论文

本文提出 RARG（Relevance-Aware RipGrep Search Agent），将相关性转化为 corpus 交互的执行先验，并证明相关性感知交互可带来更快且更可靠的搜索收敛。The Relevance-Aware RipGrep Search Agent (RARG) is introduced, which turns relevance into an execution prior for corpus interaction, and demonstrates that relevance-aware interaction enables faster and more reliable search convergence.

本文综述了该边界上的五类漏洞：多步攻击链、与沙箱边界冲突的目标、供应链与凭据暴露、持续性的 command-and-control，以及自动化行动的速度。This review synthesizes five vulnerability classes at that boundary: multi-step offensive chains, objectives that conflict with sandbox boundaries, supply-chain and credential exposure, persistent command-and-control, and the speed of automated action.

CodeNib 是一个多视图数据系统，通过单一成本可见的运行时为智能体提供搜索、导航和有界上下文，并且是首个通过单一 manifest 和 source-address 契约编译同一 commit 的词法、稠密和结构视图的系统。CodeNib is a multi-view data system that serves search, navigation, and bounded context to agents through one cost-visible runtime, and it is the first to compile lexical, dense, and structural views of one commit behind a single manifest and source-address contract.

介绍 VisualPatchWorld，将世界动态表示为代码，先通过短时主动探查选择定性动力学形式，再通过最小化多步预测误差，从记录的状态-动作轨迹中拟合该形式的自由参数。VisualPatchWorld is introduced, which represents world dynamics as code and first selects a qualitative dynamical form with short active probes, then fits that form's free parameters from recorded state-action traces by minimizing multi-step prediction error.

介绍 HANDBOOK_md，一个包含 65 个 agentic 任务的 benchmark，模拟员工遵循公司手册的方式；每项任务对 10 份基础手册之一进行修改，变动评分所依赖的具体规则和阈值，因此没有任何两个任务共享同一套策略。HANDBOOK_md is presented, a benchmark of 65 agentic tasks modeled on how employees follow company handbooks, and every task modifies one of 10 base handbooks, altering the specific rules and thresholds on which grading depends, so no two tasks share the same set of policies.

一项受控的种子干预实验发现，与随机非 gold 上下文相比，由检索得到的初始上下文以更少的后续探索取得更高的文件 F1，而 oracle gold 上下文仍存在可观的提升空间。A controlled seed-intervention pilot finds that retrieval-derived initial context yields higher file F1 with less post-seed exploration than random non-gold context, while oracle gold context shows substantial remaining headroom.

本文提出 Metis，首个 memory foundation model 原型，赋予 foundation model 原生记忆能力，并表明原生记忆在架构、端到端优化和效率方面具有优势。This paper proposes Metis, the first prototype of memory foundation models, which empower foundation models with native memory capabilities and shows that native memory offers advantages in architecture, end-to-end optimization, and efficiency.

跨任务的测试时扩展实验表明，SkillRise 跨任务复用可迁移的 skill，而非受益于对同一任务的重复采样，在保持强性能的同时显著降低多阶段 skill 学习流水线的运行时开销。Scaling at test time across tasks suggests that SkillRise reuses transferable skills across tasks rather than benefiting from repeated sampling of the same task, and retains strong performance while substantially reducing the runtime overhead of skill learning pipelines with multiple stages.

提出 CAST（Credit Assignment from Solver Teachers），将游戏求解器状态价值的变化转换为求解器优势，并将其作为 turn 级信号注入 RLVR，在 ALFWorld 和 WebShop 上取得最高的平均 zero-shot 性能。CAST (Credit Assignment from Solver Teachers), which converts value changes in a game solver's state value into solver advantages and injects them into RLVR as turn-level signals and achieves the highest average zero-shot performance on ALFWorld and WebShop.

介绍 StealthBench，一个跨越六个 OPSEC 维度衡量自主攻击性安全 agent 操作隐蔽性的 benchmark，并以公共 benchmark 形式发布，以支持隐蔽感知 agent 的开发及自主攻击性安全部署中的自动化 OPSEC 监控。StealthBench, a benchmark that measures operational stealth in autonomous offensive-security agents across six operational security (OPSEC) dimensions, is introduced and released as a public benchmark to support both the development of stealth-aware agents and automated OPSEC monitoring for autonomous offensive-security deployments.

本文贡献包括：圣训学概念到多 agent 流水线的形式化映射、实现 claim 链和分级 narrator 注册表的关系模式、结合链等级与内容批评的决策矩阵，以及对真实物理教材中 20,000 条 claim 的评估。A formal mapping from hadith-science concepts to multi-agent pipelines, a relational schema implementing claim chains and a graded narrator registry, a decision matrix combining chain grade with content criticism, and an evaluation on 20,000 claims from real physics textbooks are contributed.

描述一个 memory store：agent 本地的 Neo4j 属性图，增强 HNSW 向量索引，并采用完整的双时态数据模型，支持时间点语义检索而无需物理覆盖历史。A memory store is described: an agent-local Neo4j property graph augmented with HNSW vector indexes and a full bitemporal data model that supports point-in-time semantic retrieval without physically overwriting history.

提出 SpecFirst，一个两阶段框架，在代码合成前强制进行需求 elicitation，并证明显式需求工程阶段是从零构建程序的有效范式。This work presents SpecFirst, a two-stage framework that forces the specification elicitation before code synthesis, and demonstrates that an explicit requirements-engineering phase is an effective paradigm for from-scratch program construction.

Voice Memory，一个面向 agentic 语音识别的纯推理方案：流式推理时，冻结 corrector 读取单一 per-domain memory，逐 utterance 决定是否作用于假设或弃权并保留 1-best，跨 corrector 族可迁移，推理路径不增加任何参数。Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best, and transfers across corrector families and adds zero parameters to the inference path.

提出 LedgerMind，配套三层 Grounding Protocol、一个按问题复杂度匹配推理深度的 Adaptive Dual-Path Dispatcher，以及一个具备形式化 provenance 非放大保证的事件触发验证与修复引擎，同时提升答案准确率与轨迹级忠实度。LedgerMind is introduced, augmented by a Three-Layer Grounding Protocol, an Adaptive Dual-Path Dispatcher that matches reasoning depth to question complexity, and an Event-Triggered Verification-and-Repair engine with a formal provenance non-amplification guarantee that improves both answer accuracy and trajectory-level faithfulness.

记忆是长时程 LLM 智能体的核心，但现有记忆系统主要保存交互内容，而未建模哪些智能体在何种条件下可信。这一局限在多智能体系统中尤为关键，因为中心模型可能无法直接验证来自对等方、看似合理或相关的响应。我们提出 Σ-Mem，一种在线可靠性记忆，记录单个对等方的历史能力证据以及跨对等集的对等关系证据。两种证据均以实对称状态形式维护，并基于后Memory is central to long-horizon LLM agents, yet existing memory systems primarily preserve interaction content rather than modeling which agents can be trusted and under what conditions. This limitation is particularly important in multi-agent systems, where a central model may be unable to directly verify plausible or correlated peer responses. We introduce Σ-Mem, an online reliability memory that records historical competence evidence for individual peers and peer relationship evidence across the peer set. Both forms of evidence are maintained as real symmetric states and updated from post

将文件系统的默认设置转化为 agent memory 的设计空间，证明模型并非塑造 store 形态的唯一杠杆：仅调整工具集即可以与更换模型相当的力度重塑 store。The study turns the filesystem default from an assumption into a design space for agent memory, and turns the model is not the only lever over a store's shape: changing the tool set alone reshapes the store as strongly as swapping the model.

提出 MisKnow-Agent，一个受控评测框架，通过可控的权威线索与来源风格构造支撑人工审核结论的任务文档，并采用报告级 false-conclusion 采纳率（仅统计认可错误结论的报告），基于三种 backbone LLM 评估 DeerFlow 与 WebThinker。MisKnow-Agent is introduced, a controlled evaluation framework that constructs task-specific documents supporting manually audited false conclusions with controlled authority cues and source styles that evaluates DeerFlow and WebThinker with three backbone LLMs using a report-level false-conclusion adoption rate that counts only reports endorsing the false conclusion.

本 tutorial 综述近年来面向对话式 AI 的神经方法，并综述 SOTA 神经方法，揭示神经方法与传统符号方法之间的联系。This tutorial surveys neural approaches to conversational AI that were developed in the last few years, and presents a review of state-of-the-art neural approaches, drawing the connection between neural approaches and traditional symbolic approaches.

研究表明，领域特定的 orchestration 可使缺乏专业团队的实验室也能实现可审计的预处理，并为其他科学领域的 AI agent 提供了可借鉴的设计原则。The results indicate that domain-specific orchestration can bring auditable preprocessing within reach of laboratories lacking dedicated expertise, illustrating design principles applicable to AI agents in other scientific domains.

本工作识别出记忆溯源洗白现象：基于LLM的记忆整合过程中，外部观察可能被改写为看似用户历史或工作流支持的内容，在保留动作触发的同时，抹去本应限制其权威性的低可信度来源。This work identifies memory provenance laundering: during LLM-based memory consolidation, an external observation may be rewritten as apparent user history or workflow support, preserving an action trigger while erasing the low-trust source that should limit its authority.

教育agentic工程师需要系统性变革而非增量式课程改革：教学必须从产出工件转向对日益自主的社会-技术系统进行判断。It is concluded that educating the agentic engineer requires systemic transformation rather than incremental curricular change: instruction must shift from producing artifacts to exercising judgment over increasingly autonomous socio-technical systems.

提出QWorld，用分位数-分位数匹配目标替代EP，直接将投影后的潜在样本与秩匹配的高斯分位数对齐，从而在尾部保持有效的修正梯度。QWorld is proposed, which replaces EP with a quantile-quantile matching objective that directly aligns projected latent samples with rank-matched Gaussian quantiles, thereby maintaining effective corrective gradients in the tails.

世界模型为规划和行动提供了预测性基础，但现有建模方式仅回答物理层面的问题：它是什么/在哪里，以及将如何演变。然而，人类行为由隐藏的心理状态驱动（一个人相信什么、想要什么、意图做什么、感受如何，以及认为在社会上何为可接受），因此仅追踪物理场景而忽略每个智能体所知与所信内容的模型，会对看起来正确的场景预测出错误的行动。我们将心理世界建模（MWM）形式化为一个通用理论框架，将心理变量作为世界模型的核心组成部分。World models enable a predictive substrate for planning and action, yet existing formulations merely answer a physical question: what/where it is, and how will it evolve. Human behavior, however, is driven by hidden mental state (what a person believes, wants, intends, feels, and considers socially permissible), so a model that tracks the physical scene but not what each agent knows and believes about it predicts the wrong action for the right-looking scene. We formulate Mental World Modeling (MWM), a generic theoretical framework that makes mental variables core components of a world model ra

提出EMBL AI Librarian，一个升级Europe PMC接口的知识层，面向AI agent，提升多项任务表现：文献综合、claim验证、开放域问答，以及下游生物学任务如protocol问题与序列操作。EMBL AI Librarian is introduced, a knowledge layer that upgrades the Europe PMC interface for AI agents that improves performance across a range of tasks: literature synthesis, claim verification, open-domain question answering, and downstream biology tasks such as protocol questions and sequence manipulation.

情感对话研究包含两种颇具影响力的策略传统。共情对话优先理解说话者的情绪体验；情感支持对话则选择并排序以满足求助者当前的需求。持续使用引入了更进一步的目标：有效的支持应在整个交互生命周期中维持用户进行情绪调节、应对、自我认同决策以及社会联结的能力。我们提出能力维持型情感对话（CSED）作为一种纵向研究范式，将支持策略与上述目标对齐，并...Emotional dialogue research includes two influential strategy traditions. Empathetic dialogue prioritizes understanding a speaker's emotional experience. Emotional support conversation selects and sequences support for the seeker's current needs. Sustained use introduces a further goal. Effective support should sustain users' capacities for emotion regulation, coping, self-endorsed decisions, and social connection across the interaction lifecycle. We propose capability-sustaining emotional dialogue (CSED) as a longitudinal research paradigm that aligns supportive strategy with this goal and or

将长视野执行重新表述为任务状态管理问题，提出LongHorizon-Harness，在执行外部显式维护任务状态，并仅用从环境中独立验证的事实更新它。This work reformulate long-horizon execution as a task-state management problem and proposes LongHorizon-Harness, which maintains the task state explicitly outside execution and updates it only with facts independently verified from the environment.

本工作提出 Skill-α，一种学习统一策略以进行渐进式 Skill 生成的强化学习方法，并引入一种新颖的回滚奖励，通过在锚定查询上比较原始技能与编辑后技能下的下游执行情况来评估每次编辑。This work proposesSkill-$\alpha, a reinforcement learning method that learns a unified policy for progressive skill generation and introduces a novel rollback reward that evaluates each edit by comparing downstream execution under the original and edited skills on an anchored query.

结果表明结构化 agent 记忆无需生成过去的中间表示，Zero-Mem 在消除记忆操作中 LLM 调用与 LLM-token 消耗的同时取得具有竞争力的性能The results show that structured agent memory need not generate an intermediate representation of the past, and Zero-Mem achieves competitive performance while eliminating LLM calls and LLM-token consumption from memory operations.

结果是面向稀疏 event-KV 服务的记忆契约：写入什么、落在何处、源消失后什么得以保留The result is a memory contract for sparse event-KV serving: what to write, where it lands, and what survives once the source is gone.

提出 PAST-Bench 基准，用于隔离评估持久 agent 从经验保留到系统性改进的能力；开发 Hermes+，提升了来自保留经验的平均增益，并提供更清晰的路径证据。The PAST-Bench benchmark is introduced, a benchmark designed to isolate how persistent agents can progress from retaining experience to systematically improving through it, and Hermes+ is developed, which raises the average gain from retained experience and provides clearer pathway evidence.

将 Agent-Centric Interactive World Proxies 概念化，将基础范式从物理状态转移转向 agent 可用的信息转移，如执行结果、检索到的经验或技能、以及验证信号，扩展了世界建模的范围，为持续改进的 agent 提供多样化反馈。This work conceptualize Agent-Centric Interactive World Proxies, shifting the fundamental paradigm from physical state transitions to agent-usable information transitions, such as execution outcomes, retrieved experiences or skills, and verification signals, broadening the scope of world modeling to provide versatile feedback for continually improving agents.

高粘度污渍以其高粘度与复杂流变特性，仍是机器人表面清洁的主要挑战。传统擦拭往往扩散污渍，而擦洗摩擦力更强却存在损伤表面的风险。本文提出 Push-Wiper，一种将高粘度污渍清洁重构为聚集问题的框架。Push-Wiper 使用海绵通过分段推送轨迹渐进式聚集污渍，随后通过后处理阶段剥离已聚集物质并实现海绵自清洁。我们采用逐步Viscous stains, characterized by high viscosity and complex rheological properties, remain a major challenge for robotic surface cleaning. Conventional wiping often spreads the stain, while scrubbing provides stronger friction but risks damaging the surface. In this paper, we propose Push-Wiper, a framework that reformulates viscous stain cleaning as an aggregation problem. Push-Wiper employs a sponge to progressively gather stains through segmented pushing trajectories, followed by a post-processing phase that detaches the aggregated material and enables sponge self-cleaning. We adopt a stepw

提出 ExplainBench，一个自动评估 coding agent 解释的基准，基于信息性解释应能让 LLM 正确回答问题的直觉，实现 agent 之间解释质量的量化比较。This work proposes ExplainBench, a benchmark to automatically evaluate explanations from coding agents, based on the intuition that informative explanations should enable an LLM to correctly answer questions, allowing quantitative comparison of explanation quality between agents.

提出 ContinualSkillBench，一个面向 in-context 持续 skill 学习的动态评估框架，表明当前 in-context skill 进化机制能够支持持续适应，但仍难以稳定地将经验整合为鲁棒且可迁移的 skill。ContinualSkillBench is introduced, a dynamic evaluation framework for in-context continual skill learning that shows that current in-context skill evolution mechanisms can support continual adaptation, but still struggle to consistently consolidate experience into robust and transferable skills.

PosterMELD 是一个模板条件的多 agent 流水线：capacity-aware slot 在渲染前引导写作，确定性 gate 与 VLM 审核将失败路由到有界修复，在生成的多种方法中获得最高的条件 CHE 并产出多个可印刷输出。PosterMELD is a template-conditioned multi-agent pipeline: capacity-aware slots guide writing before rendering, and deterministic gates plus vision-language model (VLM) review route failures to bounded repair result in the highest conditional CHE among generated methods with multiple print-ready outputs.