论文

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.

提出 Answer-Backtracked Credit Assignment（ABC），一种面向长程搜索 agent 训练的细粒度信用分配框架，将稀疏的轨迹级结果转换为稠密的步骤级监督，对有用动作（即使在失败轨迹中）给予奖励，同时抑制错误或冗余动作。Answer-Backtracked Credit Assignment (ABC) is proposed, a fine-grained credit assignment framework for training long-horizon search agents by converting sparse trajectory-level outcomes into dense step-level supervision that rewards useful actions (even in failed trajectories) while suppressing erroneous or redundant actions.

提出 FocusMem，在紧凑的潜空间记忆接口中分离情景记忆与工作记忆，一致优于完全匹配的动作-only 固定记忆基线以及先前的潜空间记忆适配方法。FocusMem is introduced, which separates episodic memory and working memory within a compact latent-memory interface and consistently outperforms a fully matched action-only fixed-memory baseline and prior latent memory adaptations.

开发 PIMiner，一种用于 prompt injection 红队的 agentic 系统，可在训练阶段从零构建策略库，并在测试时无需额外训练直接迁移到未见过的目标 LLM。PIMiner is developed, an agentic system for prompt injection red-teaming that builds a strategy library from scratch during training and can be directly transferred to a previously unseen target LLM without additional training at test time.

提出一种用于符号音乐的分层自监督"世界模型"，采用 2.55M 参数的 Swin V2 编码器，在 MIDI 钢琴卷帘图像上以 JEPA 风格目标（音高与时间平移等变性、掩码嵌入预测以及分布正则化）训练，无需标签与乐理词汇。A hierarchical self-supervised ``world model'' for symbolic music is presented, using a 2.55M-parameter Swin V2 encoder trained on MIDI piano-roll images with JEPA-style objectives (pitch- and time-shift equivariance, masked embedding prediction, and a distributional regularizer), using no labels and no music-theory vocabulary.

本综述旨在厘清自进化 coding agent 的概念边界，为设计更具适应性、可靠性与软件感知能力的 agentic 系统奠定基础。This survey aims to clarify the conceptual boundaries of self-evolving coding agents and provide a foundation for designing more adaptive, reliable, and software-aware agentic systems.

一个持久化执行状态、使运行可被中断、能在崩溃后存活并继续的框架，必须为已经触发的副作用界定"恢复"的含义。五种广泛部署的 Agent 工作流框架给出了不同答案，且均未公开可机器验证的契约，其行为甚至违背了它们自己声明的片段。RESUME CONTRACT 针对持久化 API 陈述了六项性质（前缀延续、副作用恰好一次、分支确定性、检查点有效性、消费一次、恢复确定性），并附加分支意图与活性义务。TLA+ 模型对参考语义进行了检验……A framework that persists execution state so a run can be interrupted, survive a crash, and continue must decide what a resume means for effects that already fired. Five widely deployed agent workflow frameworks answer differently, none exposes a machine-checkable contract, and behavior violates even the fragments they state. The RESUME CONTRACT states six properties over the persistence API (prefix continuation, effect exactly-once, fork determinism, checkpoint validity, consume-once, recovery determinism), plus fork-intent and liveness obligations. A TLA+ model checks a reference semantics e

EnvACE 是一种 Agentic 强化学习方法，用 world rehearsal 替代训练中的外部环境交互，将 world rehearsal 确立为突破外部环境约束、扩展 LLM Agent 训练的新路径。EnvACE is introduced, an agentic reinforcement learning method that replaces external environment interaction during training with world rehearsal, establishing world rehearsal as a new path toward scaling LLM agent training beyond the constraints of external environments.

CalibForge 是一个面向终端任务的自动合成系统，利用已验证的求解器行为，通过对抗式求解器校准来修订候选任务；消融实验表明，两种策略都比仅靠人工撰写加验证、或普通单求解器反馈产生更有效的监督信号。CalibForge is presented, an autonomous terminal-task synthesis system that uses verified solver behavior to revise candidate tasks through adversarial solver calibration and ablations show that both strategies yield more effective supervision than authoring and validation alone or ordinary single-solver feedback.

将软件侧密钥替换为硬件受限的密钥，通过厂商中立的 PKCS#11 接口访问，并由包含会话身份、作用域边界、语义验证、污点追踪和硬件执行边界的五层 Zero-Trust 执行栈提供保护。This work replaces software-resident keys with hardware-confined keys accessible through a vendor-neutral PKCS#11 interface, enabled by a surrounding five-layer Zero-Trust enforcement stack comprising session identity, scope bounds, semantic validation, taint tracking, and the hardware execution boundary.

提出构建经济世界模型（作为生成式引擎）的实施路线图：异质 Agent 在其中行动、交互、适应并与市场和制度共同演化，由此从内部生成经济动态。This paper develops an implementation roadmap for building economic world models as generative engines in which heterogeneous agents act, interact, adapt, and co-evolve with markets and institutions, thereby producing economic dynamics from the inside.

提出 ASGE-RR，一种支持可修订预留的在线 ASGE 控制器：该在线网络控制问题将运行时揭示的工作流调用映射到服务副本与网络路径上，受容量、成本和截止时间约束；研究表明，运行时揭示的工作流结构创造了新的网络控制机会。This work presents ASGE-RR, an online ASGE controller with revisable reservations, an online network-control problem that maps runtime-revealed workflow calls to service replicas and network paths under capacity, cost and deadline constraints and suggests that runtime-revealed workflow structure creates a new network control opportunity.

将一个确定性、无模型的流水线编译进 Agent 记忆：该流水线将本地采集流切分为类型化的活动帧与有界事件片段，携带应用、站点、时间、输入量以及回指原始行的证据指针，全程无模型参与。A deterministic, zero-model pipeline is compiled into agent memory with a deterministic, zero-model pipeline that segments a local capture stream into typed activity frames, bounded episodes carrying application, site, timing, input volume, and evidence pointers back to the raw rows, with no model in the loop.

提出 FactorJEPA，将世界结构作为一等预测原语，并通过 visibility gate 与分离的子空间来组合布局、实体与交互，以保留部分可观测的 Agent 并抑制跨因子捷径。FactorJEPA is introduced, which makes world structure a first-class predictive primitive, and composes layout, entities, and interactions, using a visibility gate and separated subspaces to preserve partially observed agents and discourage cross-factor shortcuts.

提出 PHOENIX（Predictive Health On-orbit Edge Neural Intelligence eXtension），为卫星赋予自主故障推理能力，并在 ESA Anomaly Detection Benchmark 上报告了初步结果。PHOENIX (Predictive Health On-orbit Edge Neural Intelligence eXtension) is proposed to give the satellite its own fault reasoning capability, and preliminary results on the ESA Anomaly Detection Benchmark are reported.

本文研究 11 项重大生活事件引发的人格变化，以大五人格作为心理测量锚点，并将所得轨迹与人类人格心理学的纵向证据进行对照，指出当前 PC-Agents 模拟了人类人格动态的均值，但未能模拟其形态。This work studies event-induced personality change after 11 major life events, using the Big Five traits as a psychometric anchor and interpreting the resulting trajectories against longitudinal evidence from human personality psychology, and suggests that current PC-Agents simulate the mean of human personality dynamics, but not its shape.

实验表明，对敏感信息的不必要获取广泛存在，并观察到任务完成能力与获取阶段泄露之间的相关性，因此对获取阶段隐私进行审计既紧迫也必要。The experiments show that the unnecessary acquisition of sensitive information is widespread, and a correlation between the task-completion capability and acquisition-stage leakage, and auditing acquisition-stage privacy both urgent and necessary is observed.

SMRC-SD（State-Matched Routing and Contextualized Self-Distillation）显式地决定特权轨迹应在何时、以何种方式指导 on-policy student，其表现始终优于无条件的成功全路径蒸馏。State-Matched Routing and Contextualized Self-Distillation (SMRC-SD), which explicitly determines when and how a privileged trajectory should guide an on-policy student, consistently outperforms unconditional successful full-path distillation.

我们提出 Ouroboros，一个自进化的 Agent harness，其工具、提示、上下文组装与核心实现通过已评审的 commit 持续改进，并成为后续工作的运行时。核心演化以两种模式推进：在递归自由演化中，改进本身就是任务，完成一个演化周期即可调度下一周期；在经验驱动核心演化中，常规工作和社交交互暴露的 bug、粗糙之处及低效上下文构造会引发已评审的结构变更。在 Terminal-Bench 2.1 上，Opus 5 运行取得 86.74% 的得分，为该基准报告的最佳结果。We present Ouroboros, a self-developing agent harness whose tools, prompts, context assembly, and core implementation improve through reviewed commits that become the runtime for later work. Core evolution proceeds in two modes. In recursive free evolution, improvement is itself a task, and completing one evolution cycle can schedule the next. In experience-driven core evolution, ordinary work and social interaction expose bugs, rough edges, and inefficient context construction that lead to reviewed structural changes. On Terminal-Bench 2.1, an Opus 5 run scores 86.74%, the best result reporte

本文提出 Agent Memory Distillation (AMD)，一个无需训练、通过分层记忆将结构化知识从大型教师智能体迁移到小型学生智能体的框架，一致优于现有基于记忆的基线方法。Agent Memory Distillation (AMD), a training-free framework that transfers structured knowledge from a large teacher agent to a small student agent through hierarchical memory, is proposed, which consistently outperforms existing memory-based baselines.

所提架构通过提供模块化、面向实现的具身认知能力 IVA 部署框架，弥合高层智能体推理模型与实时具身执行之间的鸿沟，助力在复杂交互虚拟环境中构建可扩展、自适应且可解释的智能体。The proposed architecture contributes by providing a modular, implementation-oriented framework for the deployment of embodied, cognitive-capable IVAs and bridges the gap between high-level agent reasoning models with real-time embodied execution, for scalable, adaptive, and explainable agents in complex interactive virtual environments.

本文提出 WeClawArena，一个面向个人工作空间多参与方 owned-agent 协作的可审计基准与运行时沙盒，基于有界运行时证据审计攻击成功情况，支持任务分解失败、隐私泄露、证据投毒以及权限路径失效等问题的诊断。WeClawArena is introduced, an auditable benchmark and runtime sandbox for multi-party owned-agent collaboration over personal workspaces and audits attack success from bounded runtime evidence, supporting diagnosis of task breakdown, privacy leakage, poisoned evidence, and invalid authority paths.

VeriForge 是一种混合主动写作系统，通过划分认知劳动，使系统在领域发现上承担主动权，而作者保留对叙事合成的完全主动权；在受控的冷启动写作任务中，专家评审者认为其产出在领域扎根方面更强。VeriForge is a mixed-initiative writing system that divides cognitive labor so that the system assumes initiative over domain discovery while the author retains full initiative over narrative synthesis, and is perceived by expert raters to produce passages with stronger domain grounding in a controlled cold-start writing task.

本文提出 Business Arena——一个受控环境，AI agent 在其中经营跨境店铺，在长周期内向供应商采购并向买家销售，迈出了构建面向端到端商业 agent 的真实可信测试床的第一步。Business Arena, a controlled environment where an AI agent runs a cross-border shop, buying from suppliers and selling to buyers over a long horizon, is introduced, taking a first step toward a realistic and trustworthy testbed for evaluating end-to-end business agents.

本文提出 Combodied Agents——一种以人为中心的范式，借助软件工具、传感器、可穿戴设备、机器人与人工服务作为行动通道而非终极目标，在时间维度上感知、建模、预测并支持个体的人体状态轨迹。Combodied Agents is introduced, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time, using software tools, sensors, wearables, robots, and human services as action channels rather than end goals.

结果表明，剪枝效果更取决于剪枝应用的位置，而非具体的评分规则：早期剪枝带来最大的端到端节省，后期剪枝主要用于细化最终的合成上下文。The results show that pruning effectiveness depends more on where pruning is applied than on the specific scoring rule: early pruning yields the largest end-to-end savings, while later pruning mainly refines the final synthesis context.

本文提出 InSight-doc，一种智能体视觉感知框架，将视觉分辨率视为一种自适应的推理时资源，从低分辨率起步，选择性地放大高分辨率区域以获取更细粒度的证据，且不依赖任何外部检索器。This work proposes InSight-doc, an agentic visual perception framework that treats visual resolution as an adaptive reasoning-time resource that starts from low resolution and selectively zooms into high-resolution regions for finer evidence, without relying on any external retriever.

尽管方法简单，Decision Transformer 在 Atari、OpenAI Gym 和 Key-to-Door 任务上达到或超过 SOTA 无模型离线 RL 基线的性能Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.

本文提出连续性核 (Continuity Kernel, CK)，一种激活契约，将提交前候选评估与原子状态激活解耦，将连续性定义为已接受分支头的连续且经过授权的谱系。The Continuity Kernel (CK), an activation contract that decouples off-commit candidate evaluation from atomic state activation from atomic state activation is presented, defining continuity as an unbroken, authorized lineage of accepted branch heads.

结果表明，长周期软件开发可以围绕持久化项目而非持久化智能体来组织；本文提出 EvoX Genesis，使软件项目保持持久，同时允许局部智能体保持有限生命周期。Results show that long-horizon software development can be organized around a persistent project rather than a persistent agent, and EvoX Genesis is introduced, which instead makes the software project persistent while allowing local agents to remain finite-lived.