论文

结果显示，规约规模本身并不能预测实现质量，跨 Agent 迁移可能导致显著的、依赖具体 Agent 的性能下降；因此在异构 SDD 工作流中，不应将规约默认视为与 Agent 无关的工件（artifact）。The results show that specification size alone does not predict implementation quality and that cross-agent transfer can produce substantial agent-dependent degradation, and suggest that specifications in heterogeneous SDD workflows should not automatically be treated as agent-neutral artifacts.

PhysCaP 在 code-as-policy 框架上增加了物理信息驱动的探索层，使其能够通过交互进行显式的信息获取，并引入免训练的物理属性提取模块，仅凭机器人本体感知即可估计物体质量与刚度，无需额外传感器。PhysCaP augments code-as-policy frameworks with a physics-informed exploration layer that enables explicit information-seeking through interaction, and introduces training-free physical property extraction modules that estimate object mass and stiffness from robot proprioception without additional sensors.

本文提出 ARC（Advantage Regularization via Conditioning），一种通过策略条件化 rollout 分组来恢复更公平的相对比较、并结合混合奖励与熵正则化的训练方法。The proposed ARC (Advantage Regularization via Conditioning), a training recipe that restores fairer relative comparison through strategy-conditioned rollout grouping, together with hybrid rewards and entropy regularization, is proposed.

本文提出 Knowledge Triage 框架，对 agent 知识库的每一行按类型分类，并为每种类型配置独立的保留策略，同时开源发布 AgentArtifactCorpus 数据集、分类器及参考实现。Knowledge Triage, a framework that classifies each line of an agent's knowledge base by type and routes each type through its own retention policy, is addressed, and AgentArtifactCorpus, the classifier, and the reference implementation are released.

介绍 AutoResearch，一个连接 Idea Generation 与 Idea Execution 的两阶段系统，分别解决研究思路如何形成与如何通过实验可靠验证的问题，展示「实验前先夯实洞见、接受前先夯实结论」的研究流程。AutoResearch is introduced, a two-stage system that connects Idea Generation with Idea Execution to address both how research ideas are formed and how they are reliably established through experimentation to demonstrate a research process in which meaningful insight is grounded before experimentation and conclusions are grounded before acceptance.

AtlasNav 减少了 Evidence Blindness，更早实现完整证据，在 PhantomWiki 上对语料结构与规模变化保持鲁棒，并在异构企业数据上取得领先性能。AtlasNav reduces Evidence Blindness, reduces Evidence Blindness, realizes complete evidence earlier, remains robust to corpus-structure and scale shifts on PhantomWiki, and achieves leading performance on heterogeneous enterprise data.

介绍 CyberFactory，一个统一的开源框架，贯通 PoC 生成、漏洞修补与 CyberQA 三大任务中的数据构建、轨迹合成与模型训练。CyberFactory is introduced, a unified open-source framework that connects data construction, trajectory synthesis, and model training across proof-of-concept (PoC) generation, vulnerability patching, and cybersecurity question answering (CyberQA).

介绍 DREAM，一种自主优化控制架构：在不替换现有流水线的前提下叠加感知可感知、可编排、可审计的策略层，支持将 agentic meta-control 作为工业推荐的一种可行范式。This work presents DREAM (Developing Recommender Engine with Agentic Methods), an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them, supporting agentic meta-control as a viable paradigm for industrial recommendation.

本文指出 LLM agent 中信息抽取与动作之间的 state-transmission 失效，并展示 handoff 变换如何在保留状态内容的同时削弱其对下游动作的约束。This work identifies a state-transmission failure between information extraction and action in large language model agents, and shows how handoff transformations can retain state content while weakening its constraints on downstream action.

AgentRoom 是一种面向并发编码 Agent 的实时协同编辑协议，通过在 CRDT 合并的共享文件系统上将文件级 claim、status 和 broadcast 暴露为 MCP 工具，且运行间的差异小于 CLI-stable 模型。AgentRoom is a realtime collaborative editing protocol for concurrent coding agents that exposes file-level claim, status, and broadcast as MCP tools on a CRDT-merged shared filesystem and has less run-to-run variation than CLI-stable models.

The Station 被评估为一个开放世界多 agent 环境，不同模型家族的 AI agent 在其中无需中心协调器或脚本化流程即可共同追求同一研究目标，并提供发现产生过程的透明记录。The Station is evaluated, an open-world multi-agent environment in which AI agents from different model families pursue a shared research goal without a central coordinator or scripted pipeline, providing a transparent record of how discoveries emerged.

本文提出 Secure On-Policy Distillation (SecOPD)，提供 token 级反馈以指导防御性微调，并能泛化到训练中完全未见过的领域。This paper proposes Secure On-Policy Distillation (SecOPD) that provides token-level feedback to guide defensive fine-tuning, and generalizes to domains completely unseen in training.

本文变化 handoff 方向、时机与接口，对比保留仓库状态下的全轨迹传输、压缩与轨迹移除，发现偏好接口随方向反转：减少 LC-model 轨迹信息可提升 escalation 质量，而移除 HC-model 轨迹则会降低 downshift 质量。This work varies handoff direction, timing, and interface, comparing full-trajectory transfer, compaction, and trajectory removal while preserving the repository state, and finds that the preferred interface also reverses with direction: reducing LC-model trajectory information improves escalation quality, whereas removing the HC-model trajectory reduces downshift quality.

本工作提出 QBugLM，一个多 Agent 框架，可自动化量子软件调试流水线，覆盖基于分类法的缺陷注入、基于 LLM 的检测与修复，直至基于仿真的验证，框架无关地支持 OpenQASM 3.0 程序。This work proposes QBugLM, a multi-agent framework that automates the quantum software debugging pipeline, from taxonomy-driven bug injection to LLM-based detection and repair, and finally to simulation-based validation, for framework-agnostic OpenQASM 3.0 programs.

本文提出 PILOT，一种通过两种耦合机制实现实时自我改进的 supervisor-worker 框架：(1) live steering 允许独立的 supervisor 在执行期间重定向或中止当前 worker；(2) live self-evolution 将执行中发现的过程与失败模式提炼为可复用的 skills 与记忆。PILOT is presented, a supervisor-worker harness for live self-improvement through two coupled mechanisms: (1) live steering lets a separate supervisor redirect or abort the active worker during execution; and (2) live self-evolution distils procedures and failure modes revealed during execution into reusable skills and memory.

本文提出 Reinforcement Learning with Human-Engine Verification（RLHEV），一种结合稠密引擎信号与开发过程中隐式人类接受反馈的后训练范式，用于支持强化学习后训练。This work proposes Reinforcement Learning with Human-Engine Verification (RLHEV), a post-training paradigm that combines dense engine signals with implicit human acceptance feedback from the development process to support RL post-training.

PLENA 是一个软硬件协同设计的系统，采用三条核心优化路径，具备新颖的扁平化 systolic-array 架构以及支持非对称量化方案的高效计算与存储单元（路径 2）。PLENA is a hardware-software codesigned system that applies three core optimization pathways that features a novel flattened systolic-array architecture and efficient compute and memory units that support an asymmetric quantization scheme (Pathway 2).

本文提出 DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation)，一种将范式从全局 forcing 转向拓扑引导的局部修正的新框架，相对传统 full-trajectory 基线取得显著提升。This work proposes DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation), a novel framework that shifts the paradigm from global forcing to topology-guided localized correction, and significantly outperforms traditional full-trajectory baselines.

结果表明，可靠的 Agent 自我进化需要协同设计验证、状态接地、见证语义和恢复语言表达能力，而非仅依赖迭代提示。The results indicate that reliable agent self-evolution requires co-designing verification, state grounding, witness semantics, and recovery-language expressivity rather than relying on iterative prompting alone.

Pera 描述了一种持久化 Agent，围绕感知和控制组件组织，持续从情景任务执行、上下文及周围环境变化中感知服务相关信号，并利用这些信号构建生命周期任务。Pera describes a persistent agent organized around perception and control components that continually perceive service-relevant signals from episodic task executions, internal context, and changes in the surrounding environment, and use these signals to construct lifecycle tasks.

GLM-5 在真实编码任务中展现出前所未有的能力，在端到端软件工程挑战的处理上超越既有基线，并提出了新颖的异步 Agent RL 算法，进一步提升了 RL 质量。GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges and proposing novel asynchronous agent RL algorithms that further improve RL quality.

本工作在采购、网络安全与金融场景下评估了 5 个 LLM 作为记忆写入者、2 个 LLM 作为执行者，并提出 EAL-Bench，用于衡量持久记忆在多大程度上准确保留不断演化的授权状态，以及错误是否会向下游传播为未授权操作。This work evaluates five LLMs as memory writers and two as executors across procurement, cybersecurity, and finance and introduces EAL-Bench, which measures how accurately persistent memory preserves evolving authorization state and whether errors propagate to downstream unauthorized actions.

本文提出 FoldingAgent，一个从折纸演示视频中推断显式参数化折纸程序的 Agent 框架，利用预训练 Vision-Language Model 的推理能力，并配备可模拟几何变换、验证物理合理性、检索与比较视觉内容以及评估自身预测的专用工具集。FoldingAgent is presented, an agentic framework for inferring explicit parametric folding programs directly from origami demonstration videos that leverages the reasoning power of a pre-trained Vision-Language Model equipped with a suite of specialized tools that enable the agent to simulate geometric transitions, verify physical plausibility, retrieve and compare visual content, and evaluate its own predictions.

本教程综合了推动这些汇聚方向的算法、系统与设计原则，为数据科学与数据挖掘研究者提供统一视角，涵盖将 LLM、图数据管理、图挖掘、图 ML 与 agentic 计算融合到下一代 graph-native AI 系统中。This tutorial synthesizes the algorithms, systems, and design principles driving these converging directions, offering data science and data mining researchers a unified perspective on integrating LLMs, graph data management, graph mining, graph ML, and agentic computation into next-generation graph-native AI systems.

证明了一个应用层定理：若用户在 UART 控制台输入 echo hello world，系统唯一能产生的输出即为 hello world；这证明了基于 LLM 的 Agent 能够对如此底层的细节进行推理。An application-level theorem is proved: if the user types echo hello world as input on the UART console, the only output the system can produce is hello world, which proves LLM-based agents are capable of reasoning about such low-level details.

该工作提出 AutoTraceGT（Automated Trace analysis through Grounded Theory），首个在 Agent 轨迹上自动化 grounded theory 的多 Agent pipeline，并指出 Grounded Theory 为研究 Agent 实际行为的 ML 研究者和 Agent 开发者提供了可扩展的分析工具。This work proposes AutoTraceGT (Automated Trace analysis through Grounded Theory), the first multi-agent pipeline that automates grounded theory on agent trajectories and suggests Grounded Theory offers a scalable analytic tool for ML researchers and agent developers studying what agents actually do.

该工作提出 DRACO：Distributing Rubric-based Advantage for Credit Optimization，在训练期间动态生成 rubric 以追踪 policy 的演化能力，对已完成的轨迹一次性评分，并将该判断重新分配到负责标注 rubric 的步骤上，以在 GRPO 中产生差异化的 per-step advantage。This work proposes DRACO: Distributing Rubric-based Advantage for Credit Optimization, which generates rubrics dynamically during training to track the policy's evolving capability, scores those rubrics once per completed trajectory, and redistributes that judgment over the steps responsible for annotated rubrics to produce differentiated per-step advantages in GRPO.

提出 VeriPhy，一种可审计的物理验证系统，其中纯文本 planner 在观察任何帧之前将 prompt 编译为类型化的物理义务与静态验证的执行计划。VeriPhy, an auditable physical-verification system in which a text-only planner compiles the prompt into typed physical obligations and a statically validated execution plan before any frame is observed, is presented.

该工作提出了 MaxKernel，一个多 Agent 系统，实现了 TPU kernel 开发的 three distinct paradigms：Human-in-the-Loop (HITL) Agent，用于协作式分步设计；Autonomous (Auto) Agent，执行全自动、由指标和 trace 驱动的优化循环；以及 Graph-Based Autonomous Search，将 Auto Agent 扩展以对设计空间进行全局探索。This work presents MaxKernel, a multi-agent system that implements three distinct paradigms for TPU kernel development: a Human-in-the-Loop (HITL) agent for collaborative, step-by-step design; an Autonomous (Auto) agent that executes a fully automated, metric/trace-driven optimization loop; and a Graph-Based Autonomous Search that scales the Auto agent for global exploration of the design space.

本文认为，AI agent——即以大语言模型作为主要推理引擎、动态生成与丢弃代码作为工具性资源的系统——的出现构成了对"软件"本身的根本性重构，而非渐进式的工具改进。This paper argues that the emergence of AI agents -- systems where large language models serve as the primary reasoning engine, dynamically generating and discarding code as an instrumental resource -- constitutes a fundamental restructuring of what software is, not an incremental tool improvement.

本文提出 Dr. Claw，一个开源工作空间，将现有编码 Agent 可执行文件封装在可控且可审计的人机协同工作流中，而非引入另一个自主 Agent。Dr. Claw is presented, an open-source workspace that wraps existing coding-agent executors in a controllable and auditable human-in-the-loop workflow rather than introducing another autonomous agent.

本文提出 Memanto，一种面向 agentic 人工智能的通用记忆层，挑战了"必须依赖知识图谱复杂度才能实现高保真 agent 记忆"的普遍假设，并取得 SOTA 准确率。Memanto is introduced, a universal memory layer for agentic artificial intelligence that challenges the prevailing assumption that knowledge graph complexity is necessary to achieve high fidelity agent memory and achieves state of the art accuracy scores.

主张防御的运行单元应是可修订的协同 episodes，将观察到的迁移、任务权限与响应历史关联起来，并提出跨执行监控建议具有可测试性，但并不声称提出新的检测器或测得具体的遏制收益。It is argued that the operational unit of defence should be a revisable coordination episode linking observed transfers, task authority, and response history, and it makes the recommendation to monitor across executions testable without claiming a new detector or a measured containment benefit.

Glyph 是一个生产系统，将列描述生成与列类型标注这两个耦合问题建模为协同工作的 LLM Agent，并以有状态图形式编排，使多 Agent LLM 目录编制可审计且可作为生产服务运行。Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs, makes multi-agent LLM cataloging auditable and operable as a production service.

评估 AI Agent 能否作为科学家利用 SAE 工具开展自主机理发现，旨在将实验性模型理解确立为可测量的能力，推动闭环自主 AI R&D。Whether AI agents can act as scientists utilizing SAE tools for autonomous mechanistic discovery is evaluated to establish experimental model understanding as a measurable capability for closed-loop autonomous AI R&D.

Discovery Certification Protocol（DCP）将结果声明转化为在已注册模型、信息边界与预算下的可执行审计，并由确定性验证器基于冻结记录复现本地决策The Discovery Certification Protocol (DCP) turns an outcome claim into an executable audit under a registered model, information boundary, and budget, and a deterministic verifier reproduces these local decisions from frozen records.