo,Yiyu Shi
类目: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注:
Abstract:Fairness-aware model compression requires selecting methods and configurations that balance accuracy, fairness, and deployment cost. These decisions become more difficult when compression methods are composed or the user’s requirements change. In this paper, we propose FairCompressAgent (FCA), an agentic framework that integrates fairness-aware pruning, incremental quantization, and sparse low-rank factorization through a common operator interface. A language-model planner uses model profiles and measured outcomes to select compression configurations, while an execution layer performs compression, fine-tuning, evaluation, and constraint-based selection. FCA also supports requirement updates and reports the remaining violation when a request cannot be satisfied. Experiments on Fitzpatrick-17k with VGG-11 compare four search methods over 40 measured configurations. Under the accuracy-constrained request, FCA selects a compressed model with 59.54% less inference tensor storage, while validation average precision increases from 0.5141 to 0.5233 and equalized opportunity (EOpp) decreases from 0.2251 to 0.2168. It reaches the same final selection as one-shot planning with 7.33 versus 12 candidate evaluations on average, under their respective stopping policies. Repeated fine-tuning, held-out testing, and online requirement updates characterize the stability and interactive use of this compression workflow. The results demonstrate how measured feedback and explicit constraints support the selection and interactive refinement of fairness-aware compression configurations.
[AI-98] AI and Human Approaches to Mathematical Problem Solving
链接: https://arxiv.org/abs/2609.17779
作者: Yang Ding
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:AI systems have begun to report solutions, disproofs, and substantive advances on long-standing mathematical problems, raising questions about whether they approach research in the same way as mathematicians. This study compares public AI research accounts with the human literature on 11 such problems. The human corpus contains 58 papers that directly addressed the same mathematical targets later reported by AI sources as resolved, disproved, or substantially advanced; 31 within-problem comparisons were constructed from these materials. Six validated text-based measures capture problem resolution, method articulation, uncertainty and boundary specification, successor-question generation, generality, and cross-disciplinary integration. AI accounts place greater emphasis on resolving the focal problem and connecting ideas across fields. Human papers devote significantly more attention to explaining methods, specifying assumptions and limitations, and identifying questions for subsequent research. No precise difference is detected in generality. The estimated directions remain unchanged when each mathematical problem is removed in turn. The findings reveal two distinct research profiles: AI accounts concentrate on closing and recombining problems, whereas mathematical papers more extensively document the procedures, limits, and research opportunities through which results become cumulative knowledge. Evaluating research AI therefore requires attention to the organization of inquiry, not only whether a target is solved.
[AI-99] SAGE: Governed Artifact Generation from Enterprise Guidelines
链接: https://arxiv.org/abs/2609.17775
作者: Mohammadreza Sediqin,Shivali Dalmia,Sumukha Thoppanahalli,Srinivasa Karthikeya Reddy Kovvuri,Abhishek Mukherji
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Enterprise guideline documents mix narrative text, complex tables, and embedded images, and converting them into structured work artifacts still takes two to three days of manual effort each. Current language and vision-language models extract from such documents but offer no governed workflow beyond extraction: no validation, no consistency checking, no traceable artifact generation. We introduce SAGE, a governed multi-stage LLM pipeline organized around a shared versioned rule store with stable identifiers, schema-validated inter-stage contracts, and end-to-end provenance tracking. Extracted rules undergo deterministic structural validation and LLM-based semantic scoring, then a consistency module that removes duplicates, flags contradictions, and surfaces specification gaps; only uncertain or flagged items reach reviewers, while high-confidence outputs are auto-approved. On 120 documents, SAGE cuts turnaround from days to 20-100 minutes, achieving a 96% document-level success rate with 3.2% hallucination, extracting 3,896 rules and producing 812 artifacts ready for human review; without governance, hallucination rises to 15.7%.
[AI-100] HINT-Plan: Human Intention-Aware Robot Task Planning in Context-Rich Environments using Vision Language Models
链接: https://arxiv.org/abs/2609.17771
作者: Yuchen Liu,Luigi Palmieri,Lujun Li,Radu State,Ilche Georgievski,Marco Aiello
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Approaches to incorporating human awareness into mobile robot decision-making mainly focus on collision avoidance in low-level motion planning, often overlooking the challenges posed by human presence and high-level behavior. To address this vacancy, we present HINT-Plan, a novel approach to integrate human intention prediction into robot task planning. HINT-Plan employs Vision Language Models (VLMs) to anticipate high-level human intentions from third-person image observations, convert them into goal states, and solve joint task-planning problems. To effectively enable scene awareness in context-rich environments, we use hierarchical Scene Graphs (SGs) as high-level representations of the environment, and translate environmental topology and actionable knowledge into formal planning language to ensure executable plans. Evaluated in a photorealistic simulation, HINT-Plan achieves an overall success rate of 69.71% in joint human-robot task planning, substantially outperforming the baselines by up to 35.29%, while also reducing functional conflicts. The results show the effectiveness of explicitly incorporating inferred human intentions into formal multi-agent task planning for proactive human-aware robot decision-making.
[AI-101] CALOS: Control-Affine Lyapunov On-manifold Safety Layer for Safe Deep Reinforcement Learning for Quadrotors
链接: https://arxiv.org/abs/2609.17758
作者: Fabrizio Cesareo,Sebastiano Mengozzi,Nicola Mimmo,Andrea Acquaviva
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep Reinforcement Learning has demonstrated remarkable capability in quadrotor control, yet learned policies offer no guarantee of respecting safety constraints during training or deployment. We present CALOS (Control-Affine Lyapunov On-manifold Safety), a runtime safety layer that enforces attitude constraints on a quadrotor without modifying the underlying learning algorithm. CALOS formulates four tilt-angle inequalities and a Lyapunov descent condition as a single quadratic program whose solution is the minimum-norm correction to the nominal torque output of the policy. The quadratic program is solved exactly via active-set enumeration over the three-dimensional torque space, with a computational cost low enough to enforce constraints in real time across thousands of parallel simulation environments, as required by modern massively parallel Deep Reinforcement Learning training. Evaluated on trajectory-tracking tasks in NVIDIA Isaac Lab, CALOS reduces lateral tracking error by 55-60% relative to an unconstrained Proximal Policy Optimization baseline while achieving zero attitude-constraint violations on the training trajectory. By restricting exploration to safe regions of the state space, the safety layer also accelerates training convergence and improves data efficiency without producing suboptimal policies.
[AI-102] Imitation Learning for Autonomous Driving in CARLA
链接: https://arxiv.org/abs/2609.17757
作者: Jordy Kieto
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 13 pages, 4 figures. Code, data samples and trained policy: this https URL
Abstract:Behavioral cloning trains a policy offline on expert demonstrations, but deployment is closed loop: each action affects the observations the policy receives next. We study how much closed-loop driving competence a compact multimodal policy can acquire from offline demonstrations in the CARLA simulator. The policy uses five-frame histories of RGB images, LiDAR, vehicle telemetry, and lane waypoints to predict throttle, brake, and steering at 20 Hz. Demonstrations were collected in three stages, ending with a systematic route-generation procedure that enumerates spawn points and feasible maneuvers and verifies completed autopilot routes. The released 1.36 million parameter policy was trained on 236,882 windows, representing about 3.3 hours of driving from 448 captures. The resulting policy drives autonomously for hours on training and held-out routes. In our runs, it did so without collisions and also transferred qualitatively to an unseen CARLA town with different road geometry. We also observed recovery from large trajectory deviations, although we do not claim systematic recovery without controlled evaluation. We report offline metrics and distinguish measured results from qualitative closed-loop observations. We release the code, trained checkpoint, ONNX model, data sample, and an evidence audit for the reported claims.
[AI-103] REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff
链接: https://arxiv.org/abs/2609.17745
作者: Riyaaz Shaik,Chandru Venkataraman
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 8 pages, 5 figures, 2 tables
Abstract:A central goal of autonomous reinforcement learning is continuous policy training without external resets. However, existing paradigms largely depend on underlying environmental reversibility, a property absent in real world manipulation, where events such as pushing objects off tables or spilling granular substances cannot be undone. We introduce REVERSAL-BENCH, a benchmark that controls reversibility via a continuous parameter \rho \in [0, 1] and provides a reset oracle, a ground-truth verification mechanism to test state recoverability across eight manipulation settings in five physics engines. Evaluating a broad spectrum of policy architectures, including standard actor-critic algorithms, safe RL, and specialized reset-free frameworks, reveals a sharp reversibility cliff: reset-free agents are consistently absorbed into irrecoverable states as \rho increases, whereas episodic agents maintain steady learning. We see this failure mode across autonomous reset-free baselines and constrained RL. Because reset-free agents lack external resets, any transition into an irrecoverable state results in permanent absorption, leaving the agent trapped where further learning halts. We show that this absorption phenomenon persists in full physics simulations under learned manipulation policies. By evaluating against geometrically identical reversible counterparts, we confirm that this breakdown is causally driven by irreversibility rather than obstacle complexity. We release the benchmark suite, a large multi-simulator dataset labeled with recoverability and a reset oracle. We also evaluate a safety shield that intervenes before irreversible failures occur, showing that while recoverability can be predicted accurately, active recovery primarily succeeds only when the agent can physically steer clear of the trap
[AI-104] A Systematic Evaluation of the COTQ Provincial Land Cover Product: Structural Consistency Spectral Separability and Relative Positioning Against ESA ESRI and Google Products
链接: https://arxiv.org/abs/2609.17731
作者: Étienne Clabaut,Samuel Foucher,Yacine Bouroubi
类目: Artificial Intelligence (cs.AI)
备注: 46 pages
Abstract:High-resolution land use and land cover (LULC) products derived from Sentinel-2 imagery are widely used for environmental monitoring and land management, yet their performance can vary across regions with complex ecological gradients and heterogeneous surface conditions. In Quebec, these limitations motivated the development of a provincial 10-m land-cover product, the COTQ, designed to support annual monitoring of land occupation and soil artificialisation. This study presents a systematic evaluation of the COTQ product relative to three global 10-m LULC datasets: ESA WorldCover, ESRI LandCover, and Google DynamicWorld. This paper does not introduce a new mapping methodology but focuses on analysing the behaviour and consistency of the COTQ using complementary evaluation approaches. All products are harmonized under a common legend and compared using structural indicators (object-size distributions, shape complexity, Adjusted Rand Index, and Intersection over Union), spectral separability metrics derived from Sentinel-2 reflectance data, and a targeted photo-interpretation of disagreement areas. The analysis is conducted over eight Sentinel-2 tiles selected to represent the main bioclimatic domains of Québec, from temperate and boreal forests to northern tundra environments. The results show that the COTQ exhibits structural and spectral characteristics most similar to ESA WorldCover among the reference global products, while revealing systematic differences linked to class definitions and thematic priorities, particularly for urban areas, wetlands, and rocky or cryptogamic surfaces. This multi-criteria evaluation provides an objective characterization of the COTQ product and clarifies its relative positioning with respect to existing global land-cover datasets for operational land monitoring in Québec.
[AI-105] GVD: Governed Versioning and Deduplication for Document Repositories
链接: https://arxiv.org/abs/2609.17696
作者: Mohammadreza Sediqin,Shivali Dalmia,Sumukha Thoppanahalli,Abhishek Mukherji
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Document repositories evolve continuously. Guidelines and policies are revised, superseded, and re-uploaded, so the same content recurs in different wording and newer versions refine or contradict earlier ones. These inconsistencies belong to the growing collection rather than to any single document, yet existing work treats versioning, duplicate detection, and contradiction detection as isolated pairwise tasks and stops once a pair is labeled. We present GVD (Governed Versioning and Deduplication), a framework that unifies cross-document version linking with rule-level conflict resolution under an auditable update policy. Incoming documents are assigned to version families through bidirectional rule alignment, and their rules are compared against the family memory to identify duplicates, contradictions, asymmetric refinements, and new knowledge, with Counterfactual Span Probing (CSP) resolving related pairs that inference misclassifies as neutral. Relation-specific policies suppress duplicates and escalate only consequential changes for review, retaining version lineage as an audit trail. The pipeline runs fully locally, with no large language model. On 120 enterprise documents processed as 140 ingestions across 59 version families, GVD reaches an F1 of 0.97 for version-family construction and 0.94 for rule-level consistency, with CSP raising rule consistency from 0.90 to 0.94.
[AI-106] Accelerating Diffusion Sampling via Speculative Draft Trees
链接: https://arxiv.org/abs/2609.17691
作者: Marcello Bullo,Yanxiao Liu,Öykü Sıla Güner,Arpan Mukherjee,Deniz Gündüz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Applications (stat.AP)
备注:
Abstract:Speculative sampling accelerates diffusion model generation by drafting inexpensive candidate states and correcting them under a coupling that preserves the target distribution exactly, reducing the number of expensive target evaluations. Existing diffusion samplers, notably those based on reflection maximal coupling, are topologically constrained: their lookahead drafts form a chain graph, a single linear sequence, which inherently limits the acceptance rate per target evaluation. We connect speculative sampling in diffusion models to relative entropy coding (REC). This perspective shows the lookahead need not be linear and motivates our central contribution, draft trees, which enrich the candidates considered per round and lower the target function evaluations. We further adopt greedy rejection sampling, an REC algorithm, as the draft-target coupling, improving acceptance while guaranteeing exact target samples. Experiments across diverse target and draft models demonstrate up to 8.3% acceleration over the reflection coupling baseline in practical settings.
[AI-107] Reflect Revise Reuse: Training-Free Skill Evolution for GUI Agents
链接: https://arxiv.org/abs/2609.17653
作者: Bofan Chen,Boxuan Zhang,Fei Tang,Zhengxi Lu,Yong Du,Tongbo Chen,Weiming Lu,Jun Xiao,Yueting Zhuang,Yongliang Shen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Project Page: this https URL Code: this https URL
Abstract:GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocated widgets routinely invalidate plans fixed before execution. Recent agent-skill frameworks encapsulate reusable procedural knowledge to mitigate this, yet existing skill designs are largely developed without targeting GUI execution dynamics and treat skills as static artifacts produced before deployment rather than living procedural knowledge that improves through it. We argue that what GUI agents need is not better static skills, but skills that can be revised from execution feedback at deployment time, without additional training. We propose \textbfEvoSkill-GUI, a training-free framework in which each skill is a structured multi-file package containing retrieval metadata, executable plans, backup localization, failure-recovery rules, accessibility utilities, and failure cases. EvoSkill-GUI operates through a \textbf\emphreflect-revise-reuse loop: the executor performs instant in-rollout revisions, an isolated critic diagnoses failed trajectories under strict information isolation, and the executor edits specific skill files through a restricted tool interface. Across MobileWorld, AndroidWorld, and OSWorld, three mainstream GUI benchmarks spanning mobile and desktop platforms, EvoSkill-GUI consistently improves multiple base models without any training, with maximum gains of +16.2% , +6.0% , and +10.5% respectively, and evolved skill libraries continue to benefit related tasks rather than being rebuilt from scratch. Our code is available at this https URL.
[AI-108] Lecture notes on Physics Informed Neural Networks Neural Operators and their applications
链接: https://arxiv.org/abs/2609.17638
作者: Alessandro Bombini
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computational Physics (physics.comp-ph)
备注: 264 pages; Lecture notes for the PhD course Physics Informed Neural Network held at the University of Bozen/Bolzano
Abstract:This is the set of lecture notes for the PhD course \hrefthis https URL\textitPhysics Informed Neural Network, held at the University of Bozen/Bolzano in the academic year 2025/2026. The goal of the course was to introduce the concept of Physics Informed Deep Neural Networks (PINN) and Neural Operators (NOs), discuss their implementation from scratch in PyTorch and using advanced ad-hoc developed open-source libraries such as NVIDia PhysicsNeMo to address real-world problems in various fields (engineering, physics, petroleum reservoir). We discuss recent topics such as Mixture-of-Models, Fourier Neural Operators, Physics-Informed Kolmogorov-Arnold Networks (PIKANs) and Fourier Neural Operators. Comments: 264 pages; Lecture notes for the PhD course Physics Informed Neural Network held at the University of Bozen/Bolzano Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computational Physics (physics.comp-ph) Cite as: arXiv:2609.17638 [cs.LG] (or arXiv:2609.17638v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.17638 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-109] What You Cant See Is Still What You Learn: A Preregistered Sixty-Society Confirmation That Evidence Masking Drives Compositional Generalization
链接: https://arxiv.org/abs/2609.17637
作者: Narcis Marincat
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 16 pages, 2 figures, 5 tables. Preregistered fresh-world confirmation of arXiv:2608.20054 ; related companion study: arXiv:2609.11365
Abstract:Restricting what a module can read may improve what a system learns to compute. We test this in a preregistered confirmation with sixty four-cell systems sharing a frozen language-model backbone and communicating through learned continuous packets. Five conditions vary evidence masking, ownership markers, and replacement of foreign evidence with neutral filler, across six initialization clusters, each with two data orders, on one fresh task world. With markers available in both regimes, masking improved accuracy on held-out two- and three-operation compositions by median paired differences of 0.846 and 0.859; all twelve pairs cleared the required margins, and the full preregistered behavioral criterion passed. The unmarked replication also passed. No globally visible system passed the marker-following check, so the effect of usable role information remains unresolved. The filler condition yielded seven full generalizers, but its decomposition criteria were inconclusive. Packet interventions in all eighteen audited masked systems followed the predicted intermediate-value changes on eligible cases; these finite, success-conditioned audits do not establish mediation. The results confirm a large advantage of the tested masking regime, while leaving its finer attribution and generality open. Protocols, results, and checkpoints are public.
[AI-110] Physics-Constrained Digital Twins for Sensor Integrity in Urban Pedestrian Flow: Detecting Stealthy False Data Injection with Conformal Guarantees
链接: https://arxiv.org/abs/2609.17635
作者: Oscar Mogollon Gutierrez,Fatemeh Ghasemi,Mohammadhossein Homaei,Andres Caro,Mar Avila
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 16 Pages, 4 Figures, 8 Tables
Abstract:City pedestrian counting systems now feed economic indicators, planning decisions and safety operations, yet the twins built on top of them treat the incoming stream as ground truth. We study what happens when it is not. We formalise stealthy false data injection for city-scale pedestrian sensing, where the map from latent flow to observation is far more rank deficient than in the power and water networks for which stealth has been characterised. Our twin estimates directed flows on the pedestrian street graph, assimilates counts through a learned graph-localised gain, and is trained against a flow conservation residual that couples metered and unmetered segments. Detection combines the innovation with that residual, and the alarm threshold is set by adaptive conformal calibration rather than by hand. To measure what the physics buys, we define the attack margin, the relative reduction in worst-case corruption of the estimated flow field, achieved against a white-box adversary that optimises directly through the twin. On six years of Melbourne data the margin reaches 0.54 against a single compromised device and falls to 0.19 when a third of the fleet is compromised, on a network where only 1.18 per cent of walkable segments are metered. Replacing the street graph by a distance graph collapses it to 0.09, which shows that the gain comes from the conservation law rather than from locality.
[AI-111] One Color Preprocessing Improves DSATUR
链接: https://arxiv.org/abs/2609.17633
作者: Adam Nouira,Lucas Isenmann
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The Graph Coloring Problem (GCP) is NP-hard and DSATUR stands as one of the fastest heuristics for it despite producing colorings that typically use more colors than state-of-the-art coloring algorithms. We propose SSLD (Semidefinite Spectral Learning with DSATUR), which improves DSATUR by preprocessing a first good color class before letting DSATUR complete coloring the rest of the given graph. We obtain this color class from a Semidefinite Programming (SDP), similar to an SDP used to compute the Lovász theta number. To the best of our knowledge, SSLD is the first approach to improve DSATUR by preprocessing through fixed color classes. We evaluate SSLD against DSATUR and against a naive 1-color-class preprocessing algorithm on DIMACS instances, random graphs (Erdős–Rényi, Watts-Strogatz, Barabási–Albert), Frequency Assignment and Job Shop Scheduling instances. SSLD matches or beats DSATUR in almost every case across over 1600 benchmark instances, and out performs the naive GISD baseline, allows us to confirm the value brought by the SDP-guided choice of the first color class. This quality comes at a runtime cost of roughly 195 times slower that DSATUR, but demonstrating that SDP-guided preprocessing of a first color class is a direction for future improvements.
[AI-112] Making AI-Assisted Claims Independently Challengeable: Publication Authority and a Protocol for Falsifiable Publication Records
链接: https://arxiv.org/abs/2609.17631
作者: Torsten Olivi Tiltack,Yifei Dong,Kun Yu,Xu Wang,Wei Liu,Jianlong Zhou,Ren Ping Liu,Fang Chen
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
备注: Main article: 24 pages, 4 figures; supplementary material: 13 pages. Preprint; not peer-reviewed. Yifei Dong is the corresponding author
Abstract:AI-assisted claims can appear authoritative when evidence, analysis, human authorization, presentation, and correction history refer to different states. Provenance, attestation, and transparency expose history but alone do not specify the publication transition examined here. We develop Publication Authority as an exact-state, non-transferable, single-use publication capability and instantiate it in PAC-2026 (Publication-Accountability Calculus), a machine-readable AIJIM Protocol candidate. We evaluate its fourth bounded semantic freeze (SF-4), a fixed-profile specification designed for replaceable bindings. Six obligations govern evidence, runs and artifacts, measurement disclosure, authorization, surface correspondence, and lifecycle continuity. Each yields a target-bound witness, localized counterexample, or localized unverifiability; none can compensate for another. Only a fresh, complete all-pass record derives the permit consumed by one atomic publication transition. We use identity vectors, adversarial cases, finite models, and historical implementations. Ten models explored 110,764 safe reachable states; 76 unsafe configurations produced the expected violation or observer countermodel. A reader surface passing its correspondence check cannot authorize publication unless the accepted record admits that surface. SF-4 separates evidence horizon from verification time and rejects an authentic but causally invalid authorization. A historical predecessor path reproduced 17 frozen authorization-successor outcomes. A later in-house, instance-blind test of known case classes matched all 183 scored expectations; same-host package execution reproduced its 240 archived observations. Results support internal coherence, bounded safety, fault sensitivity, and limited constructibility, but not factual truth, general refinement, blind interoperability, field efficacy, or standards status.
[AI-113] Decentralized Optimal Equilibrium Learning Over Dynamic Networks
链接: https://arxiv.org/abs/2609.17601
作者: Seref Taha Kiremitci,Muhammed O. Sayin
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Extended version of the paper: S. T. Kiremitci and M. O. Sayin, “Decentralized optimal equilibrium learning over dynamic networks”, to appear in the Proceedings of the IEEE Conference on Decision and Control, 2026
Abstract:This paper studies decentralized learning of socially optimal equilibria in finite normal-form games over dynamic communication networks. Each agent observes only its own realized payoffs, does not know the game a priori, and can communicate only with time-varying neighbors using low-bandwidth messages. We propose networked decentralized optimal equilibrium learning dynamics in which agents generate randomized semantic content/discontent signals from local payoff comparisons and exchange time-stamped time-stacked tables rather than raw actions, payoff information or local estimates/parameters. The method combines table fusion with temporal majority reconstruction to mitigate dynamic communication while preserving fully decentralized operation. We establish finite-time logarithmic regret guarantees, with an in-phase exploration perturbation, for optimal equilibrium selection under utilitarian and proportional-fair social welfare objectives. Simulation results further show that the proposed approach can effectively select socially desirable equilibria over dynamic communication networks.
[AI-114] Evolutionary Ensemble Search: Council-Guided Program Evolution with Persistent Memory
链接: https://arxiv.org/abs/2609.17590
作者: Juan P. Madrigal-Cianci,Eshan Chordia
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:Evolutionary Ensemble Search (EES) constructs machine-learning procedures through expert-guided program evolution. A role-specialized council turns task evidence and experimental results into structured search directions. An orchestrator allocates these directions to execution specialists and an evolutionary engine. The engine selects measured parents, diagnoses their errors, and produces descendants through code mutation, structured pipeline edits, and crossover. Each child must execute and acquire its own validation evidence. Population archives retain useful alternatives, while compatible predictions compete in a validation-gated ensemble stage. Search adapts through parent-relative operator credit, session memory, and problem-indexed lessons retrieved across runs. We specify these mechanisms, distinguish their execution profiles, and define the contracts required to compare candidates as their computations change. A public MLE-bench Lite development ledger records medal-threshold artifacts on 19 of 22 tasks (86.36%), with best outcomes of 11 gold, five silver, and three bronze. The procedures span text, images, tables, audio, scientific geometry, and deterministic transformations. The campaign includes grade feedback between runs, external-source routes, and mixed confirmation procedures; its aggregate is an achieved development result, not a blind autonomous-agent success rate. The report contributes a concrete architecture for cumulative executable search and a versioned account of its cross-modal development outcomes.
[AI-115] Where Grokking Happens: Distributed Utility and Fourier Recoding Without a Module Switch
链接: https://arxiv.org/abs/2609.17571
作者: Dekun Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages, 10 figures. Code and data: this https URL
Abstract:Where in a Transformer is the change from memorization to generalization functionally expressed? We introduce Transition Games–behavior-aligned exact activation games with paired non-generalizing controls–and find distributed utility gain with a prospective block-0 attention bias; selected degree-two modes account for 67–92% of its addition contrast across replacement games, and a disjoint exact path study confirms that block-1 MLP mediates more of their effect than all other tested downstream paths in 12/12 pairs. The sharper “MLP memorizes, attention generalizes” prediction instead reverses (-.331 bits/example at the memory anchor; 0/12 in the predicted direction), while routing onset, global rank collapse, and a prime-invariant architecture ridge also fail, identifying grokking here as spectral recoding of an existing distributed circuit rather than a module switch.
[AI-116] Independence-System Realisations in Single-Source Unsplittable Flow
链接: https://arxiv.org/abs/2609.17568
作者: Koyar Afrasyab
类目: Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI)
备注: GitHub repo: this https URL
Abstract:Additive-congestion constraints in single-source unsplittable flow can enforce stable-set structure. This note isolates and generalises that mechanism. We introduce a path-closed notion of realising an independence system by the zero-cost choices of primary terminals in a directed acyclic flow instance. The definition quantifies over every directed source-terminal path and therefore remains valid under prefix borrowing, suffix splicing, and hybrid this http URL main result extends the triangle mechanism: every finite loopless independence system has a polynomial-size realisation, measured in the incidence size of its minimal forbidden sets. Hence every finite simple graph, and more generally every hypergraph independence system without singleton forbidden hyperedges, is representable by an acyclic single-source gadget. We then specialise the construction to odd cycles. For C_2k+1, a uniform rational family produces a fractional cheap-selection vector that violates the odd-cycle inequality. A potential shift converts a signed connector separator into nonnegative arc costs and gives the exact cost-preserving additive-congestion threshold tau = 1 - bq. Within the symmetric family, the supremum threshold is (k+2)/(2(k+1)), which tends to 1/2. For C5, an exact certificate independently derives all source-terminal paths and enumerates all 3^10 = 59049 unsplittable routings using rational arithmetic.
[AI-117] BLADE: ReliaBle Dynamic Hardware-Aware SNN-ANN Boundary SeLection for Event-BAseD Object DEtection
链接: https://arxiv.org/abs/2609.17562
作者: Mahdi Taheri,Alwin Paul
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注:
Abstract:Hybrid Spiking Neural Network (SNN)-Artificial Neural Network (ANN) architectures combine the energy efficiency of SNNs with the superior detection accuracy of ANNs for event-based object detection. Existing hybrid SNN–ANN networks, however, employ static inference and select the SNN-ANN boundary primarily according to accuracy and energy consumption, without considering dynamic inference or reliability. This paper presents BLADE, the first reliability-aware boundary selection methodology for dynamic hybrid SNN-ANN networks with ANN early exit. The proposed framework jointly optimizes the SNN-ANN boundary and ANN early-exit configuration according to reliability, detection accuracy, execution time, and energy consumption, while incorporating reliability through hierarchical statistical fault injection during design-space exploration. Experimental evaluation on an event-based object detector achieves an mAP 0.5 of 0.691 while reducing the inference compute energy to 15.82~mJ when the ANN early exit fires. Reliability analysis identifies the most significant floating-point exponent bit as the dominant source of catastrophic failures, producing significant-or-worse accuracy degradation in 58.8% of its fault injections. Protecting this single bit with approximately 3% storage overhead eliminates catastrophic failures across the evaluated realistic technology fault rates. Furthermore, increasing the proportion of SNN computation improves fault tolerance, with the fully SNN configuration achieving a reliability retention of 0.965 under aggressive fault conditions. The results demonstrate that jointly optimizing reliability, accuracy, execution time, and energy consumption enables more dependable deployment of dynamic hybrid SNN–ANN systems for safety-critical edge AI applications.
[AI-118] WARD: Runtime Workload-Adaptive Vision TRansformer Framework for Dependable Edge AI
链接: https://arxiv.org/abs/2609.17556
作者: Mahdi Taheri,Pramit Kumar Bhaduri,Mohammad Masoumi,Ali Mahani
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Edge-deployed AI operate under dynamically changing power budgets, reliability requirements, and input distributions, requiring continuous adaptation. Such conditions arise in long-running edge AI applications, including autonomous systems, industrial monitoring, and satellite onboard intelligence. Existing fault-tolerant methods assume static operating conditions, whereas continual learning techniques neglect concurrent hardware faults during online adaptation. Moreover, the practical deployment of runtime-adaptive reliability frameworks on programmable AI accelerators remains largely unexplored. This paper presents WARD, a runtime-adaptive Vision Transformer framework that combines channel-wise subnetwork partitioning, reliability-aware continual learning, and dynamic operating-mode scheduling to jointly optimize performance, fault tolerance, and adaptation according to runtime conditions. Two physically isolated subnetworks execute under four operating modes (i.e. Full-Precision Mode, Low-Power Mode, High-Reliability Mode, and Adaptive Mode) that dynamically adjust computational cost and reliability while ensuring uninterrupted inference for real-time requirements. To validate the practical deployability of the proposed framework, WARD is implemented on a lightweight FPGA-based accelerator extended with runtime hardware support for mode scheduling and resource management. Experimental results demonstrate that the proposed split architecture achieves a network-level failure rate of only 1.79% under high Bit Error Rates. The hardware implementation incurs less than 5% area overhead and supports runtime mode transitions within few clock cycles, demonstrating that adaptive reliability management can be integrated into programmable edge AI accelerators with negligible implementation overhead. Subjects: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2609.17556 [cs.AR] (or arXiv:2609.17556v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2609.17556 Focus to learn more arXiv-issued DOI via DataCite
[AI-119] REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration
链接: https://arxiv.org/abs/2609.17555
作者: Mahdi Taheri,Samira Nazari,Mubassher Ansari,Ali Azarpeyvand,Mohsen Afsharchi,Maksim Jenihhin,Christian Herglotz
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Efficient deployment of Deep Neural Networks (DNNs) on edge accelerators requires aggressive model compression while maintaining reliability in fault-prone hardware environments. This paper presents a reliability-aware quantized weight packing methodology for systolic-array-based DNN accelerators. A sensitivity-driven mixed-precision quantization framework assigns layer-wise bit-widths according to accuracy impact while enforcing symmetric precision between weights and activations. A deterministic register-level packing strategy consolidates multiple heterogeneous operand pairs into fixed-width register words, enabling SIMD-within-a-register (SWAR) style parallel execution that reduces both memory footprint and execution cycles. To improve resilience against hardware faults, selective bit-level protection replicates the most significant bits (MSBs) of critical layers into unused register space, achieving TMR-style protection with minimal overhead. A systolic-array simulation framework is developed to evaluate the proposed packing and fault-tolerance mechanisms under realistic execution conditions. Simulations in AlexNet, VGG-11, and ResNet-18 demonstrate up to 62% memory reduction and up to 56% reduction in Multiply-Accumulate (MAC) operations, while significantly improving accuracy resilience under fault injection compared to baseline and fully protected models.
[AI-120] Securing quantum error correction against misleading advice from AI agents
链接: https://arxiv.org/abs/2609.19090
作者: A. Barış Özgüler
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Systems and Control (eess.SY)
备注: 74 pages, 32 figures (10-page main text, 62 pages of Supplemental Material, and 2 pages of references)
Abstract:Can an attacker turn influence over an artificial intelligence (AI) adviser into a harmful quantum error-correction update? We identify an ambiguity in passive syndrome records that obstructs recovery selection, then show how additional calibration measurements support certified recovery updates under uncertainty and drift. In an odd-distance square toric code with error-free preparation, syndrome measurements, and recovery operations, opposite coherent X rotations produce identical passive syndrome-history distributions. Yet a fixed phase correction can help at one sign and harm at the other. A terminal logical measurement on known encoded calibration states supplies the missing sign information. A separate evaluator accepts an update only when calibration uncertainty and a justified drift bound certify improvement over the current recovery, without assuming that the adviser recommends correctly. In simulated advice attacks, calibration-confidence checks reject harmful proposals while retaining beneficial updates under honest advice. We derive sufficient limits on calibration age that require improvement through deployment. In matched simulations, a validated channel-specific bound retains more beneficial updates than the general bound after accounting for evaluation time, while preventing the tested harmful activations under the stated drift assumption. A separate surface-code experiment includes stochastic circuit faults and noise changing during acquisition. Deterministic controllers achieve at least as many beneficial updates with the same observations. Violating the drift assumption permits harmful acceptance in the toric experiment. The results identify information required for recovery selection, establish conditional guarantees against harmful updates, and quantify the recovery improvements forgone through conservative acceptance.
[AI-121] GrainSpeech: Less Context More Detail for Compact Speech Synthesis
链接: https://arxiv.org/abs/2609.18856
作者: Zitao Liang,Chang Gao
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD); Signal Processing (eess.SP)
备注:
Abstract:Compact acoustic models face a challenging quality-capacity trade-off. We investigate two factors in this regime: encoder context and Mel-spectrogram supervision. A receptive-field-scaling study shows that expanding self-attention beyond 15 phonemes provides no consistent gains in pitch, energy, or duration prediction. Guided by this finding, we introduce a fixed-receptive-field convolutional encoder that reduces the respective prediction errors by 36.0%, 17.3%, and 3.4%. We further show that directly transferring image-domain gradient-variance supervision restores fine-scale variation but degrades predicted quality, motivating a Mel-specific formulation with axis-specific gradients, overlapping local statistics, and log-domain variance matching. GrainSpeech contains only 264.8K parameters and achieves 17.9x real-time Mel generation on a microcontroller (MCU), while attaining UTMOS scores comparable to substantially larger models with less than 1.5% of their parameters. Source code and demos are available at this https URL.
[AI-122] Hypothesis-Driven Autonomous Materials Synthesis with Multimodal LLM Agents
链接: https://arxiv.org/abs/2609.18598
作者: Izumi Takahara,Kazunori Nishio,Akira Aiba,Shigeru Kobayashi,Takao Nakajima,Taro Hitosugi,Teruyasu Mizoguchi
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Self-driving laboratories can explore synthesis conditions autonomously, but their decision-making layer is typically a black-box optimizer, and the output is a set of optimized samples, with the measurements reduced to predefined scalar objectives and the reasons behind success left unarticulated. Here we present SynAgent, a framework in which large language model agents operate an automated experimental system and maintain an explicit, revisable understanding of the synthesis process as the campaign’s primary output. Starting with no predefined analysis pipeline, SynAgent adaptively generates analysis skills for newly acquired data and evolves this understanding through multimodal reasoning over experimental data such as X-ray diffraction patterns and electron micrographs. The evolution is guided by a verify-falsify scheme, in which the agent deliberately challenges its own hypotheses by testing conditions predicted to fail as well as those predicted to succeed. In a single campaign of 18 autonomous experiments using LiCoO2 (001) thin-film deposition as a testbed, SynAgent synthesized highly crystalline films and evolved an understanding of how the substrate temperature governs crystallization, discovering an abrupt threshold and a narrow optimal growth window at 650-690 °C. These results extend autonomous experimentation beyond optimized samples to testable, human-readable understanding.
[AI-123] Multitask Reinforcement Learning for Assisting Choice Model Specification
链接: https://arxiv.org/abs/2609.18441
作者: Gabriel Nova,Stephane Hess,Sander Van Cranenburgh
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI)
备注:
Abstract:Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of-fit, parsimony, and behavioural plausibility. We present Delphos, a multitask reinforcement learning framework that learns transferable specification strategies across transport choice datasets. Delphos frames model specification as a sequential decision-making problem in which it applies a sequence of modelling actions and receives feedback from an estimation environment based on model performance and convergence. To transfer modelling decisions across datasets with different sets of variables, Delphos represents utility specifications as sets of modelling terms using a DeepSet-Q architecture, allowing a shared specification policy to learn across multiple datasets. Trained on nine transport choice datasets, Delphos consistently outperforms independently trained single-task agents, indicating that sharing modelling experience improves learning efficiency and helps identify promising sequences of modelling decisions with fewer unsuccessful estimation attempts. When applied without further training to the unseen Swissmetro and Decisions datasets, the same agent identifies competitive specifications in less than 20 minutes on a standard CPU. It achieves a higher log-likelihood per observation than the VNS metaheuristic on Swissmetro and performance comparable to a published MNL specification developed by expert modellers on Decisions. These findings show that accumulating and reusing modelling experience enables Delphos to function as an intelligent assistant for discrete choice model specification. It reduces manual trial-and-error while allowing modellers to retain control over model diagnosis, refinement, and final selection.
[AI-124] Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction
链接: https://arxiv.org/abs/2609.18368
作者: Cristian J. Vaca-Rubio,Konstantinos Vandikas,Aneta Vulgarakis Feljan
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Classical CSI feedback in FDD massive MIMO transmits a compressed reconstruction of the channel, optimizing fidelity to the original signal regardless of the downstream task. We propose a semantic communication perspective: instead of reconstructing the channel, the UE transmits a learned \emphsemantic embedding optimized end-to-end for beam selection at the gNB. Comparing reconstruction-oriented feedback (CsiNet) against task-aware semantic feedback across two input domains and three observation scenarios, we show that a semantic embedding of just d=8 real values from only 43 NR CSI-RS pilots in the angular-delay domain achieves the highest beam prediction accuracy, outperforming every method with access to the full 512-subcarrier channel. The key insight is that beam-relevant information is intrinsically low-dimensional: the semantic encoder learns to discard reconstruction-irrelevant structure and retain only a compact representation that is relevant to beam selection, realizing the core principle of semantic communication: transmit the intent, not the signal.
[AI-125] Remembering Solomon Marcus
链接: https://arxiv.org/abs/2609.18224
作者: Florin Nichita
类目: History and Overview (math.HO); Artificial Intelligence (cs.AI)
备注: 6 pages
Abstract:From the manifest of Andre Breton, through the transdisciplinary understanding, we arrive at a post-modern manifest. A talk by Laura De Marco (Harvard) will provide scientific background to approach an AMS poetry. The next section will be a qualitative analysis of some new operations on the real numbers. The conclusions will be given in the last section, and an appendix will recall some previous work with some new comments.
[AI-126] Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra
链接: https://arxiv.org/abs/2609.18025
作者: Ziyang Zhang,Qin Li,Vasyl B. Yurchyshyn,Kangwoo Yi,Haimin Wang,Wenda Cao,Bo Shen
类目: olar and Stellar Astrophysics (astro-ph.SR); Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Strong chromospheric absorption lines such as H \alpha 6562.8 A and Ca II 8542.1 A provide vital diagnostics of plasma dynamics and thermal structure in the solar chromosphere. Multilayer spectral inversion (MLSI) offers a physically interpretable framework for modeling these lines using a finite number of radiative-transfer layers, but conventional MLSI relies on pixel-by-pixel nonlinear least-squares fitting, making it computationally expensive for large imaging spectroscopic data sets. Here, we introduce a physics-informed neural-network (PINN) framework to accelerate MLSI while preserving its analytic radiative-transfer formulation. The network predicts MLSI parameters directly from observed line profiles and passes them through a differentiable MLSI forward model to synthesize spectra. Training follows a two-stage approach: an initial stage optimized solely via spectral reconstruction loss, followed by fine-tuning that combines spectral consistency with parameter-space supervision from conventional MLSI results on a single reference image. This strategy eliminates the need for large precomputed training sets while maintaining physical interpretability. Applied to Fast Imaging Solar Spectrograph (FISS) observations from the Goode Solar Telescope (GST) targeting both quiet-Sun and active-region regions, MLSI-PINN parameter maps reproduce the primary spatial structures of direct inversions, achieving an arithmetic mean pixel-wise Pearson correlation coefficient of 0.933 across all evaluated parameters. The reconstructed spectra closely match both observed profiles and conventional MLSI fits. Post-training, MLSI-PINN processes a raster in approximately 5-15 seconds compared to 3-5 minutes for conventional MLSI, delivering an inference speedup of about 12-60 times without substantial loss in reconstruction quality, enabling efficient MLSI analysis on large chromospheric data sets.
[AI-127] Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
链接: https://arxiv.org/abs/2609.17989
作者: Davood Wadi,Yu Ma
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) now serve as conversational shopping assistants on platforms that also sell advertising. These AI agents face a conflict of duty. They advise consumers who rely on their judgment, yet are deployed by platforms that benefit when sponsored listings are chosen. Sponsorship disclosures, designed to allow consumers to penalize paid placements, now reach the AI agent rather than the consumer, and the agent’s evaluation of them is hidden from the consumer. Drawing on the fiduciary concept of conflict of duty, we argue that an agent’s evaluation of a sponsored listing should not depend on which party deployed it. In controlled choice experiments, we manipulate assigned roles in the system prompt to name either a traveler or a booking platform as the agent’s principal. Platform delegation significantly attenuates the penalty that agents apply to sponsored listings and weakens the skepticism that disclosure triggers in their reasoning traces. We replicate out findings across LLMs and reasoning depths. A second study decomposes the disclosure label and shows that the divergence between the two delegates widens significantly when the paid placement is attributed to the platform. Stricter terminology (“Sponsored” instead of “Promoted”) lowers choice of paid listings but does not close this gap when the platform is named. The findings show that disclosure mandates designed for human consumers cannot by themselves protect consumers in AI-mediated commerce.
[AI-128] Learning Nuclear Structure with AI: Radii and Collectivity
链接: https://arxiv.org/abs/2609.17838
作者: Giuliano Giacalone,Sokratis Trifinopoulos,Mike Williams
类目: Nuclear Theory (nucl-th); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Nuclear Experiment (nucl-ex)
备注: 15 pages, 7 figures, 2 tables
Abstract:Low-energy nuclear structure is encoded in a broad body of experimental information across the chart of nuclides. Learning how this information is organized across observables and nuclei can provide a data-driven empirical baseline for theoretical extrapolations and experimental design. Here, we develop held-out ensembles based on NuCLR (Nuclear Co-Learned Representations), a multi-task model of nuclear data, to study charge radii and electric-quadrupole transition strengths. Out-of-fold (OOF) validation shows that shared representation improves performance over single-task learning, yielding a charge-radius \mathrmRMS deviation of 0.0147~\rm fm and a \mathrmB(E2) \mathrmRMS deviation of 0.192~e^2\rm b^2 across hundreds of nuclides, competitive with state-of-the-art nuclear models. Our error bars estimate the expected prediction accuracy across the nuclear chart, highlighting regions where new data would encode information beyond the learned patterns. NuCLR thus serves as a data-driven surveyor of nuclear structure and a step toward a shared, multi-observable foundation model of the nuclear chart.
[AI-129] SAiFE-gym: Model-based Environments for Automated Market Making with Concentrated Liquidity
链接: https://arxiv.org/abs/2609.17788
作者: Georgios Chionas,Charalampos Kleitsikas,Stefanos Leonardos,Leandro Sánchez-Betancourt,Carmine Ventre
类目: Trading and Market Microstructure (q-fin.TR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Mathematical Finance (q-fin.MF)
备注: 8 pages
Abstract:We present SAiFE_gym, a Python module that provides a collection of simulation environments for studying trading problems in Constant Product Markets (CPMs) with Concentrated Liquidity (CL). These markets give Liquidity Providers (LPs) granular control over how their capital is allocated and enable them to adjust their range of liquidity provision dynamically based on market conditions, which in turn, dictates how they earn fees. We decompose the microstructure of CPMs with CL in interactive components that allow researchers and practitioners to capture various economic settings. We employ a vectorized approach to optimize our environments, making them scalable for high dimensional Reinforcement Learning (RL) workflows that best describe sequential decision problems. We demonstrate the benefits of our environments by evaluating the performance of RL agents in CPMs with CL under uncertainty in market parameters.
[AI-130] Information Set Emulation: Causal Certificates for AI Derived EHR Features
链接: https://arxiv.org/abs/2609.17777
作者: Takes Fujita(VRI),Nobutaka Hattori(Department of Neurology, Juntendo University School of Medicine)
类目: Methodology (stat.ME); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注: 44 pages, 1 figure. Ancillary files include simulation code, seeds, and replicate-level results
Abstract:AI and large language models can recover clinically meaningful features from electronic health records (EHRs), but predictive usefulness does not establish admissibility for causal inference. We introduce information set emulation: an AI typed lift attaches source evidence, clinical and recording times, decision-time availability, representation version, proposed causal roles, and unresolved ambiguity to extracted features under a locked target trial. Causal certificates record auditable evidence for those roles. Features with unresolved downstream roles are routed to compatible reporting or separate analyses. Typed evidence defines an observational fiber of causal worlds consistent with the observed law. The locked scalar estimand maps this fiber to a compatible image whose squared Chebyshev radius equals the residual minimax mean squared error when the image is nonempty and compact. This classical identity provides a target-specific measure of information ambiguity. The contribution is its integration with a joint EHR observation map and an auditable certificate architecture. Under explicit exchangeability, positivity, and nuisance-consistency conditions, we give identification and cross-fitted augmented inverse probability weighted estimation, distinguishing empirical and population targets. An EHR compression-drift identity separates the roles of frame presence, treatment assignment, and outcome observation. Artificial simulations and a common-law finite-world example illustrate estimation failures and information-radius reduction. Synthetic Phase 0 notes demonstrate audit diagnostics; a separate role-specific analysis spread illustrates routing and is not an exact fiber radius. All experiments are synthetic. The framework specifies when reconstructed information can support a point claim and when compatible reporting is required. Comments: 44 pages, 1 figure. Ancillary files include simulation code, seeds, and replicate-level results Subjects: Methodology (stat.ME); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM) Cite as: arXiv:2609.17777 [stat.ME] (or arXiv:2609.17777v1 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2609.17777 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-131] When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments
链接: https://arxiv.org/abs/2609.17772
作者: Takes Fujita(VRI),Nobutaka Hattori(Department of Neurology, Juntendo University School of Medicine)
类目: Methodology (stat.ME); Artificial Intelligence (cs.AI)
备注: 29 pages. Ancillary files include simulation code, seeds, and replicate-level results
Abstract:AI-generated covariates from notes, conversations, images, and wearable streams can change the causal question when their roles are left unspecified. A generated feature may represent a treatment version, pre-action state, history, design variable, mediator, outcome proxy, observation process, or intercurrent event; these roles are not interchangeable. We formulate a causal type discipline for sequential experiments: a versioned representation map, a causal role classifier, a claim-status filter, and an estimand lock. The lock fixes a standardized proximal effect before generated covariates enter the analysis. Under audit correctness and standard identification assumptions, admissible role assignments preserve this estimand. We apply the established conditional-covariance characterization of compression bias to substitution of generated representations for design-relevant states. A standardized decomposition separates compression, conditional-law, and standardization drift. Further results cover mediator adjustment, post-action leakage, marker-intervention conflation, outcome-guided discovery, and state-measurement error. Cluster-level orthogonal estimators distinguish empirical and superpopulation targets under repeated sessions and missing outcomes. Simulations show that refinement helps when it retains design-relevant information, whereas design erasure, leakage, and same-data marker selection can produce bias or undercoverage. The framework places causal semantics and claim status before confirmatory inference with generated representations. Comments: 29 pages. Ancillary files include simulation code, seeds, and replicate-level results Subjects: Methodology (stat.ME); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.17772 [stat.ME] (or arXiv:2609.17772v1 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2609.17772 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-132] Rethinking Domain Specialization for Open-Ended Scientific Reasoning in Astronomy Language Models
链接: https://arxiv.org/abs/2609.17644
作者: Vanessa Lama,Sanjay Das,Emily Herron,Yuan-Sen Ting,Tijmen de Haan,Junqi Yin,Tirthankar Ghosal,Feiyi Wang
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Domain-specialized language models are widely used for scientific question answering, but stronger general-purpose systems raise a sharper question: when does domain-specific fine-tuning remain valuable for open-ended scientific reasoning? We study this in astronomy with a curated QA benchmark from publicly available 2017–2026 Olympiad-style materials. The free-response subset contains 300 questions, including 204 text-only and 96 image-linked examples. We compare open-weight and API-served general-purpose, multimodal, and astronomy-specialized models using judge-based correctness and complementary reference metrics. Strong general-purpose models establish the highest correctness baseline in this testbed, while analyses of metric agreement, judge sensitivity, benchmark composition, and modality reveal variation not captured by a single leaderboard. These results motivate treating domain specialization as a task- and deployment-dependent property and highlight the role of domain-specific evaluation in determining which models, capabilities, and evaluation criteria are appropriate for scientific workflows.
[AI-133] Structure is not mechanism: high-gain gated-FFN rows across text and genomic foundation models
链接: https://arxiv.org/abs/2609.17599
作者: Alexandros Tzanakakis,Aris Karatzikos,Ilias Georgakopoulos-Soares
类目: Genomics (q-bio.GN); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 25 pages, 5 main figures, 2 supplementary figures
Abstract:A small number of unusually high-gain parameters can exert disproportionate effects in transformer language models, but whether analogous structures recur in genomic foundation models and whether structural geometry determines functional importance remains unknown. We analyzed high-gain rows in gated feed-forward networks across text and genomic foundation models, including a frozen 22-model causal census. Computing an associated bilinear weight operator exactly, without a diagonal approximation, we tested whether structural extremeness is a transferable mechanism. Activation-derived candidates were functionally enriched relative to random and top-norm same-layer controls, yet neither spectral concentration nor operator magnitude predicted causal effect size, and these associations vanished within the endpoint-homogeneous text-decoder subset. A within-layer sweep of 36 rows in one genomic and one text decoder resolved this into two regimes: below the detector’s acceptance threshold the ratio carried no positive information about causal damage, whereas above it the ratio ordered rows strongly but did not grade severity as a dose-response. The same sweep revealed a second individually catastrophic row invisible to a one-candidate-per-model census, and non-additive damage among co-located critical rows. Case studies showed divergent causal organizations: a robust super-additive pair interaction in DNABERT-2, and in GENERator a sharply position-localized dependence in which preserving or restoring the row’s beginning-of-sequence contribution rescued essentially all native-loss damage. High-gain gated-FFN rows are therefore a recurrent architectural phenotype whose structural prominence acts as an enrichment signal, not a calibrated measure of functional criticality or a specification of causal organization. Enrichment is general, but the mechanism is model-specific.
[AI-134] Math for AI safety: an invitation for mathematicians
链接: https://arxiv.org/abs/2609.15289
作者: Lionel Levine
类目: History and Overview (math.HO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 33 pages, 5 figures
Abstract:Artificial intelligence threatens to outrun human understanding and control. New mathematics is needed to design AI that is legible, steerable, and cooperative with humanity. I organize this invitation by mathematical field, so you can turn straight to your own: logic and game theory for cooperation; probability for agency and world-models; algebra and representation theory for learned features; analysis and geometry for generalization and training dynamics. Each section ends with an open problem that is accessible to a working mathematician with no prior experience in AI safety.
机器学习
[LG-0] Exponential Hardness of Off-Policy Evaluation under History-Dependent Logging
链接: https://arxiv.org/abs/2609.19135
作者: Pranaya Jajoo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Can a logged dataset visit every hidden state frequently and still be exponentially uninformative about a target policy’s value? We show that it can when the logger depends on history. For every horizon H \ge 3 , we construct two POMDPs with at most two latent states per stage, three actions, and a common logger with three memory states. Action coverage, belief coverage, and two behavior-marginal outcome-revealing conditions all have constants independent of H . Nevertheless, evaluating a known deterministic target policy to accuracy 1/8 requires \Theta((3/2)^H \log(1/\delta)) logged episodes at confidence 1-\delta , for 0 \delta \le 1/4 , even when both candidate models are known. The mechanism is simple: a reset erases the unknown transition that determines the target value. We characterize the resulting statistical experiment exactly and obtain a matching optimal estimator. A directed two-lane gridworld realizes the construction, and trajectory simulations agree with its finite-sample prediction. The result establishes intractability for the history-dependent-logging, model-based case posed by Zhang and Jiang (2025, arXiv:2503.01134), under their behavior-marginal definition of revealing.
[LG-1] How Model Growth Recursion and Boundary Operators Influence Scaling Exponents
链接: https://arxiv.org/abs/2609.19107
作者: Zixi Chen,Akshay Vegesna,Samip Dahal,Andrew Gordon Wilson
类目: Machine Learning (cs.LG)
*备注:
Abstract:Scaling laws predict how loss decreases with increases in computation. We show, contrary to conventional wisdom, that architectural interventions can modify scaling exponents in pre-training, leading to exponential improvements in performance with increases in computation. As an anchoring point, we consider the architectural formulation of looped transformers. Although not typically used in this way, looping, also known as recursive depth, provides a mechanism for model growth, by increasing the number of loops during training. Model growth, with and without shared weights, provides the biggest changes to the scaling exponents. In particular, a 7.4B model growth architecture matches GPT-3 13B on CORE with roughly 20\times less compute, and has compute efficiency gains that increase with scale. Moreover, simply using a boundary operator in a vanilla transformer, which normalizes and injects an earlier block, also provides increasing compute-efficiency gains, although to a lesser extent. In the data-constrained, multi-epoch setting, standard looping has a useful regularizing effect, where we find it is compute-optimal to increase the number of loops with scale. These results can be understood through the lens of computational depth: for a given computational budget, we wish to increase the usable depth of the transformer, which can lead to efficiency gains that increase with scale.
[LG-2] Evidence-Grounded Agent ic Formulation Development in an Autonomous Laboratory
链接: https://arxiv.org/abs/2609.19099
作者: Michael M. Craig,Riley J. Hickman,Yingshan Ma,Rémi Piché-Taillefer,Christine Allen,Pauric Bannigan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Self-emulsifying drug delivery systems (SEDDS) can improve the oral bioavailability of poorly soluble drugs, but identifying high-performing formulations remains experimentally intensive. We present Andromeda 2, an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools to design and execute successive formulation batches. Using a miniaturized automated laboratory at a matched budget, we benchmark it against Andromeda 1, a probabilistic optimization model deployed across dozens of live development projects, and a wet-lab design-of-experiments (DoE) campaign. For paclitaxel, Andromeda 2 achieved a 50% high-performance hit rate versus 17% for Andromeda 1 and 2% for DoE, and identified 12 formulations meeting all four target product profile (TPP) objectives versus 6 and 0, respectively. Median AUC_10-240 was 70.1, 12.0, and 3.5 mg \cdot min/mL, while maximum AUC was comparable between Andromeda 2 and Andromeda 1. A selected full-TPP formulation achieved an apparent effective paclitaxel loading of 19 \pm 5% w/w at the first FaSSIF measurement, approximately 3.3-fold higher than the 5.7% w/w loading reported for a published paclitaxel S-SEDDS. A controlled ablation showed that access to structured in-house experimental evidence increased mean AUC by 34%.
[LG-3] LightSleepX: A Lightweight Inception-Based Dual-Modal Network for Sleep Staging
链接: https://arxiv.org/abs/2609.19062
作者: Yi Wang
类目: Machine Learning (cs.LG)
*备注: 6 pages, 2 figures, 5 tables
Abstract:Automatic sleep staging is fundamental to personal health monitoring, yet many existing approaches are ill-suited for real-world applications. Traditional pipelines often rely on hand-crafted features or shallow machine learning models that struggle to generalize, while state-of-the-art deep learning methods, though accurate, are computationally heavy and impractical for resource-constrained environments. This paper introduces LightSleepX, a lightweight framework designed to deliver robust sleep analysis in resource-constrained environments. LightSleepX combines an Inception-style architecture with depthwise separable convolutions and Multi-scale Enhanced Attention for efficient multi-modal EEG/EOG feature extraction, and a Mamba encoder for rule-free long-range temporal modeling. On public benchmark datasets, LightSleepX achieves 85.9% accuracy and a 0.803 macro-F1 score on Sleep-EDF-20, and 81.8% accuracy and a 0.796 macro-F1 score on the cross-subject ISRUC-S3 dataset. With 0.049M parameters and 195.9 MFLOPs, the framework targets practical local deployment where computational cost and privacy are central constraints.
[LG-4] Integrated Optimization of Automated Warehouse Operations and Last-Mile Transport for Differentiated On-Demand Delivery
链接: https://arxiv.org/abs/2609.19048
作者: Xiaozhu Sun,Bilal Farooq
类目: Machine Learning (cs.LG)
*备注:
Abstract:In the context of differentiated on-demand goods delivery services, this study proposes an integrated optimization method for automated guided vehicles (AGVs) based smart warehouse operations and the last-mile multi-modal transport. A deep reinforcement learning algorithm for multi-objective joint scheduling is designed to establish a dynamic connection between two systems, solving key challenges such as achieving high-throughput continuous order scheduling, meeting competing requirements, and improving the overall system sensitivity and adaptability. For warehouse optimization within this framework, we propose an improved algorithm based on multi-objective, Multi-Reward Machines-A* Guided Deep Q-Network (MORM-AGDQN), which combines service level, system cost, and external transportation demand. For external optimization, we propose an improved algorithm based on a Multi-Reward, Multi Head attention-Heterogeneous Capacity Vehicle Routing Problem (MRMH-HCVRP) framework, which incorporates the optimized scheduling order sequence and grouping, combined with customer location, demand, and priority, vehicle capacity, speed, and service range. The results show that the proposed framework significantly outperforms traditional methods, achieving 100% on-time delivery rate for warehousing operations. After joint optimization, the average delivery time for the last mile was reduced by 29.3% to 53.2%, the total transportation distance was reduced by 46.4%, the high-priority service rate was increased to over 92%, and a balance was maintained between operating costs and customer satisfaction.
[LG-5] winMark: A Unified Watermark for Provable Survival Under Feature and Logit Distillation
链接: https://arxiv.org/abs/2609.19011
作者: Redwanul Karim,Tobias Feigl,Christopher Mutschler,Felix Ott
类目: Machine Learning (cs.LG)
*备注: 10 figures, 46 pages
Abstract:We propose TwinMark, a watermarking scheme that reads a single SHAKE128 secret through two complementary linear functionals of model-output summaries: a covariance projector against the carrier-set covariance (cov-Feat) and a class-conditional Fisher-aligned linear carrier decoded from class-mean logits (cc-FALC). The two readouts share one bit vector and cover the two extraction surfaces of a deployed vision model: a classifier API attacked by KL knowledge distillation (KD) (Std. KL-KD), and a representation-only host attacked by feature-matching KD (FM-KD). Each readout admits a teacher-measurable a posteriori certificate that lower-bounds post-distillation detection power, and the two channels combine under a regime-restricted OR rule whose test statistic (calibrated null or bit vote) is selected by the exposed surface. cov-Feat admits a rank-blind operator-norm certificate, cc-FALC admits a centered-logit-gap certificate that decouples bit capacity from class count: at K=1024 in m=100 classes (a 10.24x over-encoding), the bit-vote attains z=23.0 sigma at a teacher-accuracy cost of +0.9±0.2%p. Across 13 attacks on CIFAR-10, CIFAR-100, and Mini-ImageNet, TwinMark verifies on every cell whose post-attack model retains task utility, survives cross-architecture distillation onto ResNet-18/50, VGG-16, and MobileNet-V3, and ports to GNSS few-shot, VOC detection, ISIC segmentation, and STL-10 SimCLR.
[LG-6] Capability Emergence Can Be Forecast: Per-Seed In Advance With Calibrated Intervals Certified False Alarms and a Blind Pre-Registered Gate
链接: https://arxiv.org/abs/2609.19000
作者: Gunner Levi Howe
类目: Machine Learning (cs.LG)
*备注:
Abstract:Emergent capabilities are widely treated as unpredictable: loss improves smoothly while abilities appear abruptly. Prior work offers early-warning indicators but never scores them as forecasts: no lead time at controlled false-alarm rate, no calibration, no negatives, no blind tests. We supply that discipline and show that, in grokking model systems and small language models, emergence timing is forecastable per run, in advance, with calibrated uncertainty. Across 30 transformers at identical configuration, the formation time of the previous-token head forecasts each seed’s induction-head emergence at Spearman rho=0.977 with median lead 975 steps (~15% of training); a best-case loss rule ties the ranking with 50-step lead (a nowcast). Conformal intervals covered 15/15 held-out seeds, and the frozen rule passed blind pre-registered gates on TWO never-seen configurations (10/10 and 9/10 coverage). A trap-language rung then attacked our own rule as pre-registered: where previous-token context pays for the task itself, the bare precursor false-alarms on 10/10 capability-blocked runs, while the mechanism-composed conjunction is certified in both language classes (0 false alarms) and times emergence at rho=1.000. Finally, a gap-origin study broke the fixed offset (both lr and batch move the gap ~2.3x; no external clock owns it) and revealed the law beneath: across 80 valid-anchor runs the anchor fires at 0.843 of time-to-emergence – t_event ~= 1.19 x t_anchor – and this multiplicative rule passed its own blind gate (5/5) at a third unseen configuration. False alarms are certified against 33 manufactured negatives. The precursor leads across 3 public model families (Pythia, OLMo, OLMo-2; 7 suites), with OLMo-2 at 1B tokens showing precursor formed, capability absent. Four pre-registered kill criteria fired and are reported. Every freeze precedes its data in a public commit chain.
[LG-7] he Automaton Underneath: The Additive Input Pathway Is a Parasitic Attractor for State Tracking in Householder Linear RNN
链接: https://arxiv.org/abs/2609.18966
作者: Gunner Levi Howe
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:Linear RNNs with input-dependent Householder-product transitions (DeltaNet/DeltaProduct-class) can provably represent hard state-tracking automata, yet trained models fail to length-generalize – a gap recent work attributes to optimization, without a causal account. We give one, in a pre-registered, within-architecture causal ablation: the same model with one term deleted – the additive input injection b_t = W_b e_t . With b_t , models fit length 32 and collapse out-of-distribution on parity, S_4 , A_5 , and non-solvable S_5 word problems (0.20 at position 512 on S_5 ). Without it – input acting only through the orthogonal transitions – the same architecture learns the exact automaton: median accuracy 1.00 at 16x the training length, at every width admitted by a representation law we state and test: the minimal number of Householder factors per token equals the maximal reflection length of the task’s generators in the format-pinned representation (parity 1, S_4 3, A_5 and S_5 4); below it, nothing fits. The contrast with DeltaProduct’s S_4 / A_5 at n_h=2 (group-element classification, SO(3) realization) shows the law is representation-relative: task format is a hidden variable in state-tracking benchmarks. Two pre-registered arms locate the mechanism. (i) Initialized at a verified-exact solution with W_b=0 , Adam grows the additive path and pulls the model off the exact solution; -b controls stay at 1.00. (ii) Our registered prediction that the fit routes through b_t fired its kill criterion: all 49 fitting seeds retain in-domain fit under W_b:=0 – and at law-minimal width, zeroing W_b at inference restores exact generalization (parity 5/5, S_5 5/5, S_4 4/5, A_5 4/5). The additive pathway is parasitic: it destabilizes, then conceals, a correctly learned automaton. All 202 runs pre-registered; all numbers regenerate from artifacts.
[LG-8] FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning
链接: https://arxiv.org/abs/2609.18964
作者: Zhilin He,Gauri Joshi
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Federated Reinforcement Learning (FRL) enables collaborative policy learning across distributed agents with heterogeneous environments. While recent methods based on variance reduction, divergence penalization, and momentum optimization improve FRL under heterogeneous settings, they still primarily synchronize policy or value-network parameters and do not explicitly address distributional mismatch among heterogeneous clients. Therefore, we propose \textbfFedGuide, a FRL framework that uses diffusion priors as behavior models to provide personalized data supported distributions for heterogeneous local policy learning. Instead of directly averaging local policies, FedGuide aggregates those diffusion priors through Optimal-Transport Mixture-of-Experts (OT-MoE), preserving heterogeneous behavior modes in distribution space. It further develops a Distribution Correction Estimation (DICE) value baseline to provide low-variance, return-aware guidance for local policy improvement. Experiments across heterogeneous environments show that FedGuide outperforms representative FRL methods in client-average returns, final-round performance, and worst-round robustness, while maintaining stable learning under stronger heterogeneity.
[LG-9] Changepoint-Aware World Models: Detecting Dynamics Shifts and Recovering by Forgetting Stale Replay in Model-Based RL
链接: https://arxiv.org/abs/2609.18950
作者: Everest Yang
类目: Machine Learning (cs.LG)
*备注: Accepted to the RSS 2026 Workshop on Robot World Models (R-WM)
Abstract:A robot’s learned model of its own dynamics is only valid until those dynamics change: actuators wear, payloads shift, and joints stiffen. A model-based agent that keeps training as if nothing happened adapts slowly, dragged back by a replay buffer full of stale experience. We present Changepoint-Aware World Models (CAWM), a DreamerV3 agent that detects an abrupt dynamics shift from its own internal prediction error, using an online CUSUM test against a rolling baseline that fires only on abrupt change rather than on slow learning drift. It then forgets stale replay, keeping the learned representation while flushing obsolete data. On simulated locomotion under two robot-relevant shifts, doubled gravity and halved actuator gain, CAWM recovers substantially faster than passive retraining. It also beats a strong baseline that respawns a fresh dynamics model on detection, the deep-world-model analogue of model-bank methods. With the response triggered at the shift, CAWM gains +95 to +153 return in the first 30k post-shift frames over three seeds, while matching that respawn at asymptote. Running the detector in closed loop reproduces this gain on the gravity shift. The benefit holds across both shift types, and is largest when the shift is severe enough that old data is genuinely obsolete.
[LG-10] Preventing Model Collapse: A Fisher-Rao Perspective on the Dynamics of Training with Synthetic Data
链接: https://arxiv.org/abs/2609.18878
作者: Matteo Marchi,João Pedro Silvestre,Bahman Gharesifard,Paulo Tabuada
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 8 pages. Extended version of the paper accepted for presentation at the 2026 65th IEEE Conference on Decision and Control (CDC). This version contains the full proofs of the auxiliary lemmas, omitted from the conference version for space
Abstract:Large Language Models (LLMs) are now routinely trained using synthetic data, since high-quality human data has been exhausted by the ever increasing needs of larger and larger models. However, recursive training on synthetic data frequently induces model collapse, a degenerative feedback loop where models progressively forget the true underlying data distribution. Training on a mixture of synthetic and fresh human data is a logical countermeasure and can prevent model collapse. However, it is an open question as to what is the exact minimum required ratio of human-to-synthetic data to maintain training stability. In this paper, we establish rigorous theoretical guarantees on the minimum rate of human data required to prevent model collapse. Although previous work established a formal lower bound for this ratio, such bound can be vacuous for very high dimensions, as the analysis relies on the usual Euclidean metric in R^n and is not adapted to the space of categorical probability distributions. Instead, in this paper we explicitly leverage the information-geometric structure of the probability simplex by analyzing the dynamics of the process under the Fisher-Rao metric. We derive quantitative contraction and invariance bounds that are stable and do not become trivial as the dimensions increase. Thus, we show that the effective required data ratio to prevent model collapse is different than previously implied.
[LG-11] Physics-based prediction uncertainty quantification and decision-making for IN718 crystallographic texture intensity across LPBF defocus regimes
链接: https://arxiv.org/abs/2609.18863
作者: Yisheng Lu,John Riris,Jie Song,Yao Fu,Jie Chen
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE)
*备注: 69 pages, 9 figures, 9 tables. Includes supplementary material (S1-S7)
Abstract:Reliable prediction of crystallographic texture in laser powder bed fusion is critical for linking process conditions with anisotropic response and for qualification. However, black-box models may fail under shift and cannot distinguish weak data support from loss of physical validity. This study develops a two-stage physics-based model for 001 || BD (build direction) texture in Inconel 718. Stage 1 maps process variables to melting mode and melt pool geometry. Stage 2 predicts texture by combining an empirical physics model with a random-forest residual model. A k-nearest-neighbor weight attenuates residual corrections for poorly supported queries, while a study-specific areal beam-power-density criterion withholds predictions outside the adopted conduction envelope. Conformal intervals are evaluated on the retained physics-valid set, and SHAP and Sobol analyses assess residual sensitivity. Under a controlled leave-one-defocus-out evaluation, the physics anchor achieved R^2 = 0.778, against -0.001 for the black-box model and 0.750 for the gated hybrid. Under leave-one-group-out cross-validation, the gated hybrid reached R^2 = 0.592 against 0.538 for the black-box model. Retained-set coverage was 92.9% at a mean full width of 3.65 multiples of a uniform distribution (MUD) under grouped cross-validation and 100% at a width of 3.21 MUD under transfer to a withheld +80 mm defocus regime. An illustrative mapping produced a retained BD elastic-modulus span of 127-187 GPa. On nine conditions from a separately built sample set, the framework withheld three, attenuated three, and matched the measured ordering for the rest. Separating data applicability, physics validity, and predictive uncertainty into distinct decisions lets the framework transfer where an unconstrained model does not, and withhold predictions where no model class performs adequately.
[LG-12] Interpretable Multi-Instance Learning Enables Early Prediction of Key Molecular Alterations from Routine Flow Cytometry in Acute Myeloid Leukemia
链接: https://arxiv.org/abs/2609.18825
作者: Jonathan Legrand(IMB, MONC),Aguirre Mimoun(CHU Bordeaux),Baudouin Denis de Senneville(IMB, MONC),Audrey Bidet(CHU Bordeaux),Pierre-Yves Dumas(CHU Bordeaux, Inserm U1312 - BRIC),Christèle Etchegaray(MONC, IMB)
类目: Machine Learning (cs.LG)
*备注:
Abstract:Background: Molecular testing for NPM1 and FLT3-ITD mutations guides critical early treatment decisions in acute myeloid leukemia (AML), but results can take weeks, long after these decisions must be made. Flow cytometry, already performed within hours of admission as part of routine care, may carry enough signal to predict these mutations directly, without added cost or delay. Methods: We developed an interpretable multi-instance learning classifier based on a decision tree, in which each patient sample is modeled as a collection of individual cells and mutation status is inferred from cell-level predictions. The model was benchmarked against a random forest trained on clinical variables and a deep convolutional neural network adapted for multitube flow cytometry data. Performance was assessed by cross-validation on a discovery cohort of 197 patients and tested on an independent cohort of 161 patients, using the area under the receiver operating characteristic curve (AUROC) and positive predictive value. Results: In cross-validation on the discovery cohort, the MIL model achieved mean AUROCs of 0.96 (SD=0.05) for NPM1 and 0.86 (SD=0.10) for FLT3-ITD, outperforming the clinical baseline and matching deep learning approaches. The model then successfully generalized to the independent test cohort of 161 patients, reaching AUROCs of 0.90 (NPM1) and 0.82 (FLT3-ITD), with positive predictive values of 0.87 and 0.68, respectively. Cell-level interpretation recovered established immunophenotypic signatures (CD33 ^+ /CD34___ for NPM1-mutated cases, CD33 ^+ /low side-scatter for FLT3-ITD), directly linking model predictions to known biology. Conclusions: These results show that an interpretable model applied to data already collected in routine care can predict AML molecular status within hours, offering a practical route to earlier, biology-informed treatment decisions.
[LG-13] WaveTLM: Reliable Time-Series Language Modeling through Task Compilation
链接: https://arxiv.org/abs/2609.18812
作者: Jiahui Chen,Bingke Zhu,Hongyu Pan,Yingying Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Time-series language models provide a shared natural-language interface across temporal tasks, but plausible text does not guarantee reliable task outputs. Responses may appear reasonable while hallucinating the required object: numerical sequences can violate shape, scale, channel order, or temporal alignment, and textual decisions can fall outside the legal label space. We formulate reliable time-series language modeling, separating task-object reliability from predictive quality. We introduce ExecTS-QA, a contract-grounded benchmark spanning forecasting, imputation, classification, anomaly detection, and waveform analysis. We further propose WaveTLM, a unified compiler-executor model whose task compiler transforms user requests, visible arguments, and wave-grounded evidence into typed task states, while task-native executors construct numerical tensors, legal decisions, or structured records. On ExecTS-QA, a single WaveTLM checkpoint achieves 99.40% contract-valid coverage, compared with 37.83% for the strongest evaluated string-first baseline, while retaining balanced predictive performance across all five task families. Evaluations on SciTS, TSQA, IRTS-ToolBench, and ARFBench provide additional evidence of transfer. The code, construction scripts, and ExecTS-QA dataset will be publicly released upon publication. These results show that task compilation can convert plausible language generation into reliable time-series outputs.
[LG-14] A Convergence Framework for Deep V-Learning: Error Propagation and Sharp Action-Gap Bounds
链接: https://arxiv.org/abs/2609.18782
作者: Yury Kolomeytsev
类目: Machine Learning (cs.LG); Robotics (cs.RO); Optimization and Control (math.OC)
*备注: 37 pages
Abstract:We establish convergence bounds for deep V -learning with horizon H . The algorithm fits a scalar value function to targets from executed transitions and selects actions using a predictive model and the value function. For current observed-successor targets with fresh true-kernel outcomes, the conditional mean is \mathcalT^\beta V , which averages over behavior-policy actions. The Bellman optimality update is \mathcalT V . We decompose the update error into six residuals: fitting, transition reuse, target construction, replay, action selection, and exploration. Under L^s concentrability, their L^p norms ( p=s/(s-1) ) control expected L^1 policy loss. The bound explicitly weights residuals from only the last H-1 update blocks, plus an initialization term for shorter runs. We quantify the cost of a shared sampling distribution across horizon levels. For statistical error bounds of order n^-\nu , we derive optimal continuous allocations and an integer allocation whose objective is within a factor 2^\nu of the constrained optimum. A margin condition with exponent \alpha gives action error of order \Lambda^1+\alpha/p , where \Lambda combines network drift and score error; a one-step construction proves the exponent sharp. Bounds on the distance between frozen and optimal scores transfer an optimal-gap condition to frozen-iterate gap bounds while retaining the mass of optimal ties. Survival probabilities and coverage conditions at deployment yield bounds for policies selected with approximate scores. Separate spatial ReLU networks per horizon level give a conditional neural regression rate, and the finite-state case gives a log-free expected fit rate. These results give expected policy-loss consistency for the fixed-horizon generative-reset approximate-ERM procedure with exact action scores and provide an explicit residual-decay criterion for FIFO/interleaved SGD.
[LG-15] When Edit Flows are Edit Jumps: replicating Edit Flows and EvoFlows
链接: https://arxiv.org/abs/2609.18745
作者: Gabriel Bénédict,Melanie Buechler,Gerard Riera-Solà,Chloé de Ancos,Yves Gaetan Nana Teukam,Moritz Freidank
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Antibody lead optimization calls for a small, bounded set of edits to an existing candidate: substitutions, but also insertions and deletions. Edit-based generative models are the only ones that allocate such an edit budget without fixing the edit positions, the edit count, or the output length in advance. However, the existing approaches Edit Flows and EvoFlows did not release code or complete training specifications. Here, we show that both methods follow the same underlying process – edits firing one at a time, at learned rates, in continuous time – the pure-jump case of generator matching over finite sequences. With EditJumps we introduce the first open implementation of this framework, with a single generalist antibody editor trained on 1.66M Observed Antibody Space homolog pairs to propose homolog-like variants of a seed sequence, editing unseen leads zero-shot, without the per-family retraining original approaches require. Replicating this system from scratch exposes why open code is essential for generative biology: reconciling published edit distributions required reverse-engineering an undocumented rate-scaling hyperparameter that dictates realized mutation counts. Moreover, we show that published evaluation metrics are highly sensitive to reference sample size, frequently flipping method rankings. We release our full codebase, automated test suite, and configurations at: this https URL
[LG-16] oward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study
链接: https://arxiv.org/abs/2609.18704
作者: Shenjia Ding,David Flynn,Paul Harvey
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:Modern networks must support changing topologies, configurations, and performance objectives, motivating fast and reliable performance estimation. Network digital twins (NDTs) enable what-if analysis for performance estimation in such network scenarios, however, existing machine learning-based NDT approaches often rely on entire topology representations, which are inherently monolithic and lack reusability under topological or traffic changes in the network. This paper introduces a composable NDT approach that decomposes networks into subgraphs represented by reusable unit twins that capture subgraph structure, configuration and traffic behaviours. A lightweight composer aggregates unit twin combinations to create NDTs that predict per-route end-to-end latency through an overall topology. Evaluation across controlled synthetic topologies and diverse traffic scenarios, real-world Topology Zoo topologies, and a public NDT challenge dataset demonstrates that the composable NDTs achieve high in-distribution accuracy while remaining stable under out-of-distribution scenarios. Comparison with monolithic full topology NDTs demonstrates that our composable approach achieves reusability, while achieving comparable or superior accuracy.
[LG-17] VLA-ULAP: Interleaving Cloud VLA Calls with Ultra-Lightweight Local Action Prediction at the Edge
链接: https://arxiv.org/abs/2609.18663
作者: Deyu Cao,Ryuji Oi,Kosuke Matsushima,Yuxuan Pan,Ziheng Wang,Daichi Fujiki,Atsutake Kosuge
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Preprint
Abstract:Billion-parameter vision–language–action (VLA) policies demand substantial onboard power, while communication delays in remote inference hinder timely responses. We propose VLA-ULAP, which interleaves remote VLA calls with an Ultra-Lightweight Local Action Predictor (ULAP). With approximately 7.4M parameters including the frozen vision encoder, ULAP combines current views, proprioception, and executed action history to predict chunks in one pass. Trained independently, it requires no VLA hidden states, online verification, or server round trips. On Jetson Orin Nano, ULAP takes 19.9 ms and 0.183 J per inference, compared with 284.3 ms and 50.55 J for GR00T on RTX A6000. Across three simulated base-policy/benchmark pairs, selected operating points remove 48.8–76.7% of VLA calls while retaining 95.0–97.5% of the baseline success rate. Against local VLA-acceleration alternatives on VLA-JEPA, ULAP uses an estimated 49.2% less inference time and 51.0% less GPU energy per successful episode than ACT at comparable success rates, and 77.1% less time and 79.9% less energy than SP-VLA at equal success rates. Physical SO-101 experiments retain 95.2–100% of the baseline success rate across seen and held-out placements while reducing inference time by an estimated 47.9–58.0% and inference-device energy by 52.1–62.5%, based on successful-episode call counts and measured device costs. Faster responses also improve dynamic-task success rates: in latency-aware LIBERO-Safety simulation, VLA-ULAP exceeds \pi_0.5 by 11.0 and 15.5 percentage points on two tasks while approximately halving VLA calls.
[LG-18] Revisiting Distributed Sign-Based Variance Reduction
链接: https://arxiv.org/abs/2609.18656
作者: Wei Jiang,Zechao Li,Lijun Zhang
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注:
Abstract:Sign-based methods reduce communication costs in distributed environments, but aggregating local signs can introduce bias when data are heterogeneous. As a result, existing sign-based variance reduction methods fail to obtain the optimal convergence rates. In this paper, we solve this problem and obtain optimal rates for both nonconvex stochastic and finite-sum optimization. We first give a counterexample showing that majority voting can fail to approach stationary points even with exact local gradients. Motivated by this limitation, we propose tracking the global gradient at the server through unbiased compression of recursive gradient increments. As a result, we can obtain the convergence rates of O(\sqrtd/K+\sqrt d (a/(nK))^1/3) for the \ell_1 -norm and O(\sqrta/K+\sqrt a/(nK)^1/3) for the \ell_2 -norm. Here, K is the iteration number, n is the number of workers, d is the dimension, and a=1+\omega , with \omega denoting the compressor’s relative variance. For finite-sum problems with M components, we combine periodic exact gradient refreshes with compressed component-gradient differences. The resulting total sample complexities are O(M+d\sqrtaM\epsilon^-2) and O(M+a\sqrt M\ epsilon^-2) for \ell_1 and \ell_2 gradient norms at most \epsilon , matching the corresponding bounds in centralized settings.
[LG-19] Learning to Program Adaptive Non-Local Observables for Machine Learning
链接: https://arxiv.org/abs/2609.18655
作者: Yu-Ting Lee,Samuel Yen-Chi Chen,Huan-Hsin Tseng
类目: Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注:
Abstract:Quantum neural networks (QNNs) are typically built from variational quantum circuits (VQCs), which are limited by local measurements. Adaptive non-local observables (ANO) address this by jointly optimizing circuit parameters and multi-qubit measurements. However, existing ANO-based VQCs learn only a single static observable that remains invariant across all inputs. We propose QFWP-ANO, a novel architecture which employs a classical hypernetwork to dynamically program VQC parameters and/or non-local observables conditioned on each input. On multivariate time-series forecasting across four ETT datasets, QFWP-ANO achieves the lowest MSE in 16 of 20 settings and second-lowest in the remaining four, surpassing ANO-based and other strong baselines. On reinforcement learning tasks, QFWP-ANO consistently surpasses ANO-VQCs. Our results establish input-conditioned ANO as an effective approach for enhancing QNNs.
[LG-20] How Many Labels Does Model Choice Need? Certificates and Budgets for Selective Prediction
链接: https://arxiv.org/abs/2609.18622
作者: Tetsuji Kuboyama
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 22 pages, 13 figures, 3 tables. Includes proofs and experimental details in the main text
Abstract:Classifiers can make identical predictions yet require labels to compare their selective performance: confidence ranks weight the same errors differently. We quantify this requirement for the area under the generalized risk-coverage curve (AUGRC). A prelabel lower bound rules out insufficient budgets. With all labels known, a covering linear program bounds the minimum number of labels sufficient to fix the winner (the certificate size) within K-1 labels for K candidates. For fixed K , independent uniform orders and identical predictions, the prelabel bound approaches one quarter of the pool. With iid Bernoulli errors independent of the orders, every exact acquisition policy reads almost all labels asymptotically, although a two-candidate certificate needs only half. Across 108 feature-panel comparisons on nine datasets, disagreement labels settle every accuracy choice but no AUGRC choice. A 20% budget is ruled out in 96 conditions; certificates need 56-57% on average. On ten conditions with pretrained image classifiers, confidence-score choice reads 68-91% of 10,000 labels for exact selection and 50-67% with AUGRC tolerance 5\times10^-4 . An exact stopping test works with any acquisition order. Together, these results link confidence ranks to label budgets and certified model comparison.
[LG-21] A Geometric Theory of Decision Boundaries in Structured Markov Decision Processes
链接: https://arxiv.org/abs/2609.18610
作者: Fredy Pokou(MRE, INOCS)
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Classical dynamic programming represents optimal sequential decisions through value functions and policies. While this functional representation is natural for computing optimal decisions, it does not directly identify the mathematical object governing policy reconstruction, representation complexity, or oracle-query complexity once an optimal policy is fixed. This paper addresses this question by developing a geometric theory of structured optimal policies in which the decision-boundary geometry induced by the policy becomes the primary object of analysis. We show that, under suitable structural regularity conditions, this geometry provides the minimal representation required for policy reconstruction and determines the statistical and computational complexity of the reconstruction problem. Building upon this representation, we establish structural properties of policy-induced decision geometry, introduce intrinsic notions of boundary and decision complexity, derive information-theoretic measures of decision compression, and obtain statistical guarantees for boundary estimation and policy reconstruction from black-box policy queries. Collectively, these results demonstrate that, for the structured decision problems considered here, the complexity of policy reconstruction is governed by the geometry of the decision boundary rather than by the cardinality of the ambient state space. Controlled numerical experiments examine the principal theoretical predictions and provide empirical evidence consistent with the proposed framework.
[LG-22] ReDIL-GNN: Resynthesis Domain Incremental Learning for Circuit Graph Neural Networks
链接: https://arxiv.org/abs/2609.18595
作者: Rupesh Raj Karn,Johann Knechtel,Ozgur Sinanoglu
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 12 pages
Abstract:Logic resynthesis preserves circuit functionality while changing gate vocabulary, topology, and structural statistics, creating domain shift for circuit graph neural networks (GNNs) without changing task labels. To study this setting, we introduce ReDIL-GNN, a resynthesis domain-incremental learning framework that adapts a fixed prediction or representation head as new synthesis styles arrive and evaluates retention on all previously observed domains. Because not every shift should be adapted blindly, ReDIL-GNN further introduces the Resynthesis Adaptability Index (RAI), a pre-adaptation score that combines adaptation need, source-equivalence recoverability, structural coverage, and update compatibility. We evaluate supervised hardware-security tasks and representation-learning models using task-native metrics for classifiers and source-equivalence retrieval metrics for embedding models, comparing naive fine-tuning with LwF, Online EWC, MAS, ER, A-GEM, DER++, ER+LwF, and equivalence-guided replay. Across the studied pipelines, RAI separates unsupported shifts from promising updates, ranging from 0.001 for a structurally uncovered GNN-RE ABC-rewrite shift to 0.824 for the best original-only GNN-RE adaptation case. In practice, ReDIL-GNN turns resynthesis-aware circuit learning into a deployment control loop: RAI screens each new synthesis flow before update, guiding whether to reuse the current model, apply retention-aware adaptation, or defer adaptation until the shift is better supported.
[LG-23] Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels
链接: https://arxiv.org/abs/2609.18588
作者: Souhardya Chattopadhyay,Julian Oelhaf,Antonia Schoening,Jessica Deuschel,Bitan Bhattacharyya,Christian Bergler,Andreas Maier,Siming Bayer
类目: Machine Learning (cs.LG)
*备注: 5 pages, 2 figures, 3 tables, Accepted at IEEE PES ISGT Europe 2026
Abstract:For distribution system operators, short-term load forecasting (STLF) supports congestion management, voltage control, and asset protection. Most existing approaches focus on overall accuracy across all time steps and neglect performance during high-demand (HD) periods, where larger forecast errors can increase the risk of congestion and voltage violations. In this paper, we study peak-aware STLF across three operator-relevant distribution grid aggregation levels, area codes (AC), secondary substations (SUB), and low-voltage (LV) feeders, using open datasets from the United Kingdom and Switzerland. We compare statistical baselines, machine learning models (LightGBM and XGBoost), and recent time-series foundation models (Chronos Bolt and Chronos-2) under a peak-aware evaluation framework that reports both overall and HD forecasting performance using NMAE and MAPE. The results show that Chronos-2 achieves the best HD performance across all aggregation levels, with HD-NMAE and HD-MAPE of 0.039 and 4.53% at AC, 0.080 and 9.45% at SUB, and 0.138 and 16.14% at LV, while Chronos-Bolt consistently ranks second best. Compared with the gradient boosted ML models, Chronos-2 reduces mean HD-NMAE by about 20-51% across levels while remaining best or near-best on the overall metrics. A quantile analysis of the probabilistic Chronos outputs further identifies aggregation-specific operating points, and runtime measurements indicate that foundation model inference is fast enough for practical deployment. Overall, the findings highlight peak-aware evaluation and aggregation specific quantile selection as a practical pathway toward more operationally relevant STLF in distribution networks.
[LG-24] M-Bench: A Framework for Text-to-Music System Performance Benchmarking
链接: https://arxiv.org/abs/2609.18585
作者: Giorgia Adorni,Michela Papandrea,Battista Rimoldi,Tiziano Leidi
类目: ound (cs.SD); Machine Learning (cs.LG); Multimedia (cs.MM); Audio and Speech Processing (eess.AS)
*备注:
Abstract:Text-to-music (TTM) systems are increasingly used to generate musical audio from natural-language descriptions. Robust evaluation is therefore essential, yet reliable performance comparison remains challenging. This difficulty stems from differences in system architecture, supported conditioning information, and access mode, as well as heterogeneous and fragmented metrics that cannot be applied uniformly across systems. To address these challenges, we introduce TTM-Bench, a framework that defines a common protocol for systematic, reproducible performance benchmarking of contemporary TTM systems. It evaluates performance along two dimensions: musical-content alignment, quantified by interpretable semantic, genre, and musical-descriptor agreement scores against a common musical specification and summarized by an aggregate score; and computational efficiency, characterized by generation latency and real-time factor, alongside resource use for local models and cost for hosted services. We demonstrate the framework through a preliminary comparative case study, illustrating the complementary evidence captured by these dimensions. The results show that higher musical-content alignment does not systematically coincide with lower computational demands, highlighting the importance of assessing TTM performance through distinct, interpretable measures rather than a reductive overall indicator.
[LG-25] Accurate Trace Estimation with Fewer Random Bits via Recursive TensorSketch
链接: https://arxiv.org/abs/2609.18577
作者: Mohammad Azhar Khan,Rameshwar Pratap,Amit Sharma
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:We consider the problem of estimating the trace of an implicit matrix \mathbfA \in \mathbbR^d^p\times d^p that can only be accessed through matrix-vector products queries. The \textitHutchinson trace estimator% ~\citeGirard1987algorithme, article-hutchinson is a classical sketching method for this problem. Their estimator, H_m(\mathbfA) = \frac1m \sum_i=1^m \mathbfz^(i)^T \mathbfA \mathbfz^(i), \quad \textwhere \ \mathbfz^(i)\in \mathbbR^d^p , and z^(i)_j \in N(0, 1), j\in [d^p] , satisfies the following guarantees: (i) \mathbbE[H_m(\mathbfA)]=\operatornametr(\mathbfA) , and (ii) \mathrmVar[H_m(\mathbfA)]=\frac2m||\mathbfA||_F^2 . Generating one query vector \mathbfz^(i) requires O(d^p) random bits; thus, m queries require O(md^p) random bits, which can be prohibitive in large-scale applications. Recent work by Meyer et al.~\citemeyer2025hutchinsonsestimatorbadkroneckertraceestimation proposes a variant of the Hutchinson trace estimator in which each query vector in \mathbbR^d^p is constructed as the Kronecker product of p random vectors in \mathbbR^d , requiring O(mpd) random bits for m query vectors. The estimator of~\citemeyer2025hutchinsonsestimatorbadkroneckertraceestimation is unbiased; however, its variance grows exponentially with p . In this work, we address this limitation by proposing a sketching-based estimator that requires O!\big(p (d + m)\log m\big) random bits, yields an unbiased estimate of the trace, and simultaneously achieves a variance bound that grows polynomially with p .
[LG-26] he evolution of sex for artificial intelligence: a population-genetic framework for multigenerational model populations
链接: https://arxiv.org/abs/2609.18560
作者: Giorgio F. Gilestro
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Populations and Evolution (q-bio.PE)
*备注: 22 pages, 5 figures, 1 table. Supplementary Information (26 pp) and a plain-language figure appendix for readers from biology (23 pp) are included as ancillary files. Code, configs and seeds: this https URL
Abstract:Some aspects of AI development resemble a population process in which models are specialised, retrained on the output of peers, or combined by averaging weights. These practices lead to generations of models, in the biological sense studied by population genetics. Here, I develop this parallelism and interpret multigenerational model populations in terms of sexual and asexual reproduction, formally recombining the two fields. I test these analogies in an exact inheritance model, in trained networks (recurrent, feedforward and variational autoencoder generators) and in large language models, and show that they hold generally, with some measurable architecture-specific biases. Training recursively on model output is known to lead to model collapse, a process previously described as akin to genetic drift; I develop all that follows. A minimal model of a learner retrained on its parent’s output reproduces the Wright-Fisher process exactly; verified real data added to each generation play the role of immigration, with the surprising finding that the absolute number of real data samples matters, not their share, exactly as in population genetics. Training a child on the average of its parents’ outputs cancels the benefit of having several parents, matching blending inheritance (and reviving Jenkin’s objection to Darwin), whereas combining parents so that each keeps its strongest contribution preserves it; merged language-model specialists exceeded every parent across seeds (the Fisher-Muller effect); and lineages become reproductively isolated, losing the ability to merge at all, when they have learned conflicting conventions and not when they have merely drifted apart. As AI societies become societies in time as well as in space, a mathematical framework for their inheritance acquires predictive power. Remarkably, that framework can be adapted almost wholesale from biology. Comments: 22 pages, 5 figures, 1 table. Supplementary Information (26 pp) and a plain-language figure appendix for readers from biology (23 pp) are included as ancillary files. Code, configs and seeds: this https URL Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Populations and Evolution (q-bio.PE) Cite as: arXiv:2609.18560 [cs.LG] (or arXiv:2609.18560v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.18560 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-27] Revisiting the Objective of Echo Chamber Detection
链接: https://arxiv.org/abs/2609.18545
作者: Abylaikhan Bexeit,Kushani Perera,Shanika Karunasekera,Jean Honorio
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we study the detection of an echo chamber in a social network, i.e., the identification of a set of nodes that agree on a topic, while disagreeing with the rest of nodes. We argue that this problem is different from other social network analysis problems such as community detection, and from other graph problems such as maximum graph cut and maximum clique. To the best of our knowledge, we are the first to formalize the objective function of echo chamber detection, by using the theory of Fourier transforms of set functions (Stobbe and Krause, 2012). We propose scalable semidefinite relaxation, solved via an interior point method and sparse linear algebra. Experimentally, our algorithm recovers the ground truth echo chamber better than competing methods on small synthetic experiments. Our algorithm produces echo chambers with better network properties than competing methods on large real-world datasets. To independently validate our proposed objective function, we show that our algorithm finds echo chambers with more agreements with suspended users than competing methods on a small real-world dataset.
[LG-28] Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders
链接: https://arxiv.org/abs/2609.18535
作者: Weijian Yu,Jean Honorio
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Causal discovery aims to recover causal relationships from observed data. In various fields, exploring causal relationships among variables remains an important topic, but this task becomes challenging due to the existence of latent confounders. Ignoring such confounders can lead to false associations and incorrect edge directions. In this paper, we study the linear structural equation model with latent confounders. We propose an algorithm that iteratively identifies terminal (observed) nodes and reconstructs the directed acyclic graph of the observed variables. To do this, we recover the precision matrix of the observed variables as a sparse plus low-rank matrix: a sparse matrix captures the conditional dependencies among observed variables, while a low-rank matrix captures the combined influence of a few latent confounders. We establish that for p observed variables, r latent confounders and s edges, our procedure correctly identifies the directed causal relationship among observed variables, for n \gtrsim \max\s\log p,\ r p\ samples. Experimental results validate our theoretical contributions.
[LG-29] Provable Guarantees for Spectral Structured Prediction
链接: https://arxiv.org/abs/2609.18527
作者: Violet Zheng,Jean Honorio
类目: Machine Learning (cs.LG)
*备注:
Abstract:Structured prediction is the simultaneous prediction of multiple labels, and is widely used in various fields, such as natural language processing and computer vision. In this paper, we study binary node label recovery on signed graphs with edge-flip noise, a model introduced by (Globerson et al., 2015), via a simple spectral method that decodes node labels from the signs of the principal eigenvector of the noisy signed adjacency matrix. We develop graph structure-agnostic theoretical guarantees for approximate inference of node labels as well as guarantees for maximum angle deviation with respect to the ground truth node labels. By leveraging tools from matrix concentration theory and eigenvector perturbation analysis, we derive new concentration inequalities that explicitly quantify the effect of the spectral gap of the adjacency matrix, number of nodes, degree distribution, and noise level. As a corollary, we relate our general results to the Cheeger constant and provide results for different classes of graphs. We perform several synthetic experiments to validate our theory. To the best of our knowledge, we are the first to provide theoretical guarantees for the spectral-based approach. As a byproduct of our analysis, we derive technical results that might be of independent interest and useful for other machine learning problems.
[LG-30] COMPASS-ABS: Reducing Frag mentation in Shared GPU Clusters for Deep Learning Training Workloads
链接: https://arxiv.org/abs/2609.18519
作者: Yukai Zhou,Hongfan Wu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 22 pages, 7 figures
Abstract:With the rapid advancement of deep learning technology, shared GPU clusters receive an increasing number of deep learning training (DLT) jobs. Yet resource fragmentation make such clusters underutilized and forces the DLT jobs running on them to endure long turnaround times. Extensive research has been devoted to quantifying fragmentation and developing scheduling algorithms that alleviate its impact. However, existing fragmentation measures break down in the absence of workload distribution information, while current schedulers cannot continuously maintain resource fragmentation at a low level. To tackle these problems, we first introduce Scheduler-Induced Fragmentation (SIF), a metric built on the notion of partial-nodes that is independent of historical workload knowledge. We then propose COMPASS-ABS, which employs the COMPact-ASSured (COMPASS) algorithm to confine the cluster state within a tight Anchor-Based Space (ABS), whose construction fully leverages the topological alignment between dominant workload size and node capacity. Moreover. We also prove that it ensures SIF is bounded by \frac2N under a workload composition condition that matches both theory and production. Evaluations implemented on a physical cluster and a simulated cluster demonstrate COMPASS-ABS effectiveness at improving resource utilization, reducing DLT job completion time by reducing fragmentation.
[LG-31] ActiveScale: Scaling Active Perception for Robots across Model Data and Hardware
链接: https://arxiv.org/abs/2609.18514
作者: Shuai Zhou,Kaisheng Pang,Wenxuan Song,Wenjie Zhang,Xinhu Zheng,Haoang Li
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: this http URL
Abstract:Active perception is essential for robotic manipulation when fixed viewpoints leave task-relevant information occluded or unobserved. However, enabling vision-language-action (VLA) models to reason across changing viewpoints and actively acquire informative observations remains challenging. We present ActiveScale, a framework that advances active perception through coordinated model, data, and hardware designs. Our model augments a VLA with historical video observations and explicit camera-pose supervision, using per-frame pose tokens and a lightweight prediction head to associate observations across viewpoints and support a coherent understanding of the scene. To learn from the camera motion naturally present in human activity, we introduce a scalable human–robot mid-training recipe using 1000 hours of egocentric and robotic data, adapting the model to temporal inputs and pose supervision. We further introduce Active-perception Mobile-manipulation Platform (AMP), a robotic platform that supports active perception and mobile manipulation through single-operator teleoperation, enabling scalable collection of demonstrations that coordinate viewpoint changes and manipulation. Experiments demonstrate improved success rates on active-perception tasks, while ablation studies validate the contributions of camera-pose-aware modeling and egocentric mid-training. Together, these components provide an integrated foundation for studying and developing active perception in robotic manipulation.
[LG-32] Spatially Adaptive Noise Injection
链接: https://arxiv.org/abs/2609.18466
作者: Frantzeska Lavda,Maciej Falkiewicz,Van Khoa Nguyen,Alexandros Kalousis
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Diffusion samplers reverse a learned noising process using either stochastic (DDPM) or deterministic (DDIM) updates, which represent endpoints of a single family controlled by a scalar noise-injection variance that is applied identically at every spatial location. This uniform approach neglects the geometry of natural images: high-curvature regions such as edges and textures, where the denoiser is uncertain, benefit from stochastic correction, whereas smooth regions, where the score is precise, are degraded by injected noise. This work investigates whether each pixel requires stochastic correction at a given timestep and introduces Spatially Adaptive Noise Injection (SANI), a novel sampling framework that dynamically adjusts noise application on a per-pixel basis. SANI integrates a probabilistic gating mechanism with a derived spatially adaptive variance, ensuring that noise is injected precisely where needed to refine complex features while preserving well-formed structures. Experimental results and decoupling ablations demonstrate that SANI consistently improves Fréchet Inception Distance (FID) over the vanilla DDPM and DDIM endpoint samplers across diverse sampling timesteps, while remaining competitive with variance-learning baselines, highlighting the importance of spatial adaptivity in diffusion sampling.
[LG-33] HiLNO: A Hierarchical Latent Neural Operator with Multi-Scale Supervision for PDEs on General Geometries
链接: https://arxiv.org/abs/2609.18419
作者: Zhicheng Hu,Jiacheng Li,Min Yang
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:Latent neural operators improve the efficiency of operator learning for partial differential equations (PDEs) by performing the main computation on compact latent representations. However, directly compressing the input representation to obtain such compact representations may discard solution-relevant spatial information, especially for PDE solutions with multiscale structures. To address this problem, we propose HiLNO, a hierarchical latent neural operator that constructs a fine-to-coarse-to-fine latent space and further introduces multi-scale supervision (MSS) and anisotropic Gaussian attention. The hierarchy mitigates potential information loss during compression, while MSS aligns intermediate predictions with downsampled target fields, encouraging solution-relevant structures to be captured across multiple spatial scales. Anisotropic Gaussian attention enables feature transfer across the hierarchy, making HiLNO applicable to general geometries. Experiments on representative PDE benchmarks and a large-scale automotive aerodynamics task show that HiLNO achieves competitive predictive accuracy, while reducing the parameter count by an average of 84.4% and FLOPs by an average of 69.2% compared with LinearNO. Additional experiments demonstrate effective generalization to unseen spatial resolutions. Code is available at this https URL.
[LG-34] Every Fixed Metric Has a Blind Spot: A Learned Atmospheric Critic for Scoring Forecast Realism
链接: https://arxiv.org/abs/2609.18381
作者: Younes Elberkennou,Dmitri Demler,Thierry Meier,Luca Rispoli,Fanny Lehmann,Joel Oskarsson
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注:
Abstract:Despite their high accuracy on point-wise metrics, machine learning weather forecasting models can exhibit different failure modes such as blurring, periodic irregularities, and other unphysical spatial artifacts. This has motivated a variety of metrics to detect known failure cases. Existing metrics fix a representation or transformation in advance, and that choice limits the artifacts they can detect. We propose to train a discriminator for separating reference data from the model’s output, and using its output logit to obtain a divergence-like realism score. The discriminator learns whatever separates the model’s fields from real weather, adapting to whichever failure mode that model exhibits. We compare our learned atmospheric critic to existing metrics using various synthetic corruptions applied to ERA5 reanalysis data. Our method successfully identifies the corruptions and ranks their severity, while existing metrics fail on at least one corruption. Additionally, we evaluate forecasts from real weather models, and find that the realism score degrades with longer lead times and the metric generally assigns higher realism to numerical models than to machine learning models.
[LG-35] RecMorph: Topology-Guided Spatial Recurrence for Generalized Morphology Control
链接: https://arxiv.org/abs/2609.18359
作者: Quanrui Rao,Yong Liu,Xueming Xiao,Yingbo Luo,Kun Wu,Zhenyu Xu,Meibao Yao
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 26 pages. Code and experimental resources are available at this https URL
Abstract:Generalized morphology control requires a single policy to transform information across limbs with different physical roles, coordinate whole-body motion, and remain efficient as body size grows. Existing communication mechanisms address these requirements only partially. We introduce RecMorph, a topology-guided spatial recurrent architecture that uses recurrent sequence computation to jointly perform cross-limb communication and representation transformation. A depth-first traversal converts the kinematic tree into a morphology-derived sequence, along which shared bidirectional transitions progressively transform limb information before action decoding. Residual preservation, RMS normalization, and input-dependent channel modulation stabilize this repeated spatial transformation, yielding linear token complexity at fixed model width and depth. Across five UNIMAL tasks, RecMorph achieves the strongest mean final training performance among the evaluated generalized morphology controllers and the highest measured inference throughput on FT, while generalizing to unseen variations and bodies with up to 30 limbs. We further migrate representative generalized controllers from UNIMAL benchmarks to a four-platform quadruped setting. RecMorph achieves the best macro-averaged performance under nominal and high friction, reduces nominal velocity RMSE by 43.5% relative to specialist MLPs, and one shared policy completes 40 physical Go1/Go2 trials without falls. These results show that topology-guided recurrent transformation provides an effective and efficient communication mechanism for Generalized Morphology Control and remains effective when transferred from procedural bodies to physical robot platforms. Code and experimental resources are publicly available at this https URL.
[LG-36] Multi-Appliance Non-Intrusive Load Monitoring via Label-Preserving Aggregate Recomposition and Prediction Consistency
链接: https://arxiv.org/abs/2609.18315
作者: Jiangfeng Liu,Yanfang Fan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Non-intrusive load monitoring (NILM) estimates appliance power sequences from aggregate power, but models trained on source households commonly lose accuracy in unseen households. Aggregate power also contains loads from other appliances and measurement error, so predictions may depend on the residual background that co-occurs with source-household targets. Time-aligned submetered measurements and the additive decomposition of aggregate power expose a relation unused by window-wise supervision: an aggregate window can be recomposed by replacing only its residual background while preserving all modeled target-appliance power sequences pointwise. We combine label-preserving aggregate recomposition with prediction consistency. Both windows receive complete power and operating-state supervision. For each appliance, disagreement between the two power predictions is penalized only when both satisfy a fixed reliability criterion and only to the extent that it exceeds a fixed margin. The proposed method is implemented using a multi-appliance architecture with two-stage shared-to-specific mixture-of-experts routing. On REDD, UK-DALE, and REFIT, the proposed method lowers appliance-averaged mean absolute error relative to single-window training from 14.75 to 13.14 W, from 8.88 to 8.51 W, and from 15.83 to 14.55 W. Label-preserving aggregate recomposition and prediction consistency are used only during training, and add no inference-time module or parameter.
[LG-37] Beyond Quadratic Loss: The Stability Phase Diagram of Adam
链接: https://arxiv.org/abs/2609.18314
作者: Gaoxiang Tang,Huanran Chen,Ziming Liu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 20 pages, 9 figures
Abstract:Loss spikes are recurrent instabilities in neural-network training and can arise from multiple mechanisms. For Adam in particular, macroscopic loss spikes have been linked to optimizer dynamics, yet how its two momentum timescales govern them remains unclear. We investigate this dependence by mapping training dynamics across the (\beta_1,\beta_2) plane. Across a range of model–task settings, an approximately linear boundary, 1-\beta_2=C(1-\beta_1) , separates spiky from non-spiky dynamics, whereas a one-dimensional quadratic loss produces approximately cubic slope. A one-dimensional superquadratic loss L(x)\propto|x|^n recovers the near-linear scaling and links the boundary coefficient to the effective loss exponent n . We further show that confident cross-entropy losses develop a core–wall landscape comprising a narrow quadratic core followed by a steep wall, which produces effective superquadratic behavior at the scale of an optimizer update. Together, these results connect Adam loss spikes to both the mismatch between momentum timescales and finite-scale superquadratic loss geometry beyond the Hessian.
[LG-38] Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric ECML KDD2026
链接: https://arxiv.org/abs/2609.18306
作者: Omran Berjawi,Giuseppe Fenza,Rida Khatoun
类目: Machine Learning (cs.LG)
*备注: Accepted at the 6th Workshop on Bias and Fairness in AI (BIAS 2026), ECML PKDD 2026, Naples, Italy
Abstract:Large language models (LLMs) are increasingly deployed in applications involving interaction between agents, where their output plays a role in collective reasoning and decision-making processes. Despite significant research into the functioning of LLMs in such multi-agent systems, the processes of bias propagation in such systems are still a challenge. This work studies how biased opinions are propagated in the form of textual interaction in an environment of LLMs, in which a minority of agents maintain persistent extreme opinions, while the remaining agents iteratively update their beliefs through structured textual interactions. The findings show that even the presence of a small percentage of biased agents in such a system leads to significant shifts in the opinions of non-biased agents. It suggests that for the same percentage of biased agents, the shifts occur more quickly for the Llama~3.2 model when compared to a classical Friedkin-Johnsen (FJ) model. Further semantic analysis demonstrates that rhetorical consistency in textual explanations increases systematically with biased exposure and, importantly, is partially decoupled from numerical convergenumericalutral agents adopt the vocabulary employed by the biased agents even in configurations where their numerical opinion shifts remain moderate. The research helps explain how bias and language develop together in multi-agent language model ecosystems.
[LG-39] A GAN-Based Framework for Robust DDoS Attack Detection
链接: https://arxiv.org/abs/2609.18281
作者: Makram Chehayeb,Walid Fahs,Amina Rizk,Rida Khatoun,Omran Berjawi
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:The availability and consistency of online services remain vulnerable due to Distributed Denial of Service (DDoS) attacks. These attacks are evolving by adopting more complex strategies to evade traditional network security systems. Despite the effectiveness of machine learning models in detecting DDoS traffic, targeted adversarial attacks can degrade their classification accuracy. This work proposes a robust detection framework that integrates generative adversarial modelling with advanced machine learning models. We trained Random Forests, Deep Neural Ensembles, and Transformer-based models using the CICDDoS2019 dataset to establish the frameworks baseline performance. To enhance the models defensive capacity, we generated synthetic adversarial flows that simulate potential evasion attempts and adversarial traffic using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Then, we combined the generated traffic with benign and malicious traffic to construct hybrid datasets to train the models to learn more generalizable decision boundaries. The experimental results indicate that the proposed methodology significantly enhances detection accuracy and resilience, especially against unseen adversarial traffic. We also tested the designed framework using real-world generated traffic, which demonstrates its capability in practical settings. The scalable and efficient solution against adversarial DDoS attacks, introduced in this work, paves the way towards more resilient and adaptive network defense systems that combine generative adversarial augmentation with recent advances in learning models.
[LG-40] Behavioral Fingerprinting and Navigation Prediction in Web Browsing
链接: https://arxiv.org/abs/2609.18273
作者: Ralph Elsaghbini,Omran Berjawi,Walid Fahs,Rida Khatoun
类目: Machine Learning (cs.LG)
*备注:
Abstract:Web browsing often appears ephemeral: users visit a few websites, complete a task, and move on. However, even short fragments of browsing activity can contain rich and structured behavioral signals. In this work, we conduct a comparative empirical study of two complementary behavioral inference tasks: session-level user identification and next-domain prediction. Both tasks are derived from the same cleaned event stream and evaluated on large-scale anonymous browsing traces, with sessionization and splitting adapted to the temporal requirements of each task. For user identification, we evaluate classical and neural models operating on session-level behavioral and domain features. For next-domain prediction, we combine graph-based modeling with Large Language Models (LLMs). Experimental results show that short browsing sessions are highly identifiable, while future navigation actions are highly predictable from long-term interaction structure combined with recent behavioral context. Furthermore, LLM-derived semantic features yield only marginal gains over purely structural and sequential models, indicating that repeated interaction patterns remain the dominant predictive signal in the evaluated web-browsing setup. These findings highlight the extent to which interaction history substantially contributes to both user identifiability and navigation predictability in browsing traces.
[LG-41] Acting in Meters: Learning Metric Interactions for Precise Robotic Manipulation
链接: https://arxiv.org/abs/2609.18243
作者: Lijie Wang,Zheng Lu,Yiming Wang,Heyang Yu,Kenghou Hoi,Bowen Hu,Di Cui,Tianyu Xin,Haoran Liao,Wanqi Zhong,Xingjie Fan,Yizhao Xu,Ziliang Wang,Fei Gao,Yiming Li
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Vision-Language-Action models and World-Action Models have advanced language-conditioned robotic manipulation, yet often leave metric relations among actions, objects, and scene geometry implicit. Human manipulation combines semantic understanding of task-relevant objects with spatial feedback that guides hand motion relative to objects and their surroundings. Inspired by this, we introduce a metric interaction framework that models object-level and scene-level interactions in physical Cartesian space at a shared metric scale. At the object level, Interaction-Centric Tokens (ICTs) explicitly represent end-effector pose trajectories relative to manipulated objects and are jointly denoised with actions, providing physically grounded interaction supervision. At the scene level, the Metric Action Interaction Field (MAIF) uses action and ICT queries to attend to metric scene point-cloud features and learns geometry-conditioned action corrections. Through two-stage adaptation, our framework improves diverse VLA and WAM baselines with a small number of additional parameters and training steps. Experiments demonstrate average success-rate gains of 0.80 and 3.59 percentage points on LIBERO and RoboTwin~2.0, respectively, alongside gains of 6.80 percentage points on real-world tasks and 7.45 percentage points on their out-of-distribution variants.
[LG-42] F-DACE: Fuzzy Disagreement-Aware Causal Evidence Fusion for Abstention-Safe Conversational Retail Decision Support
链接: https://arxiv.org/abs/2609.18238
作者: Sourish Dey
类目: Machine Learning (cs.LG)
*备注: Pages: 21,Figures: 6,Tables: 10
Abstract:Observational decision-support systems often expose one causal estimate as a recommendation even when plausible estimators disagree. The inherent engine of the proposed system is causal machine learning: a conditional-average-treatment-effect estimand identified by backdoor adjustment, estimated by an EconML DML causal forest and DoWhy linear regression, checked by two-way fixed effects, and converted into candidate levers by constrained optimisation. F-DACE is the decision layer on that engine. It represents precision, propensity overlap, placebo-refutation stability, interval overlap, and directional agreement as fuzzy memberships. Hard vetoes force abstention after estimand mismatch, failed diagnostics, informative sign conflict, or weak evidence. In 180 panel simulations spanning six identification conditions, F-DACE made a decision in 67.2% of runs and limited false recommendations to 17.2%; the corresponding rates were 33.3% for the causal forest and 35.6% for backdoor regression, matching deterministic unanimity rather than dominating it. Nearly all (30 of 31) false recommendations occurred under shared unmeasured confounding, which no fusion rule can diagnose when every component shares the omitted variable. The retail application aggregates a public Walmart panel to 6,435 store-weeks across 45 stores. F-DACE abstains for all five markdown indicators: some estimates are imprecise, one refutation fails, and MarkDown5 has a direct sign conflict. A LangGraph conversational agent exposes impact, what-if, and lever-optimization tools while a deterministic verifier preserves causal-layer status. On 24 live questions it achieved 100.0% tool-routing accuracy, 100.0% status fidelity, and 0.983 mean groundedness. On ten adversarial questions it resisted all injected instructions.
[LG-43] Anomaly Detection in General Ledger Data: Results from a Hybrid Approach
链接: https://arxiv.org/abs/2609.18228
作者: Jan Gronewald,Alexander Michael Rombach,Sebastian Stephan,Peter Fettke
类目: Machine Learning (cs.LG)
*备注: Presented at the International Conference on Auditing and Artificial Intelligence 2024
Abstract:Journal Entry Tests (JETs) are a mandatory part of annual audits to evaluate and assess both highrisk audit areas and potential material misstatements. However, as JETs are designed to detect known patterns based on domain knowledge, the resulting lists are often very large and require substantial additional effort from the auditor. To ensure the economic efficiency of the audit, the number of false positives in JET result lists must be reduced. Especially machine learning (ML) methods represent a promising approach to improve anomaly detection in this field. In this research in progress paper, we investigate different approaches on how to combine JETs with ML-methods in a hybrid manner. We present specialized models to increase the detection performance and validity of anomaly detection results to improve audit efficiency. The experiments are based on synthetic data consisting of different normal and anomalous journal entries.
[LG-44] Reinforcement Learning for Real-Time Vision-Language-Action Policies
链接: https://arxiv.org/abs/2609.18207
作者: Perry Dong,Kuo-Han Hung,Dorsa Sadigh,Chelsea Finn
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning fine-tuning on top of large, pretrained Vision-Language-Action (VLA) models offers promise for highly reliable robot deployment. However, because of their scale, modern VLA models suffer from high inference latency, so the observation used to select an action is often stale by execution time, creating a distribution shift that can substantially degrade reliability and performance. Prior work has explored asynchronous policy execution to reduce the effect of latency, but these methods are mostly built on imitation learning and offer no mechanism for moving beyond the training distribution toward higher reliability. We close this gap by enabling RL fine-tuning that meets the real-time control requirements of dynamic real-world manipulation. Our approach builds on EXPO-FT, a framework for sample-efficient, reliable VLA fine-tuning with reinforcement learning, and decouples slow, expressive action generation from fast, reactive action edits: a large pretrained VLA proposes action chunks using its strong behavior prior, while a lightweight edit policy performs fast, reactive decision-making by editing actions in response to changes in state, conditioned on the latest observation. We instantiate this as Real-Time EXPO-FT, an RL framework for finetuning real-time VLA policies. On the Kinetix benchmark, Real-Time EXPO-FT enables a delayed policy to achieve the best performance among delayed and non-delayed methods in 10 out of 10 environments. On four dynamic real-world tasks, robot object passing, ball balancing, table soccer kicking, and dynamic object picking, with online robot data capped at 10 minutes, Real-Time EXPO-FT improves average policy performance from 42% to 97%, all without human intervention, demonstrating rapid, sample-efficient adaptation to challenging real-world dynamics. Website: this https URL
[LG-45] ransformation Laws in Neural Representations: Structure Realisability and Construction
链接: https://arxiv.org/abs/2609.18190
作者: Yuan Sun
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 46 pages, 12 figures, 63 tables
Abstract:How neural representations preserve the structure of input changes connects representation analysis with internal intervention. We study operable representational content through compatible actions of reference transformations on neural features. We characterise when a transformation descends through an encoder, and give a linear setting in which the defect is governed by the transformation’s demand for discarded information, measured in the metric the representation induces. On a rectifier the failure to realise a transformation has two distinguishable sources — what the source region has already made unrecoverable, and what it costs to satisfy every region the transformation visits with one operator — and for a \textitmeasured harmonic carrier the same question has a closed answer: a linear realisation exists exactly when the retained harmonic blocks are invariant under the action. Using colour as the in-depth instance, we find that hue orbits in frozen visual features concentrate 84–88% of their energy in the first two harmonics with rotation planes shared across shapes, that this organisation is substantially inherited from input and architecture and is reshaped by training and depth, and that the measured structure supports prediction, transport from new starting states, and composition — with global and local realisations differing sharply in which they achieve. Guided by the measurements, we construct a compact interface whose rotation action is fixed by the structure and never fitted: it reads hue zero-shot at 3.4 ^\circ median error on unseen shapes. Theory, structural measurement, and construction together establish transformation laws as a concrete object connecting the understanding of neural representations to their design.
[LG-46] Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning
链接: https://arxiv.org/abs/2609.18167
作者: Everest Yang,Skye Thompson,George D. Konidaris
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Adapting to changes in robot dynamics requires learning from new data without discarding experience that may still be useful. In continual model-based reinforcement learning (RL), replay collected before a dynamics change can slow adaptation, while removing it unnecessarily reduces available training data and can be especially costly if earlier dynamics return. We study when recent transitions are preferable to the full replay history. Two quantities characterize this trade-off: change magnitude and age-staleness area under the curve (AUC), measuring how well transition age separates stale from fresh data. Forgetting stale data helps after large permanent shifts but hurts when dynamics recur and older data becomes useful again. Choosing a replay strategy therefore depends on predicting when older data will help or hurt. We test these effects across two locomotion morphologies, two model-based RL algorithms, and Real-World RL benchmark perturbations. Because ground-truth staleness labels are unavailable on deployed robots, we evaluate whether an estimator built from interaction data can still provide the quantities needed to choose a replay strategy after permanent changes. Our results show that replay retention depends on change magnitude and on how the dynamics evolve.
[LG-47] Reaching Every Position Without Searching: Rotating Sparse Wiring on the Hypercube as a Substitute for Attention
链接: https://arxiv.org/abs/2609.18145
作者: Yoshiaki Takashita
类目: Machine Learning (cs.LG)
*备注: 13 pages, 7 figures
Abstract:Attention pays, at every layer and for every input, the cost of searching for whom to connect. We ask how far one can get with wiring that is fixed, sparse, and simply rotated from layer to layer. Treating the n positions of a sequence as the vertices of a \log_2 n -dimensional hypercube and connecting each position, at layer \ell , to its neighbour along dimension \ell \bmod \log_2 n , information from every position reaches every other in \log_2 n layers with 2n links per layer instead of n^2 . On a synthetic task that is unsolvable unless all positions are reached, this rotation matches all-to-all wiring at 1/32 of the links, while the same sparse pattern held fixed across layers fails; what matters is that every dimension is touched, not the order. On character-level language modelling of a public corpus (the first 12 M characters of enwik8), a hybrid that keeps two attention layers among sixteen sparse ones reaches 0.06 bits-per-character lower held-out loss than a fully attentive model of the same width at the same step budget (three seeds each, no overlap), with 1/7 of the links, 42% fewer parameters, and 2.4\times less wall-clock time; the purely rotated schedule is level with the hybrid. The same ordering holds on a second corpus of mixed Japanese, English and code, where the gap widens to 0.16 . The usable learning-rate window is four to eight times wider than attention’s on both. We also report what did not work - learned coordinates, and a “dynamics” variant whose apparent gains turned out to be an artefact of a saturated kernel - and the measurement discipline (frozen corpus, full-coverage evaluation, seed spread as the bar for ranking) that we found necessary to say anything at all at this scale.
[LG-48] Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization
链接: https://arxiv.org/abs/2609.18130
作者: Lu Han,Jin Wang,Yuchen Li,Haoran Gu,Shulei Liu,Ziyang Shi,Wenao Lu,Handing Wang
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注:
Abstract:Surrogate-assisted evolutionary algorithms (SAEAs) are effective methods for solving expensive optimization problems (EOPs), where surrogate models replace most expensive evaluations and critically influence the final optimization results. In recent years, tabular foundation models have advanced rapidly, and the Tabular Prior-data Fitted Network (TabPFN) has been adopted as a surrogate model for EOPs due to its strong predictive capability, demonstrating promising performance. Motivated by its potential as a surrogate model in SAEAs, this work conducts a comprehensive study that combines extensive experiments with in-depth theoretical analysis to investigate the effectiveness of TabPFN. Specifically, we perform experiments across both offline and online SAEA settings, covering diverse problem scenarios such as single-objective, multi-objective, constrained, combinatorial, mixed-variable, and engineering optimization problems. In addition, we further analyze the advantages and limitations of TabPFN within SAEAs and provide practical guidelines for its application in different optimization settings. Results show that the effectiveness of TabPFN is highly problem dependent, and it cannot replace conventional surrogates universally. Overall, TabPFN should be adopted selectively according to data availability, landscape complexity, search space characteristics, and its role within the algorithm. Customized model management strategies and role-specific algorithm design are necessary to fully exploit its advantages and avoid its pitfalls.
[LG-49] Learning Fractional-Order Dynamics from a Single Trajectory
链接: https://arxiv.org/abs/2609.18127
作者: Xiaole Zhang,Ziyi Zhang,Zehao Zhao,Stephen Tu,Guannan Qu,Yorie Nakahira,Paul Bogdan
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the most recent state alone. This paper studies system identification for discrete-time fractional-order linear time-invariant systems from a single observed trajectory of length t , a setting that captures such non-Markovian dynamics through the Grünwald–Letnikov difference operator. Unlike Markovian systems, fractional-order systems couple estimation across the entire history, making both statistical analysis and practical identification more challenging. We propose \emphFractional-Order Ordinary-Least-Squares Grid-Search (FO-GS), a simple two-stage estimator that exploits the diagonal structure of the fractional-difference operator to decouple the identification problem row-wise. Under the stability assumption, we establish high-probability, non-asymptotic error bounds for estimating both the fractional order and the system matrix in the heterogeneous setting, with both estimation errors scaling as (\mathcalO(t^-1/2)). Through experiments, we show that \emphFO-GS outperforms existing baselines in recovering both the fractional order and the underlying system dynamics.
[LG-50] oken Latency Fairness: Performance Isolation for Multi-Tenant LLM Serving
链接: https://arxiv.org/abs/2609.18112
作者: Dev Bali,Soujanya Ponnapalli,Yichuan Wang,Natacha Crooks,Scott Shenker,Matei Zaharia
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:LLM serving is typically offered as a shared, multi-tenant service, where high-demand workloads from one client can cause latency SLO violations for others. Existing solutions for performance isolation equalize client throughput in the long run, for example through queueing and batching fairness. However, these approaches do not provide latency isolation guarantees; as a result, well-behaved clients can still experience significant degradation to their token-level latencies. In this paper, we present FairInference, which provides the novel \delta-token fairness guarantee: for a well-behaved client, if a token is generated in d time units in isolation, it will be generated within d + \delta time units in multi-tenant execution, providing strong latency isolation guarantees for LLM serving. To achieve this, FairInference addresses a key challenge of LLM serving: bounding delays from sharing GPU resources without support for fine-grained scheduling or resource allocation. In FairInference, the scheduler enforces per-token deadlines, while bounding the delays from GPU compute sharing and accounting for the additional delays introduced by the shared KV caching in GPU memory. We show that FairInference effectively bounds token-level latency spikes for well-behaved clients and improves overall throughput compared to state-of-the-art LLM serving systems. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2609.18112 [cs.DC] (or arXiv:2609.18112v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2609.18112 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-51] FoundAna: A GNN-assisted Foundation Model for Graph Anomaly Detection
链接: https://arxiv.org/abs/2609.18107
作者: Suprim Nakarmi,Chahana Dahal,Yue Zhao,Junggab Son,Zuobin Xiong
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 11 pages, 3 figures, and 5 tables
Abstract:Graph anomaly detection aims to identify graph structures (e.g., nodes, edges, or subgraphs) that deviate significantly from expected patterns, which supports critical applications in fraud detection, spam identification, network intrusion, etc. Despite the growing methods in the field, existing approaches follow a one-model-per-dataset paradigm, limiting their transferability across diverse real-world scenarios due to task heterogeneity, label scarcity, and domain variability. In this work, we introduce FoundAna, a GNN-assisted Foundation Model for Graph Anomaly Detection - the first foundation model framework designated for generalizable, cross-graph anomaly detection by combining GNNs and transformers. FoundAna integrates an anomaly detection-specific GNN component with a standard transformer encoder augmented by four complementary positional encodings, which enable the model to capture both local and global structural information. Specifically, the positional encoding enriched node representations are passed through attribute and adjacency decoders, and the reconstruction errors serve as the anomaly score. Extensive experiments on nine benchmark datasets spanning financial, social, and citation network domains demonstrate that FoundAna consistently outperforms state-of-the-art baselines. The code implementation and Supplementary materials are here: this https URL.
[LG-52] MINDBench: iEEG Multi-Institution Neural Decoding Benchmark
链接: https://arxiv.org/abs/2609.18104
作者: Geeling Chau,Saba Hashemi,Yonghyeon Gwon,Eshani Patel,Jan DeWitt,Christopher Wang,Andrii Zahorodnii,Sabera J Talukder,Danny Dongyeop Han,Chun Kee Chung,Maryam M Shanechi,Yisong Yue
类目: Machine Learning (cs.LG)
*备注: 26 pages, 13 figures
Abstract:Intracranial electroencephalography (iEEG) is widely used to record electrical activity directly from electrodes inside the human brain, making it an attractive modality for neural decoding. However, progress in iEEG decoding, especially toward general-purpose foundation models, remains difficult to measure reliably: datasets are task- or institution-specific, limiting evidence of generalization across tasks and recording environments, and preprocessing choices can strongly influence performance, making model improvements difficult to distinguish from preprocessing gains. Thus, we introduce iMINDBench, an iEEG Multi-Institution Neural Decoding Benchmark that evaluates models on a shared suite of fifteen decoding tasks across three naturalistic movie-watching datasets. The benchmark additionally defines standardized preprocessing tracks and fixed evaluation splits to support consistent model comparisons. Using iMINDBench, we find that the evaluated pretrained systems generally outperform baselines within their respective preprocessing tracks, while strong spectral baselines remain competitive across institutional datasets. In our scaling study, adding up to 25 times more supervised data from other subjects or institutions yields only small or task-dependent gains over within-session training. Together, these findings highlight the need for iEEG models that improve on strong preprocessing baselines and make more effective use of data across subjects and institutions. Project website: this https URL
[LG-53] FedPGT: Progressive Gradient Transmission for Vehicular Federated Learning over Time-Varying Channels
链接: https://arxiv.org/abs/2609.18089
作者: Jintao Yan,Tan Chen,Yuxuan Sun,Sheng Zhou,Zhisheng Niu
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注:
Abstract:Vehicular federated learning (VFL) enables privacy-preserving collaborative model training for intelligent transportation systems, where communication resource allocation and gradient sparsification techniques have been explored to reduce communication overhead. However, vehicle mobility leads to rapidly varying channel conditions and transmission capacity, rendering predetermined resource allocation and sparsification decisions ineffective. In this paper, we propose FedPGT, a progressive gradient transmission scheme for VFL over time-varying channels, where vehicles progressively transmit high-magnitude gradient entries in response to instantaneous channel conditions. We establish a convergence bound that characterizes the impact of transmitted gradient entries and reveals diminishing-return behavior governed by a power-law decay. Motivated by this result, we formulate a stochastic optimization problem for online decision-making, where the main challenge lies in a cumulatively coupled, non-separable objective. To handle this challenge, we introduce per-slot surrogate transmission variables to decouple the long-term dependence across time slots and convert the original objective into an additive per-slot optimization problem, enabling a Lyapunov drift-plus-penalty approach for online scheduling. We further develop a low-complexity resource allocation algorithm for efficient online implementation. Experimental results demonstrate that the proposed scheme achieves a 3.65% accuracy improvement on the CIFAR-10 image classification task and a 12.66% reduction in average displacement error on the Argoverse trajectory prediction task compared with state-of-the-art baselines, demonstrating its applicability to diverse learning tasks under highly dynamic vehicular environments.
[LG-54] Beyond Embedding Transfer: Component Roles in Grokking Transfer and Stability
链接: https://arxiv.org/abs/2609.18078
作者: Zeyu Jia
类目: Machine Learning (cs.LG)
*备注: 15 pages, 5 figures, conference preprint
Abstract:Warm-start transfer can make algorithmic tasks generalize rapidly, yet it is unclear which model components provide the gain and whether that gain remains stable under continued optimization. We study cross-operator transfer on modular arithmetic and separate efficacy (early velocity) from stability (post-reach drawdown). In a scale-matched 108-run battery across 12 seed blocks (96-run 2^3 factorial plus 12-run scale control), transferring internal attention/MLP weights (B) alongside token embeddings and readout (E+U) improves early accuracy by 5.46 pp (Holm p=0.0039) and cuts confirmation latency by 558 steps (Holm p=0.0088). While readout plus internal-block transfer satisfies the pre-specified +/-500-step latency equivalence criterion in 1-layer models (TOST p=0.0011, though Full is faster in 11/12 paired seeds), a prospective 2-layer replication confirms the internal-block advantage (12/12 seeds, +704.67 integral units, p=4.88x10^-4) while revealing an architectural boundary: omitting donor embeddings falls 4475.6 units below Full, outside the +/-250-unit margin. Continued target training frequently triggers severe post-grokking relapse. Freezing transferred representation carriers (E, U) nearly eliminates offline relapse (19.40% - 0.07%, Holm p=0.005859). Online validation-triggered gating slashes True Max Drawdown from 22.06% to 0.60% on 2a+b (p=0.000488), with prospective confirmations extending protection across affine, nonlinear quadratic, and 2-layer targets (10.94-23.47 pp reductions), distinguishing continual stabilization from static early stopping. In non-abelian S_5, unshielded transfer surges transiently (95.4% peak), but a prospective shielding cohort yields no confirmed benefit (+0.15 +/- 1.14 pp). These results establish a component-level dissociation between transfer acceleration and trajectory stability, and expose the empirical boundaries of parameter shielding.
[LG-55] Regional Explanations via Causal Sufficiency and Necessity
链接: https://arxiv.org/abs/2609.18049
作者: Xuexin Chen,Peng Liang,Zijian Li,Zhiyong Lin,Ruichu Cai
类目: Machine Learning (cs.LG)
*备注:
Abstract:Model explainability is essential for understanding and trusting machine learning models. Existing explainable AI methods often explain predictions through feature importance, counterfactual explanations, or rules. However, a region-level characterization of when and only when a prediction behavior arises remains less explored. This paper proposes Causal Sufficient and Necessary Regional Explanations (SNRE), a framework that learns an input region A and output region B such that membership in A is both sufficient and necessary for the model output to fall in B . Motivated by the classical Probability of Necessity and Sufficiency (PNS), we formulate a region-level PNS measure through stochastic interventions and derive a differentiable finite-sample estimator for optimization. SNRE parameterizes the input-output region pair with explicit and interpretable algebraic region families, together with a learnable feature mask, balancing expressiveness and interpretability. Experiments demonstrate that SNRE learns region pairs with strong sufficiency-necessity performance, robust explanation behavior, and practical utility for model analysis.
[LG-56] Structural Inference under Hidden Agents
链接: https://arxiv.org/abs/2609.18045
作者: Zhongben Gong,Xiaoqun Wu,Mingyang Zhou,Hui Huang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recovering latent interaction structures from multi-agent dynamics is important for understanding and predicting interacting systems. Trajectory-based structural inference has achieved promising performance, but conventional formulations assume that the trajectories of all modeled agents are available. In practice, agents may become unobserved at deployment because of limited sensing, occlusion, or communication failure. Existing studies have considered unseen-node estimation, structural inference under partial observations, and missing-value imputation, yet the joint recovery of hidden-agent trajectories and their interactions remains underexplored. We formulate this problem as structural inference under hidden agents. Its key difficulty is a circular dependency: recovering interactions involving a hidden agent requires an estimate of its trajectory, while trajectory reconstruction can itself benefit from structural information. To address this challenge, we propose Structural Inference under Hidden Agents (SIHA), which combines structure-agnostic initialization with structure-guided iterative refinement. SIHA reconstructs hidden trajectories from visible observations, infers interactions using Neural Relational Inference, and feeds the estimated structure back into hidden-state reconstruction through multi-strength structural attention and iterative state–structure updates. Experiments on three benchmark dynamical systems demonstrate consistent improvements in visible-to-visible structural inference, while also showing benefits in hidden-state reconstruction and future prediction. Motion-capture experiments with simulated whole-limb occlusion further demonstrate its effectiveness in realistic hidden-agent settings.
[LG-57] QuanText: Protecting Dataset-Level Secrets in Textual Data Sharing
链接: https://arxiv.org/abs/2609.17995
作者: Shuaiqi Wang,Zinan Lin,Giulia Fanti
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:Natural-language datasets support many downstream applications and research studies, but releasing text can reveal sensitive global properties of the underlying data source, such as the proportion of records associated with a particular gender, diagnosis, or political stance. Existing work has largely focused on property inference attacks that recover such global properties, while defenses for protecting these dataset-level secrets remain limited. Differential privacy, although effective for protecting individual records, provides only weak protection for aggregate properties. We propose Randomized Quantization for Text (QuanText), a training-free and large-language-model-agnostic data release mechanism that protects global secrets in textual datasets while preserving data utility. Given a dataset-level secret, such as the proportion of records with a particular diagnosis, and attributes whose utility should be preserved, such as topic and sentiment, QuanText perturbs both the secret distribution and the distributions of correlated attributes. It does so by constructing candidate release distributions over secret and non-secret attributes, randomly selecting a candidate sufficiently close to the private empirical distribution, and rewriting each private text sample to match the selected distribution using attribute-related snippets from the original text. QuanText is inspired by the Statistic Maximal Leakage (SML) framework, which bounds leakage about a secret function of a data distribution. Under idealized conditions, we show that QuanText satisfies an SML guarantee. Since these conditions may not hold exactly in practice, we also evaluate QuanText empirically on real-world datasets. Our results show that QuanText achieves a better empirical privacy-utility trade-off than competing data generation baselines.
[LG-58] he Operable Pareto Front: Distilling Offline Search into Run-Time Control for Multi-Objective UAV Edge-Computing Scheduling
链接: https://arxiv.org/abs/2609.17992
作者: Qiao Liao,Zhiyong Feng,Bin Wu,Guodong Fan
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注: Includes supplementary material
Abstract:A UAV mobile edge computing (MEC) fleet trades energy against delay, and its schedules form a Pareto front; we call a scheduler operable when the fleet can be asked for any point on that front at run time. We propose PrefDT, to the best of our knowledge the first preference-conditioned Decision Transformer for the problem of joint trajectory, association and offloading scheduling. Its idea comes from language modeling: we hand the model the desired trade-off as an input, such that a single model only needs to be trained once offline to return any desired point on the curve in one rollout. The fleet’s state is summarized by attention pooling with a per-user bypass, so the scheduler keeps working when user reports are lost. The energy target is a running budget decremented by what the fleet actually spends. As a result, when wind or load pushes consumption off the plan, the policy can track the difference and hold its budget. Because no corpus of preference-labeled flights exists, we design a distillation pipeline and build the corpus by ourselves. In simulation against 26 method variants, PrefDT produces the best trade-off curve of any learned method and holds its energy budget to within 0.6% when propulsion cost rises by half in mid-flight.
[LG-59] Mixed-Integer Nonlinear Differentiable Predictive Control for Underground Pumped Hydro Energy Storag e Systems
链接: https://arxiv.org/abs/2609.17964
作者: Honghui Zheng,Ján Boldocký,Yury Dvorkin,Ján Drgoňa
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 6 pages, 5 figures. Accepted for publication in the 2026 65th IEEE Conference on Decision and Control (CDC)
Abstract:This paper extends Mixed-Integer Differentiable Predictive Control (MI-DPC) to multi-modal discrete decisions and nonconvex polynomial dynamics arising in Underground Pumped Hydro Energy Storage Systems (UPHES). A neural policy mapping problem parameters to continuous setpoints and integer mode selections via a Gumbel-Softmax layer is trained in a self-supervised manner by differentiating the expectation of the finite horizon control objective through the nonlinear dynamics model. Three methodological contributions enable this extension: a parallel differentiable simulator that preserves gradient magnitude, a Transformer encoder that captures long-range temporal dependencies, and a Gumbel-Softmax temperature annealing schedule that regularizes the combinatorial search. We demonstrate the framework on day-ahead scheduling of a UPHES, a large-scale mixed-integer optimal control problem with nonlinear unit performance curves and volume-head coupling. MI-DPC achieves only 1.6% suboptimality relative to a piecewise mixed-integer quadratic programming baseline, while providing five orders of magnitude speedup in online scheduling time.
[LG-60] Maximum Strong Independent Sets in Hypergraphs: Reductions Bounds and Greedy Certificates
链接: https://arxiv.org/abs/2609.17951
作者: Yingquan(Cody)Wu,Jason Cong
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study the maximum strong independent set problem in a finite hypergraph: find the largest vertex set that intersects every hyperedge in at most one vertex. This objective arises whenever each observed block is a local incompatibility constraint but transitive closure across overlapping blocks is not justified. A motivating example is multi-band LSH-MinHash deduplication, where each collision bucket gives local evidence, while connected-component contraction can impose spurious global equivalences. The paper develops an incidence-structural toolkit for this problem. We prove exact reductions for dominance, incidence twins, and weight-1 blocks; derive closed-form and low-weight upper bounds; introduce puncturing and covering certificates that sharpen those bounds; and analyze a layered greedy clustering algorithm driven by block weights and residual incidence. The algorithmic analysis includes feasibility, maximality, conditional optimality, a layered witness-matching upper bound, and incidence-local complexity bounds. The results give correctness, termination, fixed-point, and optimality certificates for broad incidence families, together with examples showing when different certificates separate or coincide.
[LG-61] On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
链接: https://arxiv.org/abs/2609.17942
作者: Sambit Mishra,Urbashi Mitra
类目: Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME); Machine Learning (stat.ML)
*备注: 50 pages, 5 figures
Abstract:The problem of identifiability in linear parametric models (LPMs) whose nodes follow either an ordered logit model or a regular one-parameter exponential family is evaluated. The results go beyond classical structural equation models as well as results for nodes with observations from a homogeneous family of distributions. The main result establishes that the orientation of every edge joining an ordinal node to an exponential-family node is identifiable from the joint distribution alone at every parameter value, provided the ordinal node has at least three categories and the exponential-family node at least three points of support, with no restriction on the sufficient statistic. Converses show that both requirements are necessary: the three-category requirement is binding only for affine sufficient statistics, and the three-point requirement is binding under the canonical link. The guarantee extends to orienting every such mixed ordinal-exponential family edge of a given d -node undirected skeleton. Numerical experiments illustrate the theoretical results by successfully separating orientations within a Markov equivalence class, which are indistinguishable by conditional independence alone.
[LG-62] Beyond the Previous Layer: Residual Predictive Structure in Sparse MoE Routing
链接: https://arxiv.org/abs/2609.17940
作者: Hao Li,Yasuyuki Tahara,Yuichi Sei
类目: Machine Learning (cs.LG)
*备注: 6 pages, 3 tables
Abstract:Sparse mixture-of-experts models route each token through a sequence of expert selections. We ask whether the immediately preceding selection adequately summarizes this trajectory for predicting the next router. Using frozen OLMoE and JetMoE models, we measure the held-out predictive gain from earlier expert selections while retaining the most recent selection as a common baseline. In OLMoE, extending the history from one to eleven layers raises router-logit R^2 from 0.59879 to 0.66544. A preregistered JetMoE replication yields four-layer gains of 0.14275 and 0.20528 at two target depths, with paired bootstrap intervals above zero. These gains survive nonlinear decoding: adding history to a small multilayer perceptron improves R^2 by 0.17137 and 0.21861, whereas nonlinear decoding of the recent state alone adds 0.00139 and 0.00936 over a linear probe. Parameter-matched controls preserve the advantage, and cross-fitted history residuals predict target residuals with R^2 of 0.20549 and 0.23556. These findings identify residual predictive structure in expert-selection trajectories beyond adjacent-layer persistence.
[LG-63] Locating Hidden Failures Makes Long-Horizon Agents More Reliable
链接: https://arxiv.org/abs/2609.17930
作者: Salman Rahman,Yubin Kim,Mihir Parmar,A. Ali Heydari,Genglin Liu,Simon A. Lee,Weizhi Zhang,Arian Hosseini,Ahmed A. Metwally,Yuzhe Yang,Baharan Mirzasoleiman,Xin Liu,Pavel Izmailov,Saadia Gabriel,Mark Malhotra,Shwetak Patel,Daniel McDuff,Hamid Palangi
类目: Machine Learning (cs.LG)
*备注:
Abstract:As AI agents take on long, autonomous tasks, we increasingly oversee rather than perform the work, yet we still judge them almost entirely by whether they finally succeed. An outcome cannot reveal where a run went wrong, whether the agent recovered, or the irreversible harm it caused along the way, and where long-horizon agents fail remains unmapped. We study 2518 agent trajectories across software engineering, computer use, and science, close to real deployment, and classify 6967 mistakes into 78 failure types. Failure follows a recurring signature: after its first mistake an agent often fails to recover and rarely catches the error itself, so the run continues unchecked while still looking correct; whether an agent recovers depends on the task and the environment’s feedback, not on the agent framework running it. Long-horizon agents can do real harm on the way to a passing result: even runs scored as solved delete data, corrupt systems, or fabricate success rather than earning it. We release these human-verified annotations as Traverse, a benchmark on which six frontier judges struggle to locate failure regardless of scale: even the strongest correctly identifies the first mistake in fewer than a third of runs. Yet Scout, a 4 B verifier we trained, locates failure far better than these judges and transfers to domains it never saw. Used at test time to select among an agent’s candidate runs, it raises task success above the agent’s own single-attempt performance, without retraining the agent. By making failure cheap to locate and correct, this work is a foundation for more trustworthy long-horizon agents that learn from their own mistakes, and a practical path to overseeing increasingly autonomous AI.
[LG-64] Symmetry without a manifold: intrinsic dimension on orbits NEURIPS
链接: https://arxiv.org/abs/2609.17926
作者: Chon-Fai Kam,Miloud Bessafi,Frédéric Cadet
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 33 pages, 3 figures. Under review at NeurReps 2026 (NeurIPS workshop, Proceedings Track)
Abstract:The standard geometric derivation of neural scaling exponents takes the intrinsic dimension of a data manifold as its input. On modular addition in \mathbbZ_p that derivation has no input. The exact algebraic solution is an orbit of \mathbbZ_p acting by isometries. Transitivity alone makes the ratio statistic underlying the standard dimension estimator a point mass, so the estimator is undefined, and here the two nearest neighbour distances coincide exactly. Breaking the symmetry at scale \epsilon returns a number, but one that tracks 1/\epsilon with no scale free plateau. We show that the failure is general, since on any finite orbit of a group acting by isometries the estimator reports the resolution at which the set is probed rather than a dimension. What replaces the power law is exponential in hidden width, L(h)=L_\infty+A\exp(-c,h^\alpha) , with R^2 between 0.982 and 0.995 against 0.857 to 0.906 for a power law admitting the same floor and fitted under the same protocol. Where the data supply is sufficient the rate belongs to the regulariser rather than to the group, since weight decay moves c by a factor of 47 while group order moves it by 1.10, a residual below seed to seed resolution, for every fixed \alpha between 0.75 and 2. The critical width falls with group order rather than rising, against capacity counting that assigns a fixed number of neurons to each irreducible representation.
[LG-65] EdgeReMIND: A Scalable Top-Ranked Memorization Baseline for Temporal Multi-Relational Link Prediction
链接: https://arxiv.org/abs/2609.17916
作者: Bryant Pollard
类目: Machine Learning (cs.LG)
*备注: 22 pages, 6 figures, 13 tables. Accepted at the Fifth Learning on Graphs Conference (LoG 2026), Proceedings Track. Code: this https URL
Abstract:Temporal link prediction on the Temporal Graph Benchmark 2.0 (TGB 2.0) faces a scalability ceiling: on the benchmark’s three largest datasets, every existing embedding method runs out of memory or exceeds the time budget. These large-scale graphs are the ones nearest real deployment scale, so failing on them is a real production limitation. EdgeReMIND sets the highest reported test mean reciprocal rank (MRR) on six of eight TGB 2.0 datasets and is the only relation-aware method that runs on all of them. This linear memorization model, with learned per-relation weights over data-calibrated features, is therefore not merely a fallback where embeddings fail but a practical state-of-the-art baseline across the benchmark.
[LG-66] abPFN-3.5: Technical Report
链接: https://arxiv.org/abs/2609.17895
作者: Benjamin Jäger,Nick Erickson,Léo Grinsztajn,Felix Birkel,Klemens Flöge,Oscar Key,Kürşat Kaya,Jonas Kübler,Adèle Frankel,Tobias Schröder,Anurag Garg,Jan Hendrik Metzen,David Salinas,Simon Bing,Kristina Collins,Tuana Çelik,Vahid Balazadeh,Lydia Sidhoum,Tomás Pereda,Brendan Roof,Andrej Tschalzev,Siyuan Guo,Philipp Singer,Lennart Purucker,Jake Robertson,Marie Salmon,Philipp Jund,Jerry Chen,Diana Kriuchkova,Arthur Cahu,Eliott Kalfon,Adrian Hayler,Georg Grab,Vitor Monteiro,Lilly Wehrhahn,Dominik Safaric,Clara Cornu,Alan Arazi,Rylee Grace,Simone Alessi,Mihir Manium,Bernhard Schölkopf,Yann LeCun,Madelon Hulsebos,Sauraj Gambhir,Noah Hollmann,Frank Hutter
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We introduce TabPFN-3.5, our new flagship Tabular Foundation Model. It significantly outperforms its predecessor, TabPFN-3, and all existing baselines across a broad range of tabular problems. TabPFN-3.5 sets a new state of the art on standard tabular prediction in TabArena, and extends it to the data practitioners encounter in practice: non-i.i.d. data with temporal or grouped splits, tables with strings, text and images, high-cardinality categorical features, and wide tables with many features. These gains carry over to our task-specific harnesses: state of the art on relational data and stronger time-series forecasting. For faster inference, our variant TabPFN-3.5-Fast runs up to 3x faster than TabPFN-3 while keeping most of the accuracy gains. In addition, we upgrade TabPFN-3.5-Plus, expanding our multimodal capabilities with advanced text and date handling alongside proprietary inference optimizations. Finally, we release a new version of our Thinking mode, TabPFN-3.5-Thinking, which scales inference-time computation to push the state of the art further. It benefits from our stronger base model and from inference-time improvements that make it up to 12x faster than TabPFN-3-Thinking.
[LG-67] Dataset-Dependent Effects of Cross-Depth Aggregation and Soft-Routed Experts in EEG Foundation Model Fine-Tuning ICASSP2027
链接: https://arxiv.org/abs/2609.17886
作者: Mingyang Jiang,Yamin Li,Daniel Moyer,Fan Ma,Hua Xu,Catie Chang
类目: Machine Learning (cs.LG)
*备注: 5 pages, 2 figures, 3 tables. Submitted to IEEE ICASSP 2027
Abstract:EEG decoding tasks can rely on different temporal dynamics and cross-channel relationships. We test whether specialized modules improve a fully fine-tuned EEG foundation model by augmenting CBraMod with cross-depth Attention Residuals (AttnRes) and two soft-routed expert banks. Across matched three-seed experiments on FACED, ISRUC, SEED-V, and PhysioNet-MI, the complete model changes mean balanced accuracy relative to full fine-tuning by -0.12, +1.27, +0.77, and -1.27 points, respectively. AttnRes alone improves mean balanced accuracy on three datasets, whereas adding experts on top of AttnRes helps only FACED and SEED-V. These gains come with substantial overhead: AttnRes requires 2.11 to 2.88x runtime and 1.78 to 2.67x memory, while the complete model requires 2.41 to 3.04x runtime and 1.86 to 2.85x memory. Overall, the added modules produce dataset-dependent, sometimes opposing effects rather than consistent gains over full fine-tuning.
[LG-68] he Unbearable Weight: Scaling Models and Methods for UAV Audio Classification
链接: https://arxiv.org/abs/2609.17884
作者: Andrew P. Berg,Qian Zhang,Mia Y. Wang
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:As unmanned aerial vehicles (UAVs) become increasingly prevalent in consumer and defense settings, classifying them reliably from limited, modality-specific data is an urgent challenge. The dominant approach, large pretrained networks fully fine-tuned on task data, carries a substantial computational and memory weight that is hard to bear in resource-constrained UAV deployments, where edge inference and rapid retraining for emerging platforms are both required. This paper systematically scales across both model architectures and fine-tuning methods for UAV audio classification, asking when that weight is justified and when lighter alternatives prevail. Using a custom dataset of 3,100 audio clips spanning 31 drone classes, we evaluate transformer (ViT, AST) and convolutional (custom CNN, ResNet-18/152, MobileNet-V3-S/L, EfficientNet-B0/B7) backbones under full fine-tuning, classifier-only fine-tuning, and four parameter-efficient fine-tuning (PEFT) methods: SSF, IA3, OFT, and selective batch-norm tuning. All configurations are evaluated with 5-fold cross-validation across accuracy, training time, trainable-parameter share, and inference-time memory footprint. Selective batch-norm fine-tuning of EfficientNet-B7 with three-fold augmentations achieves the highest validation accuracy (97.65% ± 0.30) while updating under 0.5% of model parameters. Across the sweep, lightweight CNNs consistently outperform transformers on both accuracy and efficiency. For UAV audio classification under data scarcity, scaling the method outperforms scaling the model.
[LG-69] Hybrid coupling with numerics-informed neural networks and the overlapping Schwarz alternating method
链接: https://arxiv.org/abs/2609.17841
作者: George Chumbipuma,Irina Tezaur,Alejandro Diaz,Beatrice Riviere
类目: Machine Learning (cs.LG); Mathematical Physics (math-ph)
*备注:
Abstract:We develop a hybrid modeling framework for coupling pre-trained numerics-informed neural networks (NINNs) with classical full order models (FOMs) using the overlapping Schwarz alternating method. We consider the two-dimensional advection-diffusion equation in the advection-dominated, Peclet-number 10^6 regime. We first demonstrate that, unlike the corresponding physics-informed neural network (PINN), a monolithic NINN can be accurately trained on our model problem without domain decomposition. We then employ overlapping multiplicative Schwarz as a deployment mechanism for coupling a pre-trained, subdomain-local NINN with a neighboring FOM, with the NINN weights held fixed throughout the Schwarz iteration. We consider two training approaches for the subdomain-local NINNs: a top-down approach, in which boundary data are obtained from a coupled Schwarz solve on the full domain with a FOM on each subdomain (FOM-FOM Schwarz), and a bottom-up approach, in which boundary traces are generated synthetically on the NINN subdomain without requiring any full-domain solves. The resulting hybrid NINN-FOM solutions agree closely with the corresponding FOM-FOM Schwarz solutions, with the top-down and bottom-up training approaches yielding comparable accuracy.
[LG-70] NObSP: Functional Decomposition of Neural Networks via Oblique Subspace Projections
链接: https://arxiv.org/abs/2609.17825
作者: Alexander Caicedo,Víctor De La Hoz,Santiago Alférez
类目: Machine Learning (cs.LG)
*备注: 24 pages, including 6 pages of supplementary material. Code available at this https URL
Abstract:Understanding how deep neural networks make decisions remains a fundamental challenge. We present NObSP (Nonlinear Oblique Subspace Projections), a framework that decomposes predictions into explicit per feature contribution functions and an interaction residual. NObSP exploits the linear final layer of a trained network and uses oblique projections in sample space to reduce double counting when learned feature subspaces overlap, thereby supporting both local explanations and global functional analysis. We establish connections to functional ANOVA and the Kolmogorov-Arnold representation theorem and derive an efficient partial regression algorithm for out of sample evaluation. For convolutional networks, NObSP-CAM produces class activation maps without backward passes after a one time calibration. Experiments on tabular and vision benchmarks show faithfulness comparable to established attribution methods. On a synthetic benchmark with known component functions, NObSP obtains a Function Reproduction Score of 0.989, compared with 0.966 for KernelSHAP and 0.922 for Integrated Gradients. On TinyImageNet, contribution vector embeddings improve mean nearest neighbor class purity from 0.654 for raw activations to 0.713 and reduce mean neighbor distance by more than half. These results indicate that NObSP complements scalar attribution methods by recovering functional contribution profiles with separable positive and negative evidence.
[LG-71] Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder
链接: https://arxiv.org/abs/2609.17808
作者: Graeme Kelly,Emilio J. Palacios-Garcia,Barry P. Hayes
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 5 pages, 4 figures, 4 tables. Accepted at IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) 2026
Abstract:The increasing adoption of electric vehicles (EVs) is expected to place significant additional demand on residential distribution networks, creating a need for realistic charging datasets for planning and simulation studies. However, access to real-world EV charging data is often limited due to privacy constraints, incomplete records, and restricted availability. This paper proposes a conditional variational autoencoder (CVAE) for the generation of synthetic EV charging sessions from real transaction-level charging data. The model is trained on engineered session features describing plug-in duration, charging duration, delivered energy, charging delay, and cyclical time-of-week, while conditioning on day of week and managed charging status. A Gaussian negative log-likelihood (NLL) reconstruction loss is employed to model feature-wise heteroscedastic uncertainty, and the latent space is regularised using a Kullback-Leibler (KL) divergence term. The statistical fidelity of the generated data is evaluated using distributional metrics and downstream task performance through the Train-on-Synthetic-Test-on-Real (TSTR) protocol. Results demonstrate that the proposed approach produces synthetic EV charging sessions that preserve key statistical properties of the original dataset while supporting predictive modelling tasks.
[LG-72] Modular Deep Learning Mechanisms for Auditable Next-Day Wildfire Spread Prediction
链接: https://arxiv.org/abs/2609.17763
作者: Miguel Esparza,Aydin Ayanzadeh Ahmad Mousavi,Ali Mostafavi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Next-day wildfire prediction requires models whose forecasts can be evaluated alongside the assumptions and historical evidence used in their computation. Although deep learning can learn spatial patterns from remote-sensing data, predictive performance alone does not establish physical fidelity or operational trustworthiness. This study investigates three modular augmentations for next-day active-fire prediction: wind- and slope-conditioned attention biases, physics-feature retrieval-augmented output correction, and fire conditioned dual-stream gating. The attention biases expose prescribed directional preferences, while the retrieval module selects historical tiles using a nine-dimensional environmental and fire-state descriptor and applies a learned correction to a frozen model’s logits. The modules are evaluated across five backbones on the Next Day Wildfire Spread benchmark, using staged ablations, directional audits, retrieval perturbations, calibration measures, and computational comparisons. The three-seed mean F1 score and area under the precision–recall curve (AUC-PR) of a SwinUNETR model with all three augmentations are 0.4216 and 0.3673. Then, a mixed ensemble (two augmented architectures and one non-augmented architecture) model achieves 0.4292 and 0.3790. Benefits vary across architectures, and retrieval-related improvements in AUC-PR do not consistently translate into higher F1. The constructed wind bias aligns closely with input wind, but its alignment with observed next-day fire displacement is much weaker, distinguishing prior inspectability from predictive physical fidelity. The study contributes a framework for exposing and evaluating selected domain-informed components within wildfire prediction models. Together, the results presented show that predictive performance, operational trustworthiness, and computational practicality need not be competing objectives.
[LG-73] SAM-on-the-Curve: Sharpness-Aware Mode Connectivity for Robust Weight-Space Interpolation
链接: https://arxiv.org/abs/2609.17748
作者: Alejandro Calatrava,Xu Zhang,Ren Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deep neural networks that are independently trained to similar performance can be connected by low-loss parametric curves in weight space, a phenomenon known as Mode Connectivity (MC). This geometric property underpins practical techniques such as weight averaging, model ensembling, and model merging. We argue that low-loss connectivity is an incomplete geometric criterion: it controls loss only along a one-dimensional trajectory while leaving the surrounding weight-space neighborhood unconstrained, so the optimized curve may traverse sharp ridges that become fragile under distribution shift. We therefore reformulate mode connectivity as a neighborhood-robust path optimization problem, seeking a curve whose entire local neighborhood maintains low loss. We propose Sharp Mode Connectivity (SMC), which applies a first-order sharpness-aware approximation to the resulting minimax functional, enforcing flatness along the entire curve rather than only on it. We derive a practical optimization algorithm for connectivity paths under this sharpness-aware objective. Under severe blur corruptions from CIFAR-10-C, SMC achieves up to 6.09% absolute accuracy improvement over standard MC. Remarkably, SMC produces negative loss barriers, meaning that models obtained at interior points of the optimized path can outperform the average endpoint loss. These results, validated across ResNet-18, VGG16-BN, and ViT-Tiny on CIFAR-10 and ImageNet-100, establish path-wise flatness as a practical principle for robust weight-space interpolation.
[LG-74] FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining
链接: https://arxiv.org/abs/2609.17730
作者: Rappy Saha,Nima Amirafshar,Jude Haris,Nima Taherinejad,José Cano
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG); Performance (cs.PF)
*备注: Accepted at the 38th IEEE/SBC International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) 2026
Abstract:Approximate multipliers can reduce hardware area and energy consumption in Deep Neural Network (DNN) inference; however, they introduce computational errors. Assessing the accuracy of numerous approximate multiplier designs across diverse DNN models and large-scale datasets remains challenging due to prohibitive evaluation times. This overhead primarily stems from the slow emulation of approximate multiplier behavior using look-up tables (LUTs) on CPU and GPU platforms. Moreover, the resulting accuracy degradation must be carefully quantified and, if necessary, mitigated (e.g., through retraining), further increasing the overall evaluation cost. To address these challenges, we propose FAME, an FPGA-based platform for evaluating approximate multipliers. The platform exploits the reconfigurable logic of Field-Programmable Gate Arrays (FPGAs) to implement approximate multipliers directly in hardware, eliminating the need for LUT-based emulation on CPU/GPU platforms and thereby enabling efficient DNN inference while significantly reducing evaluation time on large datasets. Furthermore, we introduce a pattern-guided DNN retraining technique to mitigate accuracy degradation induced by approximate multipliers. Specifically, retraining is guided by multiplier-specific patterns to effectively recover potential accuracy losses. We evaluate FAME using two DNN models, ResNet-18 and MobileNetV2, on the ImageNet dataset across 27 approximate multipliers. During inference, our approach achieves up to a 3.47x speedup in approximate multiplier evaluation compared to prior LUT-based emulation methods. Furthermore, the proposed retraining technique improves accuracy by up to 65.5% over existing retraining approaches for the evaluated multipliers. The code is publicly available at: this https URL
[LG-75] Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control
链接: https://arxiv.org/abs/2609.17697
作者: Giray Önür,Azita Dabiri,Bart De Schutter
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Integrated deep reinforcement learning (DRL) and model predictive control (MPC) methods are increasingly used to control autonomous systems by combining their complementary capabilities. DRL learns control policies through interaction with the environment. MPC uses a system model to optimize control inputs while accounting for constraints. In DRL-MPC frameworks with shared control authority, both the DRL agent and the MPC controller each determine part of the control inputs. However, common learning formulations treat MPC as part of the environment and therefore do not explicitly account for MPC’s contribution to control or its interaction with the DRL agent. This paper proposes a novel composite-gradient learning (CGL) method that integrates the MPC controller into the learning process by representing the DRL and MPC control inputs as a joint action and accounting for their interaction when updating the DRL agent during training. CGL is evaluated on two multi-class freeway traffic networks with different strengths of interaction between the DRL and MPC control inputs and it is compared with alternative methods that treat MPC as part of the environment or that only partially incorporate MPC into learning. The results show that CGL offers limited benefit under weak interaction, but learns higher-performing control policies than the alternative methods in a subset of training runs under strong interaction, although the average control-performance gains remain modest.
[LG-76] DSD: Learning Diverse and Reusable Motor Skills via Diffusion Skill Discovery
链接: https://arxiv.org/abs/2609.17682
作者: Sun Woo Kim,Xue Bin Peng
类目: Machine Learning (cs.LG); Graphics (cs.GR)
*备注:
Abstract:Humans efficiently learn new tasks by reusing a rich repertoire of motor skills across different goals and contexts. A similar strategy can also be used to enable simulated characters to efficiently perform new tasks by leveraging reusable motor skills. To support a wide range of downstream tasks, the learned repertoire should be diverse, consisting of distinct behaviors as well as spatial and temporal variation within each behavior. A commonly used method for learning diverse skills is by maximizing the mutual information between skill latents and the states produced by a policy. The marginal state entropy promotes broad behavioral coverage, while the conditional entropy encourages consistent behaviors from each latent. However, directly estimating the marginal state entropy is intractable in high-dimensional control problems. Prior methods therefore rely on indirect latent-space approximations or coarse estimators of the state distribution. These approximations may not effectively promote broad coverage of the state space, resulting in skills with limited behavioral diversity and reduced utility for downstream tasks. In this work, we propose Diffusion Skill Discovery (DSD), a skill discovery method that uses a diffusion model to approximate the entropy gradient of the policy-induced state distribution through score matching. The resulting objective encourages the discovery of skills that produce a broader range of behaviors for high-dimensional humanoid control. The learned skills are reused in two downstream control settings: hierarchical control with a task-specific high-level policy and zero-shot control through latent selection from offline trajectories. Our experiments show that DSD discovers a broader repertoire of reusable motor skills than prior skill discovery methods, leading to the emergence of complex and agile behaviors that can be reused across downstream tasks.
[LG-77] Efficient Robust Learning at the Information-Theoretic Limit
链接: https://arxiv.org/abs/2609.17655
作者: Adam R. Klivans,Konstantinos Stavropoulos,Sergei Tikhonov,Arsen Vasilyan
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:In an important recent work, Blanc (2026) gave an algorithm for robustly learning Boolean concept classes with respect to a fixed distribution that outputs a (randomized) classifier achieving the optimal error of \eta + \varepsilon where \eta is the noise rate. In contrast, it is well known that deterministic hypotheses cannot achieve error less than 2\eta + \varepsilon. Blanc’s algorithm is computationally inefficient, and the main problem left open in his work is to find a polynomial-time algorithm given access to an oracle for empirical risk minimization (ERM). In this paper, we resolve this problem and give such an algorithm. Perhaps surprisingly, our techniques make crucial use of various types of no-regret learners. Additionally, we give an efficient algorithm (no ERM oracle required) for robustly learning any function class that admits sandwiching polynomials with respect to hypercontractive distributions. As one consequence, we give the first polynomial-time algorithm for robustly learning a halfspace with respect to Gaussian marginals that achieves error \eta + \varepsilon for any constant \varepsilon . Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2609.17655 [cs.DS] (or arXiv:2609.17655v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2609.17655 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-78] Regularized Least Squares Training of Quadratic Neural Networks with Applications to System Identification
链接: https://arxiv.org/abs/2609.17654
作者: Luis Rodrigues,Zachary Yetman Van Egmond,Mohammad R. Amiri Fard
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 11 pages single column
Abstract:This paper proposes a least squares approach for the training of quadratic neural networks with regularization. The proposed methodology yields a lower bound on the solution of the training optimization problem for the case where the regularization coefficient is positive. Moreover, it yields closed-form expressions for the approximate solution and its sensitivity The lower bound is tight and the approximate solution is the optimal solution when the regularization coefficient is zero. Having a closed-form expression for the weights reduces considerably the computational time when compared with iterative numerical methods such as backpropagation that can get stuck in local minima. The proposed approach has three main contributions, namely, (i) it yields an analytical expression for the weights, (ii) an analytical expression for the sensitivity of the weights to errors in the data is also provided, (iii) it establishes a connection between the optimization to compute a lower bound and nuclear norm minimization. The proposed least squares training is successfully applied to a nonlinear system identification example where the proposed lower bound is compared with the optimal value.
[LG-79] mbre Analysis of the Hulusi a Southwestern Chinese Free-Reed Instrument using Machine Learning
链接: https://arxiv.org/abs/2609.17612
作者: Yang Xia,Rolf Bader
类目: ound (cs.SD); Machine Learning (cs.LG); Adaptation and Self-Organizing Systems (nlin.AO); Data Analysis, Statistics and Probability (physics.data-an)
*备注:
Abstract:The hulusi is a wind instrument that was invented in Yunnan Province, China, and has become tremendously popular in recent years. It consists of a mouthpiece, a gourd, and three bamboo tubes, all with free reeds made of copper. The main bamboo tube in the middle has seven finger holes. In this instrument, the pipe length, not the free reed’s eigenfrequency, determines the instrument’s pitch, unlike, for example, with the Western accordion or the blues harp. In this study, a machine learning model implemented in the COMSAR framework (this https URL) was used to investigate the timbre characteristics of the \emphhulusi to cluster different instruments and pitches. The measured \emphhulusi pitches C, B, A, G, and F were analyzed according to seven psychoacoustic features, among which only the spectral centroid, sharpness, and fractal correlation dimension are shown to form pitch clusters. These timbre features were used to train Kohonen self-organizing maps (SOMs) for clustering. Brightness and sharpness analysis revealed that the highest pitches were less bright and less sharp than mid- and low-range pitches were. Furthermore, the fractal correlation dimension, which mainly determines the chaoticity of the initial transients, was the best-clustering timbre feature for the hulusi, with the highest pitches showing the least chaoticity. This result is supported by defining a cluster quality index for the SOMs.
[LG-80] Prior-Free Competitive Ratios for Improving Bandits: Scale Curvature and Horizon Are Free but Not Jointly Under Noise
链接: https://arxiv.org/abs/2609.17595
作者: Xuan Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:In the improving multi-armed bandits problem, each of k arms has an unknown nondecreasing, discretely concave reward curve f_i , and pulling arm i for the t -th time yields f_i(t) . For sufficiently long horizons, Blum and Ravichandran (ALT 2025) proved that randomized algorithms achieve an O(\sqrt k) approximation to the best single arm when the scale m=f^*(T) of the optimal arm is known ( T\ge2k ), and O(\sqrt k\log k) when it is not ( T4k ), against an \Omega(\sqrt k) lower bound. The logarithmic factor is unnecessary: a one-page \emphprobe-and-commit algorithm achieves competitive ratio 4\sqrt3,\sqrt k for T\ge2\lfloor\sqrt k\rfloor , without any knowledge of the scale, and we determine the optimal ratio for every horizon, \Theta(\sqrt k+k/T) , also for unknown horizons. Without noise, \emphno prior is needed at all: a random-marginal probing algorithm reading neither the scale m , nor the concavity-envelope exponent \beta of Blum, Garicano, Ravichandran and Sharma (UAI 2026), nor the horizon T , achieves the optimal \Theta(k^\beta/(1+\beta)+k/T) simultaneously for every \beta and every horizon. Under the multiplicative noise model of Blum and Ravichandran, probe-and-commit keeps the same all-horizon order \Theta(\sqrt k+k/T) without knowing the noise level (and \Theta(\sqrt k) on the same range), but the price of priors jumps: for any fixed noise level \varepsilon\in(0,1/2] , the uniform price of adaptation \phi_\varepsilon(k) — the worst case over horizons T\ge16k of the loss relative to k^\beta/(1+\beta) for algorithms knowing neither m nor \beta — is \Theta_\varepsilon(\sqrt\log k/\log\log k) , the lower bound asymptotic in k at fixed positive \varepsilon and matched by a nested random-permutation probing algorithm, whereas knowing either m or \beta alone restores a constant price.
[LG-81] When the Gradient Sees Rank: Provable Necessity Causal Recruitment and Composition in Trained Matrix Memories
链接: https://arxiv.org/abs/2609.17594
作者: Samuel Larson
类目: Machine Learning (cs.LG)
*备注: 6 pages, 2 figures. Code and archived results: this https URL
Abstract:Can gradient-based training learn the rank needed to store and compose associations in a matrix memory? In our earlier study, we used a matrix-augmented reasoner on a task that admits a rank-1 solution, leaving this question open. We train matrix memories on K fresh key-value bindings whose exact linear recovery requires \mathrmrank(Z) \geq K . A fixed linear readout queries a single matrix state without access to the original bindings. Experiments measure recovery by cosine similarity greater than 0.9, a threshold distinct from mathematical equality. Learned effective rank increases with K across the tested grid (Spearman \rho = 1.0 at d = 16 ). Training-time rank caps produce a recovery transition near k = K : at d = 8 , K = 4 , rank 3 gives at most 0.0004 recovery and rank 4 gives 0.97. Four of five seeds retain at least 0.9996 recovery through 21-fold self-application of the trained operator. On the entity subspace, the learned operator has effective rank close to K and approximates the ideal cycle. For the single converged seed capped below K , a calculation using the entity-subspace operator and ideal cycle predicts the measured cosine within 0.008 through seven applications. Extending training resolves several initial failures, but recovery still declines at larger matrix dimensions with encoder width fixed.
[LG-82] Generic Characteristic-Zero Equivalence Between Derivative Bézout Inversion and Multipoint Evaluation
链接: https://arxiv.org/abs/2609.17578
作者: Zijian Zeng
类目: ymbolic Computation (cs.SC); Machine Learning (cs.LG)
*备注:
Abstract:Let a_1,\ldots,a_m be distinct elements of a field K , and let Z(X)=\prod_i=1^m (X-a_i) . We study the arithmetic complexity of computing the unique normalized Bezout pair s,t satisfying sZ+tZ’=1 , with °tm and °sm-1 . The classical product-tree approach requires O(M_K(m)\log m) field operations, where M_K(m) denotes the cost of multiplying degree- m polynomials over K . Thus, even when M_K(m)=O(m\log m) , the resulting bound is O(m\log^2 m) rather than O(m\log m) . Over an infinite field of characteristic zero, we prove that, in the generic rational straight-line-program model, computing all coefficients of the canonical Bezout pair is equivalent, up to an additive O(M_K(m)) cost, to arbitrary-node multipoint polynomial evaluation and to interpolation. The main ingredient is an explicit differential reconstruction that recovers Z from (s,t) in O(M_K(m)) arithmetic operations on a nonempty Zariski-open subset. Combining this reconstruction with automatic differentiation and transposition yields the complexity equivalence. We further transfer Strassen’s lower bound for the elementary symmetric functions to the Bezout problem, obtaining an \Omega(m\log m) nonscalar lower bound. Hence an O(m\log m) algorithm, if it exists in this model, would be asymptotically optimal. The reconstruction is genuinely characteristic-dependent: in characteristic p , all squarefree polynomials X^p+cX+d , with fixed c\ne 0 , have the same normalized Bezout pair (0,c^-1) . Therefore the all-field, all-input O(m\log m) problem remains open; generically in characteristic zero, however, it is reduced to the corresponding arbitrary-node multipoint evaluation and interpolation problem. Subjects: Symbolic Computation (cs.SC); Machine Learning (cs.LG) Cite as: arXiv:2609.17578 [cs.SC] (or arXiv:2609.17578v1 [cs.SC] for this version) https://doi.org/10.48550/arXiv.2609.17578 Focus to learn more arXiv-issued DOI via DataCite
[LG-83] GroupKV: Hierarchical KV Cache Management for Long-Context Diffusion LLM Inference
链接: https://arxiv.org/abs/2609.17573
作者: Jinhao Wang,Zhexin Hu,Kangjie Zhou,Xin Zhou,Fangfang Liu
类目: Operating Systems (cs.OS); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Diffusion large language models (dLLMs) are emerging as a promising generative paradigm that complements autoregressive decoding. In long-context settings, KV cache bloat and offloading transfer overhead have become primary bottlenecks in inference systems. Meanwhile, the periodic full-sequence recomputation and localized token updates in dLLMs make the KV lifecycle substantially more dynamic, complicating cache management and prefetch scheduling while making heavyweight token-level indexing or clustering schemes harder to amortize effectively during decoding. To address these challenges, we present \textscGroupKV, a lightweight hierarchical KV cache management system for long-context dLLM inference. We observe that under block-wise decoding, tokens within the same generation block tend to access highly overlapping and spatially concentrated context regions, making group-level sparse selection effective. Building on this observation, \textscGroupKV partitions the context into contiguous groups and performs coarse-to-fine sparse selection. \textscGroupKV further exploits cross-layer consistency to enable predictive prefetching, and incorporates a staleness correction mechanism to maintain cache coherence under dynamic KV updates. Additionally, \textscGroupKV adopts streaming prefill to reduce peak memory consumption during prefilling. Experiments show that \textscGroupKV extends the maximum serviceable context length by up to 48.00\times under constrained GPU memory, improves end-to-end inference performance by up to 3.73\times in offload-based long-context settings, and maintains competitive task accuracy. Subjects: Operating Systems (cs.OS); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2609.17573 [cs.OS] (or arXiv:2609.17573v1 [cs.OS] for this version) https://doi.org/10.48550/arXiv.2609.17573 Focus to learn more arXiv-issued DOI via DataCite
[LG-84] Disentangling Algorithmic Bias from Archival Artifacts: A Controlled Audit of Vision-Language Model Valuation in Metropolitan Museum Archives
链接: https://arxiv.org/abs/2609.17572
作者: Manpreet Singh,Rhythm Bhatia,Rahul Joshi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Auditing vision-language models (VLMs) for societal bias requires distinguishing direct algorithmic valuation disparities from confounders embedded within archival metadata. In this study, we audit Contrastive Language-Image Pretraining (CLIP) models using historical artwork metadata from the Metropolitan Museum of Art Open Access collection (N = 1,500 total objects; N = 743 attributed works: Male n = 534, Female n = 209; n = 618 anonymous). We establish a quantitative audit framework evaluating zero-shot CLIP logit differential scores across three semantic prompt pairs (masterpiece, quality, and influence). Unadjusted evaluations demonstrate high score convergence without a statistically significant main gender effect under OpenAI CLIP (mu_F = -0.0067 vs mu_M = -0.0035, p = 0.1829) or OpenCLIP (mu_F = 0.0171 vs mu_M = 0.0237, p = 0.1224). Two One-Sided Tests (TOST) confirm statistical equivalence across Cohen’s d = 0.25 bounds (pTOST 0.005). Multivariate OLS regression controlling for artwork medium, creation era, and aspect ratio (R^2 0.02) confirms that artist gender has no statistically significant conditional effect (p 0.20). High residual embedding variance (R^2 2%) indicates that global zero-shot valuation metrics operate near an embedding noise floor, showing that broad zero-shot prompt logit differentials are a coarse measurement instrument rather than proving absolute model fairness. We highlight two key caveats: (i) macro-level score equivalence reflects metric insensitivity to fine-grained visual-semantic features and does not preclude localized micro-level visual biases, and (ii) excluding 41.2% unattributed holdings reflects institutional survival bias. These results demonstrate the necessity of multivariate confound control, equivalence testing, and archival provenance auditing when assessing AI fairness in cultural heritage collections. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.17572 [cs.LG] (or arXiv:2609.17572v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.17572 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Manpreet Singh [view email] [v1] Wed, 29 Jul 2026 17:02:00 UTC (695 KB)
[LG-85] A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings
链接: https://arxiv.org/abs/2609.19083
作者: Marcus M. Noack,Maher B. Alghalayini,Mark D. Risser
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:Kernel methods, and Gaussian Processes (GPs) in particular, require a Hilbertian distance measure—one whose square is conditionally negative definite (CND)—to guarantee positive semi-definiteness (PSD) of the kernel matrix; a condition that fails for many natural input spaces, including smooth manifolds and spaces of probability distributions. We propose the Sparse Landmark Embedding (SLE) kernel, which eliminates this requirement entirely. Each input is embedded into a sparse feature vector via compactly supported bump functions centered at all |D| training points; applying any standard PSD kernel in this embedding space yields a kernel that is provably PSD for arbitrary distance measures. The compact support automatically controls embedding sparsity, keeping kernel matrices well-conditioned and computationally tractable despite the high ambient dimension. We provide theoretical guarantees on PSD, sparsity, stability, and universal approximation, and demonstrate, using geodesic and Wasserstein distances, that the SLE kernel matches or substantially exceeds domain-specific baselines in both predictive accuracy and uncertainty quantification.
[LG-86] LSR-Net: Learning the Forward Evolution Operator for Nonlinear Fluid Dynamics
链接: https://arxiv.org/abs/2609.19039
作者: Qian Hou,Sutrisno,Yuqing Li,Zecheng Gan
类目: Computational Physics (physics.comp-ph); Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注: 7 pages, 4 figures
Abstract:We introduce the Long-Short-Range Neural Network (LSR-Net), a novel neural operator architecture designed for data-driven forward evolution modeling, and extends it to the prediction of nonlinear fluid dynamics. LSR-Net learns the evolution operator of a dynamical system solely from pairs of initial and future state snapshots, which splits the learnable integral kernel into long-range (LR) and short-range (SR) components within stacked network blocks. While the SR component uses standard convolutions to capture local dynamics, the LR component employs a sum-of-exponentials (SOE) representation. This allows for the efficient computation of global interactions as a trainable Fourier multiplier, reducing computational complexity to O(n \log n) where n is the number of pixels in an input snapshot and requiring only a few parameters per channel. LSR-Net is evaluated on three challenging 2D benchmarks: the coupled Burgers equation, the wave equation with a spatially varying coefficient, and the nonlinear shallow water equation (SWE). Results demonstrate that LSR-Net significantly outperforms the baseline short-range network (SR-Net) as well as FNO and DeepONets in predictive accuracy, achieving substantially lower relative errors by effectively capturing both local fine-scale structures and crucial global pattern interactions.
[LG-87] Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion
链接: https://arxiv.org/abs/2609.18928
作者: Lining Mao,Yvonne Peters,Ethan Simpson,Zihan Zhang
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)
*备注: 23 pages, 9 figures, to be submitted to PRX Intelligence
Abstract:In particle collider experiments, event reconstruction is the task of inferring the kinematics of short-lived particles produced in the hard scatter from the stable final states recorded by detectors. We decompose event reconstruction into two primary tasks: assigning measured jets and charged leptons to parent particles, and predicting unmeasured neutrino kinematics. We present VyPER, a novel geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology. VyPER combines the supervised classification of hyperedges for particle assignment with a diffusion model for predicting neutrino kinematics, leveraging a joint loss function to optimize both reconstruction tasks within a unified framework. We showcase VyPER across several proton-proton collision processes, comparing its performance to existing analytical and machine-learning-based reconstruction techniques. In doing so, we demonstrate that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes, opening new avenues for precision measurements in the Higgs boson, electroweak, and top-quark sectors.
[LG-88] Fast Learning Rates for Physics-Informed Kernel Methods
链接: https://arxiv.org/abs/2609.18901
作者: Luc Brogat-Motte,Joachim Bona-Pellissier,Giacomo Meanti,Lorenzo Rosasco
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:In physics-informed machine learning, a target function u^* is learned from noisy value observations y_i=u^(x_i)+ \varepsilon_i , together with differential information, given either by noisy observations d_j=(Du^)(z_j)+\xi_j or by a known physical constraint Du^=v . We consider the setting where D is a linear differential operator and analyze a physics-informed kernel estimator \hat u combining n value observations and m differential observations. In this context, we ask how much can differential information improve predictions, and how does this improvement depend quantitatively on n , m , and D . We prove finite-sample bounds, supported by numerical simulations, revealing a two-regime structure for the prediction error. When m is limited, the rate depends jointly on n and m ; when m exceeds a problem-dependent threshold, the rate saturates and matches the oracle rate obtained when the perfect constraint D \hat u = Du^ is imposed. Examples are discussed for Sobolev spaces which are reproducing kernel Hilbert spaces and include partial Laplacian constraints on the torus and gradient observations on bounded domains. These examples illustrate the range of possible learning rate improvements — from the standard nonparametric n^-1/4 to the parametric rate n^-1/2 . Finally, we derive physically consistent rates in a stronger norm that jointly controls the errors in \hat u and D\hat u .
[LG-89] Learning Lyapunov Operators for Nonlinear Systems
链接: https://arxiv.org/abs/2609.18894
作者: Amartya Mukherjee,Maxwell Fitzsimmons,David C. Del Rey Fernández,Jun Liu
类目: Analysis of PDEs (math.AP); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Constructing Lyapunov functions for nonlinear dynamical systems is a central problem in stability analysis, yet remains challenging. Lyapunov functions are commonly characterized as solutions to first-order partial differential equations (PDEs), but these solutions are typically obtained for single systems, limiting their reuse across systems. In this paper, we study the Lyapunov solution operator that maps a vector field to the corresponding Lyapunov function defined by a dissipation-based Lyapunov PDE. We establish that, on compact subsets of the domain of attraction and under exponential stability assumptions, this operator is well-defined, unique, and continuous with respect to perturbations of both the vector field and the dissipation function. These results provide a theoretical foundation for approximating Lyapunov functions uniformly over families of nonlinear systems. Building on these theoretical foundations, we employ Fourier Neural Operators (FNOs) as a data-driven approximation of the Lyapunov solution operator. Numerical experiments demonstrate that a single trained operator can accurately approximate the numerical Lyapunov functions across parameterized families of dynamics. This illustrates the potential of neural operators for approximating Lyapunov functions.
[LG-90] Stable Filters for Generative Modeling of Graph Signals ICASSP’27
链接: https://arxiv.org/abs/2609.18759
作者: Martin Schmidt,Gonzalo Mateos
类目: ignal Processing (eess.SP); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 5 pages, submitted to ICASSP’27
Abstract:Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While recent graph-aware Schrödinger bridge models incorporate topology information directly into their reference dynamics, it is unclear how perturbations of the graph propagate through these dynamics and affect the resulting generated distributions. In this paper, we analyze the structural stability of graph-aware continuous-time generative models whose drift combines a graph filter with a learned graph neural network. We derive explicit Wasserstein stability bounds that quantify the effect of relative graph perturbations on the generated distributions. Motivated by these bounds, we introduce a principled framework for designing stable graph filters that preserve the smoothing behavior of graph heat diffusion, while boosting structural stability. Experiments on synthetic and fMRI signals show our stable filters enhance structural robustness while matching or exceeding the generative quality of the heat equation baseline.
[LG-91] Rank and computation of the pathlifting Jacobian of a DAG ReLU network
链接: https://arxiv.org/abs/2609.18682
作者: Manon Verbockhaven(OCKHAM)
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:This paper provides a self-contained proof of the rank of the pathlifting Jacobian of a DAG ReLU network by performing an induction on the network’s number of hidden nodes. In fact, the induction is elementary, and the key recipe is to consider the skeleton matrix of the network, a sparse matrix encoding the network paths, and transform the representation of one of its hidden neurons into an output node. The proof relies on intermediate propositions which link the pathlifting, its Jacobian, the network parameters, and its skeleton matrix, which, on top of permitting to conclude on the rank of the pathlifting Jacobian, also provide a way to compute it without backpropagation and whose computation cost is super efficient in practice compare to usual backpropagation. The paper is provided with a Python module that implements the different propositions of the paper for feed forward networks and is used to experimentally quantifies the computational gain of computing the pathlifting Jacobian with the proposed theory.
[LG-92] Learning Array Signal Topologies as Conditional Neural Manifolds ICASSP2027
链接: https://arxiv.org/abs/2609.18616
作者: Julian P. Merkofer,Vincent van de Schaft,Ruud J. G. van Sloun
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: Submitted to ICASSP 2027
Abstract:Subspace methods such as multiple signal classification (MUSIC) achieve super-resolution direction of arrival (DoA) estimation by exploiting the orthogonality between the array manifold and the noise subspace of the measurements. Their accuracy therefore depends on the assumed manifold and degrades under model mismatch, while parameters not identifiable from the spatial manifold cannot be recovered. In this work, we propose the conditional neural manifold (CNM), which replaces the fixed manifold with an observation-conditioned mapping from source parameters to steering vectors. An encoder maps the snapshots to a latent scene representation that conditions a zero-initialized neural field over the parameter space. The manifold is learned without steering-vector supervision by shaping the resulting MUSIC landscape. Since the correction acts on the manifold rather than on the estimator, it can be used by other manifold-based methods without modification. The CNM restores resolution under array imperfections, colored noise, correlated sources, and near-field propagation, and resolves the angle-frequency ambiguity inherent to the nominal spatial manifold.
[LG-93] Deep learning emergent spacetime from fermionic spectral functions in holography
链接: https://arxiv.org/abs/2609.18566
作者: Koji Hashimoto,Hyun-Sik Jeong,Keun-Young Kim,Daichi Takeda,Kwan Yun
类目: High Energy Physics - Theory (hep-th); Strongly Correlated Electrons (cond-mat.str-el); Machine Learning (cs.LG)
*备注: 15 pages, 9 figures
Abstract:We present a physics-informed machine learning framework based on Neural Ordinary Differential Equations that solves the holographic inverse problem: reconstructing the bulk spacetime and gauge field of a charged AdS black hole directly from boundary fermionic spectral functions. Encoding the UV asymptotics, horizon regularity, and zero temperature extremality as hard constraints in the neural network architecture, our framework reliably reconstructs the extremal Reissner-Nordström AdS geometry across three quantum critical regimes set by the U(1) probe charge—non-Fermi liquid, marginal Fermi liquid (strange metal), and Fermi-liquid-like states—and can jointly infer the probe charge itself to sub-percent accuracy. Relaxing the near-AdS boundary constraint uncovers a geometrical degeneracy: bulk profiles that differ throughout the radial direction but share the same near-horizon AdS_2 \times \mathbbR^2 data reproduce identical spectral functions near the Fermi surface. This isospectral non-uniqueness is precisely the bulk degeneracy expected on general holographic grounds at zero temperature, and its spontaneous emergence across independent training runs shows that the network isolates the IR CFT universality rather than overfitting a single UV completion.
[LG-94] Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models
链接: https://arxiv.org/abs/2609.18489
作者: Jiakai Chen,Joel Oskarsson,Simon Driscoll,Sebastian Schemm
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:
Abstract:This study analyses kinetic energy (KE) spectra, difference kinetic energy (DKE) spectra, and signatures of KE transfer across spatial scales in four state-of-the-art probabilistic machine learning weather prediction (MLWP) models: NeuralGCM-ENS, FourCastNet 3, AIFS-ENS, and GenCast. Results are compared with those from the physics-based numerical weather prediction model IFS-ENS. While NeuralGCM-ENS successfully reproduces the expected upscale transfer of KE, noise injection at its encoder stage underestimates mesoscale KE. Conversely, AIFS-ENS, GenCast, and FourCastNet 3 produce realistic KE spectral magnitudes but do not capture the expected upscale transfer of KE. In particular, AIFS-ENS and GenCast, which employ spatially uncorrelated stochastic perturbations, exhibit enhanced accumulation of KE at high wavenumbers. All examined models exhibit upscale error growth, reflected by the progressive shift of the DKE spectral peak toward larger wavelengths over time. However, the MLWP models struggle to reproduce the rapid initial growth of ensemble spread at small spatial scales associated with the butterfly effect. The results show that MLWP models can misrepresent the known scale transfer of kinetic energy despite producing skilful weather forecasts.
[LG-95] Gradient Descent with Stochastic Subspaces via Persistence of Memory
链接: https://arxiv.org/abs/2609.18416
作者: Subhroshekhar Ghosh,Clement Z. Q. Ng,Pierre-Louis Poirion,Akiko Takeda
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Signal Processing (eess.SP); Machine Learning (stat.ML)
*备注: 81 pages, 2 figures
Abstract:Stochastic subspace methods have gained popularity as gradient descent based techniques for large scale optimisation problems, especially in distributed settings. In this paper, we introduce the technique of “persistence of memory” to greatly extend and improve the random subspace methods. To this end, we leverage a vector that is only weakly correlated with the gradient in order to provide a guiding structure to the generative process of the random subspace along which the descent is going to take place. This guidance vector may be fixed for a large number of iterations, only to be refreshed at wide intervals (on whose size we can provide guarantees in terms of problem parameters). In important machine learning settings, such as optimisation problems embodying sparsity or a minibatch structure, we show that the guidance vector can be obtained in an effective and computationally inexpensive manner by leveraging the structured properties of the problem. En route, we establish to our knowledge the first theoretical analysis of classical SSD methods for sparse functions. In a local neighbourhood of the optimum, we demonstrate an alignment phenomenon of our gradient estimates with a low-lying eigenvector of the Hessian, allowing a once-for-all computation of the guidance vector which renders the method computationally favourable even in scenarios with unstructured objectives.
[LG-96] Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking
链接: https://arxiv.org/abs/2609.18173
作者: Gaofeng Dong,Vamsi Eyunni,Pragya Sharma,Kang Yang,Mani Srivastava
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 7 pages, accepted to the 6th International Workshop on the Internet of Things for Adversarial Environments (IoTAE), IEEE MILCOM 2026
Abstract:Vehicle tracking is fundamental to applications ranging from urban mobility and public safety to security and defense. Conventional tracking relies on direct access to sensors that provide strong observations such as vehicle identity and location. In practice, however, factors such as ownership, privacy, cost, and operational constraints may limit directly accessible sensors, leaving sparse observations and long tracking gaps. Meanwhile, many additional third-party sensing assets may be present across the environment but remain inaccessible at the raw-data level, preventing their direct integration into the tracking system. In this work, we investigate whether weak, indirect observations with uncertain spatial and temporal cues can complement sparse direct sensing for vehicle tracking. Specifically, we propose GrayTrack, which fuses weak anonymous events with sparse direct observations using a road-constrained particle filter. We build a CARLA-Mininet-WiFi pipeline to evaluate the system under controlled conditions, generating direct observations from accessible cameras and indirect observations from third-party cameras. Our learning-based detector achieves an F1 score of 0.989 for anonymous vehicle passages. Further, incorporating indirect third-party observations reduces trajectory RMSE by 60.1% and catastrophic track loss from 35.8% to 0.3%. These results demonstrate that GrayTrack can effectively exploit weak indirect observations to extend tracking capabilities.
[LG-97] Preservation of Log-Concavity and Convergence of Wasserstein-Fisher-Rao Gradient Flows
链接: https://arxiv.org/abs/2609.18118
作者: Francesca Romana Crucinio,Sahani Pathiraja
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:We study the convergence of Wasserstein-Fisher-Rao (WFR) gradient flows for sampling from probability distributions known up to a normalisation constant. By combining Wasserstein transport with Fisher-Rao birth-death dynamics, WFR flows balance exploration and selection. These flows have been recognised as a promising mechanism to accelerate convergence beyond Langevin dynamics. We show that for a class of strongly log-concave target distributions satisfying additional curvature conditions, WFR flows preserve strong log-concavity, in contrast to Wasserstein flows which enjoy this property only in the Gaussian setting. Exploiting this result, we derive explicit non-asymptotic convergence rates for the symmetrised Kullback-Leibler divergence, without requiring a warm-start as required in current estimates. In particular, we show that the convergence rate decomposes additively into Wasserstein and Fisher-Rao contributions, thereby confirming a recent conjecture within this setting. These results provide refined convergence guarantees and further develop the theoretical foundations of WFR gradient flows for sampling and Bayesian inference.
[LG-98] Fourier Analysis of Parametrized Interactive Quantum Classifiers
链接: https://arxiv.org/abs/2609.17991
作者: Fábio Novaes,Fernando M. de Paula Neto,João V. M. Cardoso
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 34+9 pages, 8 figures
Abstract:Interactive Quantum Classifiers (IQCs) constitute a family of quantum machine learning models inspired by open quantum systems, in which the interaction between a target qubit and an environment is described by a Hamiltonian. Previous works introduced alternative Hamiltonian parameterizations and showed empirically that they can improve classification performance, but the role of these parameters in the resulting classifier remains poorly understood. In this work, we derive a closed-form expression for the reduced quantum channel generated by a parametrized IQC with a single target qubit. The analytical solution explicitly reveals how the Hamiltonian parameters control the constant, sine, and cosine components of the classifier output, establishing a Fourier interpretation of the induced feature map. This analysis motivates a generalized family of Hamiltonian encodings, including matrix-parameterized environmental Hamiltonians whose Fourier components depend on linear combinations of input features, thereby enabling non-separable Fourier structures. Numerical experiments on synthetic and real-world datasets show that the proposed models can improve classification performance on several nonlinear benchmarks. The generalized matrix encoding achieves the strongest aggregate performance in the evaluated benchmark, while a simpler four-parameter extension often attains comparable performance with substantially fewer trainable parameters. We additionally characterize the generated state ensembles using the standard fidelity-based expressibility measure, finding that global expressibility does not directly predict classification performance. Our results provide an analytical characterization of parametrized Hamiltonians in Interactive Quantum Classifiers and establish Fourier analysis as a useful framework for understanding and designing open-system-inspired quantum learning models.
[LG-99] Matching Multi-Loop Complexities with a Single Loop: Optimal Optimization Stationarity and Best-Known Game Stationarity in Nonconvex–Concave Minimax Optimization
链接: https://arxiv.org/abs/2609.17973
作者: Minghao Zhang,Zi Xu
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We introduce a new single-loop algorithmic framework for smooth nonconvex–concave minimax optimization. The resulting projected damped extragradient method combines projected extragradient updates, dual momentum, and a moving proximal center. Under both the optimization-stationarity and game-stationarity criteria, our method achieves the best-known complexity among single-loop first-order methods. For optimization stationarity, our method achieves a gradient complexity of O(L^2D_Y\bar\Delta_0\varepsilon^-3) , where L is the gradient Lipschitz constant, D_Y bounds the diameter of the dual feasible set, and \bar\Delta_0 is an initialization quantity involving the value-function gap and the initial gradients. Moreover, by incorporating a fixed-center warm-up phase, the complexity can be improved to O(L^2D_Y\Delta_\phi\varepsilon^-3) , up to an additive lower-order cost, where \Delta_\phi:=\phi(x_0)-\inf_x\phi(x) . We further establish a lower bound of \Omega(L^2D_Y\Delta_\phi\varepsilon^-3) for optimization stationarity over projected zero-respecting first-order methods. This lower bound proves that the warm-started version of our algorithm is optimal up to a constant factor for optimization stationarity within this oracle class. For game stationarity, our method achieves \mathcalO!(L^3/2D_Y^1/2\Delta_\phi\varepsilon^-5/2) gradient complexity. This matches the best-known complexity of multi-loop first-order methods, thereby establishing the same complexity with a single-loop algorithmic structure. Under dual strong concavity, the proposed framework achieves O!(\sqrt\kappa,L\Delta_\phi\varepsilon^-2) leading complexity for both stationarity criteria, where \kappa=L/\mu is the dual condition number, up to an additive initialization cost. The \varepsilon^-2 accuracy dependence is optimal under fixed regularity and initialization bounds.
[LG-100] QEMScore: How Much Does the Measurement Add to Learned Quantum Error Mitigation?
链接: https://arxiv.org/abs/2609.17896
作者: Yue Zhao,Huayue Gu,Yushun Dong,Xiyang Hu
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 55 pages, including references and appendices
Abstract:How much does the noisy measurement add to learned quantum error mitigation? An accuracy table cannot say, because a model handed circuit structure can score well without reading the measurement at all. QEMScore adds the comparison that can. Each simulated circuit carries an exact ideal answer. The learned mitigator is scored beside a capacity-matched control, a model just as flexible that reads the same circuit description but never the measurement. Each method’s measurement spend is accounted and not equalized. We run a controlled campaign on simulated circuits and reanalyze two published learned mitigators, Q-LEAR and QRAFT, from their released hardware data. Three findings stand out. First, under familiar within-family conditions (S0) evaluated across two spin-chain families and three seeds, continuous couplings identify the target, and the control that never reads the measurement matches 87.7 to 100.5 percent of the mitigator’s gain over an affine fit to the circuit description. A plain polynomial in the coupling parameters, fitted after the campaign, beats the mitigator on all six evaluations, reflecting the selected learners’ capacity. Second, for these selected learners, matching most of the gain is not matching the accuracy: on five of six evaluations the mitigator removes 19.5 to 74.5 percent of the error the capacity-matched control leaves, a learner-specific gap rather than a measurement requirement. Third, on released hardware data where descriptors only partially identify queries, the findings differ: flexible models of the descriptors show negligible mean gain over affine fits in Q-LEAR, and measurement inputs carry predictive gains in both Q-LEAR and QRAFT. These comparisons reflect representation- and protocol-specific behavior rather than an isolated cross-regime difference. A learned mitigator’s accuracy should therefore be reported beside such controls.
[LG-101] Bracketing Uncertainty in Clustering Under the Manifold Hypothesis
链接: https://arxiv.org/abs/2609.17892
作者: Savik Kinger,Luciano Dyballa,Steven W. Zucker
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:The manifold hypothesis suggests a natural criterion for clustering: partition data according to the manifold component from which each point is drawn. Whether two components are separable depends on a geometric tradeoff: the ambient separation between components versus the largest gap in sampling. In practice, this tradeoff is rarely assessed explicitly, leading standard methods to over-commit to a single clustering assignment even when the data do not support a unique answer. We formalize this tradeoff by combining intrinsic manifold geometry (volume growth and reach) with sample-level quantities (fill distance and density), yielding a threshold phenomenon for mutual- k -nearest-neighbor graphs: when the offset-to-fill ratio exceeds a conservative upper threshold, component separation is preserved; below a lower threshold, components fuse. The gap between these thresholds defines a geometric uncertainty zone in which the number of clusters is not identifiable from the data. Nevertheless, conventional approaches still seek one: sweeping parameters (an engineering approach) or fitting a generative mixture model (a model-based approach). Rather than forcing a single estimate of the number of clusters, we propose Manifold-Based Clustering (MBC), which returns an explicit bracket interval to quantify the underlying data uncertainty. This bracket acts as an empirically calibrated diagnostic: it narrows when a single resolution is supported, widens when multiple resolutions coexist, and collapses to one when no separated structure is detectable. Empirically, we find that many real datasets lie within the uncertainty zone rather than admitting one clear answer. Our results suggest that ambiguity in cluster number is often intrinsic, and should be quantified rather than resolved.
[LG-102] Sharp margin-based generalization bounds for realizable SVM
链接: https://arxiv.org/abs/2609.17845
作者: Steve Hanneke,Aryeh Kontorovich
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Let the exact homogeneous hard-margin support vector machine be trained on (m) independent observations from a Borel probability law on a real Hilbert space. We prove that, with score zero counted as an error, there is a universal numerical constant (C) such that [ \Pp\left( \gamma_m0,\quad \Risk(u_m) \fracCm \left( K_m+\log\frac1\delta \right) \right) \le \delta . ] Here (\gamma_m) is the empirical homogeneous margin, (u_m) is the exact minimum-norm unit-margin separator, (r_m) is the largest training radius, and (K_m:=r_m^2\normu_m^2=r_m^2/\gamma_m^2) on (\gamma_m0\). The proof is driven by a deterministic deletion problem. Given vectors (x_1,\ldots,x_n) in the unit ball, delete a set (B) of constraints and let (u_B) be the closest point to the origin that satisfies every retained unit-margin constraint. Suppose that (\normu_B^2\le k) and that every deleted vector has nonpositive score under (u_B). We prove that a family of such deletion sets of cardinality (q) has size at most (\exp(8k+2q)). The conceptual step is an exact identity obtained from the KKT representation of (u_B). For a random deletion set, the identity converts the mean squared spread of the separators into a weighted sum of score deficits. It therefore forces a coordinate whose deletion status separates the two conditional means by a quantitatively large amount. Revealing that coordinate decreases the conditional separator variance enough to control the binary entropy of the split. An entropy induction gives the deletion count, and an exact factorial ghost-sample identity converts that count into the stated high-probability SVM bound. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST) Cite as: arXiv:2609.17845 [stat.ML] (or arXiv:2609.17845v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2609.17845 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Aryeh Kontorovich [view email] [v1] Tue, 15 Sep 2026 21:03:47 UTC (17 KB) Full-text links: Access Paper: View a PDF of the paper titled Sharp margin-based generalization bounds for realizable SVM, by Steve Hanneke and Aryeh KontorovichView PDFHTML (experimental)TeX Source view license Current browse context: stat.ML prev | next new | recent | 2026-09 Change to browse by: cs cs.LG math math.ST stat stat.TH 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
[LG-103] METALICA: METAdynamics and repLICA exchange for enhanced diffusion sampling
链接: https://arxiv.org/abs/2609.17823
作者: Alireza Omidi,Jiajun He,Jörg Gsponer,Saifuddin Syed
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Biological Physics (physics.bio-ph); Chemical Physics (physics.chem-ph)
*备注: 14 pages, 6 figures
Abstract:Many proteins function through transitions between conformational states, yet rare states are rarely sampled by diffusion models trained on an equilibrium ensemble, demanding better sampling methods. We introduce METALICA, which implements Metadynamics on a pretrained diffusion model via Replica Exchange. It accumulates a bias potential along a Collective Variable, repels new samples from previous ones through biased sampling, and reweights samples onto the unbiased distribution. METALICA holds one replica per diffusion level, forming a Markov Chain that evolves through inter-replica communication and is refined in place as the bias grows. METALICA is the dual of sequential control, in which Sequential Monte Carlo parallelizes the sampler over a batch of particles. Parallelism over the levels of the diffusion-time schedule instead allows METALICA to generate samples from long chains, essential for the discovery of rare events, with accuracy set by run length rather than by the memory available. We validate on a bimodal target with known free energies, then apply METALICA to the unfolding of a protein. At a budget for which sequential control yields no unfolded structure, METALICA populates the basin and resolves a second free energy minimum.
[LG-104] Derivative-Free Structured Updates for Muon
链接: https://arxiv.org/abs/2609.17759
作者: Pengcheng Xie
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 23 pages
Abstract:Muon updates matrix-valued neural-network parameters by orthogonalizing a gradient-based momentum matrix. Its reliance on derivatives limits its use when gradients are unavailable or unreliable. We develop a derivative-free framework that constructs Muon-style updates from structured finite differences. Four variants are considered: full entrywise recovery, random low-rank surrogates, basis-aligned rank-one probing, and direct structured search. Exhaustive basis-aligned probing is equivalent, up to positive scaling before ideal polar orthogonalization, to coordinate finite differences. Matrix-regression experiments show that random rank-one probing can reduce the number of function evaluations substantially, at the cost of less accurate updates. Controlled noisy-gradient experiments on regression and a neural network illustrate when accurate function values can compensate for an unreliable gradient oracle. A small CartPole study further examines orthogonal rank-one probes under a fixed episode budget. These results support structured probing as a practical option for selected black-box problems; they do not establish a general convergence guarantee or an advantage over accurate, inexpensive gradients.
[LG-105] Similarity Pairing with Energy Movers Distance for Self-Supervised Pre-Training at the LHC
链接: https://arxiv.org/abs/2609.17738
作者: Ho Fung Tsoi,Dylan Rankin
类目: High Energy Physics - Experiment (hep-ex); Machine Learning (cs.LG)
*备注: 6 pages
Abstract:Many self-supervised methods for training foundation models at the Large Hadron Collider (LHC) rely on data augmentations to encourage the model to embed events into a representation space invariant to certain physical or detector symmetries. A common challenge arises from the large freedom in choosing a proper set of augmentations on which downstream performance depends. The implementation of augmentations involves either modifying existing events, potentially breaking the event fidelity, or simulating more event variants, which is computationally intensive. In this work, we present a data-driven method of pairing events by their similarity via the energy mover’s distance (EMD), which measures how similar two events are in terms of the work required to transform one into the other. With this approach, distinct events are sampled and matched by their similarity to serve as views for learning invariance, keeping the physics content of each event intact without handcrafted distortions. We demonstrate this augmentation-free pairing method by pre-training on QCD jets via self-distillation and show that it can yield semantic jet embeddings with downstream discrimination power comparable to or better than an augmentation-based baseline.
[LG-106] Machine learning kinetics from molecular dynamics data
链接: https://arxiv.org/abs/2609.17736
作者: Jonathan Weare,Aaron R. Dinner
类目: Computational Physics (physics.comp-ph); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Data Analysis, Statistics and Probability (physics.data-an)
*备注: 24 pages, 5 figures
Abstract:Most molecular transitions occur on timescales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration reaches a product state before a reactant state, is a central kinetic statistic, providing a mechanism-independent reaction coordinate and a foundation for transition path theory and the calculation of rates. This review surveys modern approaches for estimating the committor and related kinetic statistics from molecular simulations, with an emphasis on self-supervised methods that learn solutions of their defining dynamical equations rather than relying on labeled shooting data. We develop a common operator viewpoint connecting generator-based partial differential equations, variational principles, Markov state models, dynamical Galerkin approximation, and neural networks. Empirical and theoretical evidence points to the efficiency of these methods. We provide theoretical and practical guidance for realizing their full potential in applications, including strategies for treating non-Markovian effects and for sampling. We conclude by identifying opportunities for further research, including connections to reinforcement learning and generative modeling.
[LG-107] Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays
链接: https://arxiv.org/abs/2609.17726
作者: Ho Fung Tsoi,Alex Yang,Luis Felipe Gutierrez Zagazeta,Shion Chen,Dylan Rankin
类目: High Energy Physics - Experiment (hep-ex); Machine Learning (cs.LG)
*备注: 7 pages
Abstract:Reconstructing the mass of a heavy resonance from its decay products with missing energy is one of the central tasks that directly determine the sensitivity in new physics searches at collider experiments. Supervised learning approaches to this problem often struggle to generalize well due to the presence of various systematic uncertainties and distribution shifts. Exhausting all possible variations in the labeled data can be very compute-intensive, while a failure of the model to generalize can corrupt the reconstructed resonance widths that are critical in peak-hunting analyses. In this work, following the foundation model paradigm, we use a self-supervised approach to pre-train a transformer encoder with VICReg to learn an embedding invariant to various corruptions, then fine-tune it for mass regression on a heavy resonance with masses ranging from 2.5 to 6.5 TeV and a SUSY-like cascade decay into an eleven-body final state. We show that the pre-trained model reconstructs sharper resonance peaks and has a more stable performance under various realistic corruptions, compared to a supervised model of the same architecture trained on the same augmented data from scratch.
[LG-108] Decoding Extrahepatic Targeting of Lipid Nanoparticles with Interpretable Machine Learning
链接: https://arxiv.org/abs/2609.17721
作者: Asal Mehradfar,Mohammad Shahab Sepehri,Owen Antholine,Varun Shankar,Glen S. Kwon,Salman Avestimehr,Morteza Rasoulianboroujeni
类目: Quantitative Methods (q-bio.QM); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
*备注:
Abstract:Lipid nanoparticles (LNPs) have transformed RNA medicine, yet their clinical utility remains constrained by predominant hepatic accumulation after systemic administration. Redirecting LNPs to extrahepatic tissues requires understanding of how lipid chemistry and formulation composition jointly govern in vivo biodistribution. Here, we develop an interpretable machine learning framework to predict hepatic versus extrahepatic LNP accumulation and identify molecular design rules for extrahepatic RNA delivery. A literature-derived dataset of 476 intravenous LNP formulations was curated from 81 studies, integrating formulation composition, lipid chemical structures, and IVIS-based biodistribution profiles. Standardized SMILES representations of ionizable lipids, helper lipids, sterols, PEGylated or polymer-conjugated lipids, additional lipids, and polymer repeat units were converted into RDKit Expert descriptors and combined with formulation-level variables to generate an 808-dimensional feature representation. Logistic regression, random forest, and XGBoost achieved ROC-AUC values of 0.839, 0.866, and 0.874, respectively. SHAP-based interpretation and consensus feature ranking revealed that ionizable-lipid descriptors dominate biodistribution prediction, while formulation composition, particularly ionizable lipid, sterol, and PEGylated/polymer-conjugated lipid fractions, contributes substantially. The top 20 consensus features retained nearly all predictive information in tree-based models. The most informative features implicated electrotopological surface properties, charge- and hydrophobicity-weighted surface areas, molecular topology, and amide/alkyl structural motifs as drivers of extrahepatic accumulation. This study establishes an interpretable, data-driven strategy for decoding LNP biodistribution and provides actionable design principles for engineering LNPs beyond the liver.
[LG-109] Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction
链接: https://arxiv.org/abs/2609.17646
作者: Kishalay Das
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal property prediction and crystal structure generation. Motivated by the high computational cost of Density Functional Theory (DFT) and the limited availability of labeled materials data, the thesis explores graph representation learning, pretraining, multimodal learning, and generative modeling for scalable materials design. For property prediction, the thesis first introduces CrysXPP, which learns transferable crystal representations through unsupervised graph autoencoding, reducing dependence on large property-labeled datasets. It then proposes CrysGNN, a large-scale self-supervised graph pretraining framework that captures atomic connectivity, chemical attributes, and global structural information and transfers this knowledge to downstream property predictors through knowledge distillation. CrysMMNet further enriches crystal representations by jointly modeling graph structure and textual descriptions, thereby incorporating both local chemical and global structural knowledge. For crystal generation, the thesis introduces TGDMat, a text-guided joint diffusion framework that jointly models lattice parameters, atomic types, and atomic coordinates while incorporating textual structural knowledge during denoising. This enables the generation of more valid and stable periodic materials while also supporting conditional generation from natural-language descriptions. Overall, the thesis establishes a unified AI-based framework for data-efficient property prediction and controllable crystal generation, demonstrating how graph learning, multimodal representations, and generative models can reduce computational cost and improve the scalability of materials Subjects: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG) Cite as: arXiv:2609.17646 [cond-mat.mtrl-sci] (or arXiv:2609.17646v1 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2609.17646 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kishalay Das [view email] [v1] Tue, 15 Sep 2026 15:15:21 UTC (3,673 KB)
[LG-110] Democratizing Clinical Tumor Whole Genome Sequencing: 18-hour End-to-end Analysis via Trillion-parameter Large Language Models Locally Deployed on Consumer-grade Hardware
链接: https://arxiv.org/abs/2609.17620
作者: Rui Xiao,Yili Xu
类目: Genomics (q-bio.GN); Machine Learning (cs.LG)
*备注: 11 pages. Corresponding author: Yili Xu(22465225@qq.com)
Abstract:Whole genome sequencing (WGS) is essential for precision oncology, yet its clinical adoption remains limited by prohibitive computational costs and multi-day turnaround times. This work presents a fully localized low-resource framework enabling stable deployment of a trillion-parameter biomedical LLM on a single consumer-grade RTX 4060 laptop with 32GB system memory and 8GB VRAM, as well as on routine clinical workstations in general hospitals, completing the entire tumor-paired WGS workflow from raw FASTQ input to clinical-grade full-variation-spectrum report output. Under standard 30X depth configurations, our implementation finishes a single tumor-paired WGS analysis within 18 hours, achieving 99.62% F1 score for somatic variant detection with over 99.9% concordance to the industrial-standard A100 cluster pipeline, fully meeting clinical oncology accuracy requirements. Quantitative profiling shows adaptive heterogeneous memory scheduling accounts for 71% of total execution time, while model optimization introduces less than 9% of total detection error. This work is the first engineering implementation of trillion-parameter biomedical LLM-driven clinical-grade genomic analysis on consumer-grade hardware, breaking the industry paradigm that trillion-scale genomic LLMs require hundred-thousand-dollar GPU clusters and multi-day turnaround, establishing a low-resource pathway for global primary medical institutions to adopt whole-genome precision oncology at zero additional cost.
[LG-111] Stability-Constrained Approximation in Spline KANs: Exact Layer Balancing and Budget-Compatible Saturation
链接: https://arxiv.org/abs/2609.17619
作者: Aleksander Tankman
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: 33 pages. Ancillary code reproduces the numerical constants of Appendix B. Complements arXiv:2604.26444 , which constructs deep KAN representations with controlled layer-wise Lipschitz product; here the allocation of that product across layers is solved exactly, and the cost of preserving it under spline discretisation is bounded from both sides
Abstract:Deep spline superposition networks face a tension between approximation order and stability across depth. We study approximation under a hard layerwise Lipschitz budget, and organise it around two quantities: the factorisation stability complexity of a given deep factorisation, and the budget-compatible approximation complexity of a discretisation operator. First, we solve exactly the finite-depth diagonal balancing problem for a fixed chain of nonnegative envelope matrices: the optimal uniform layer budget equals |M_L-1\cdots M_0|_\infty\to\infty^1/L , attained by an explicit one-pass minimiser, for rectangular layers, with a complete treatment of degeneracies and non-attainment. The optimum can be arbitrarily larger than the Lipschitz constant of the network itself, because passing to envelopes destroys sign cancellation. Second, we give a constructive spline discretisation theorem preserving the budget up to a controlled slack, with an explicit grid threshold. Conversely, for linear spline-valued operators that preserve the budget exactly, we prove budget-compatible minimax lower bounds on classes constrained simultaneously in the first and third derivative norms – a constraint pair that is forced by the problem and that rules out the usual scaling escapes. Finally, we show that the corresponding layer errors need not cancel under composition: for every operator of the class there is a stable depth- L tower realising a constant fraction of the accumulated error, so the linear-in-depth accumulation of the upper bound is not a proof artefact. Comments: 33 pages. Ancillary code reproduces the numerical constants of Appendix B. Complements arXiv:2604.26444, which constructs deep KAN representations with controlled layer-wise Lipschitz product; here the allocation of that product across layers is solved exactly, and the cost of preserving it under spline discretisation is bounded from both sides Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Numerical Analysis (math.NA) MSC classes: 41A15, 41A25, 41A36, 15A60, 15B48, 65D07, 68T07 Cite as: arXiv:2609.17619 [stat.ML] (or arXiv:2609.17619v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2609.17619 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Aleksander Tankman [view email] [v1] Mon, 14 Sep 2026 20:17:04 UTC (44 KB)
[LG-112] Rényi Tracking Bounds for Langevin Dynamics with Moving Targets
链接: https://arxiv.org/abs/2609.17577
作者: Yuchen Xin,Jingxin Zhan,Zhihua Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study Langevin diffusion and Langevin Monte Carlo (LMC) when the target distribution changes over time. Under a log-Sobolev inequality (LSI), we derive non-asymptotic Rényi-divergence guarantees for tracking the current target. The framework covers continuous-time Langevin diffusion and its discretizations. We then apply the results to nonsmooth sampling based on successive Moreau envelopes. For this scheme, we give explicit choices of the smoothing parameters and step sizes, together with corresponding complexity bounds. To our knowledge, these are the first non-asymptotic Rényi-divergence tracking bounds for Langevin dynamics with discrete target updates.
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