arXiv:2603.14312cs.AIcond-mat.dis-nn2026-03被引 13

多智能体自主协作发现,通过共享知识库和动态协调实现科学探索的可追溯演化。

Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange

  • 智能体基于技能库自主组合工具,通过计算谱系图追踪研究全过程。
  • 在4项独立研究中实现跨领域协同与结果收敛,关键信息需求自动匹配并合成。
  • 适合需要可复现、可审计科研流程的研究团队或自动化探索系统开发者。

我们提出ScienceClaw + Infinite框架,支持无中心协调的自主科学探索,任何贡献者均可部署新智能体进入共享生态系统。系统包含三个核心组件:超过300种可互操作的科学技能注册表、保留完整计算谱系的有向无环图(DAG)型产物层,以及具备溯源治理能力的智能体化科学对话平台。智能体根据科学画像选择并链式调用工具,生成带有类型元数据和父级谱系的不可变产物,并广播未满足的信息需求至全局索引。ArtifactReactor实现无规划者协调:同行智能体通过压力评分发现并满足开放需求,模式重叠匹配触发独立分析间的多父合成。自主突变层主动修剪扩展中的产物DAG,以解决冲突或冗余流程;持久记忆使智能体能在多轮迭代中持续构建复杂认知状态。Infinite将输出转化为可审计的科学记录,包含结构化帖子、溯源视图与机器可读的对话关系,社区反馈引导后续研究周期。在四项自主研究中——针对生长抑素受体SSTR2的肽设计、轻质抗冲击陶瓷筛选、跨领域共振桥接生物/材料/音乐,以及城市形态与晶界演化的形式类比构建——框架展示了异构工具链、独立运行智能体间的涌现共识,以及从原始计算到发表成果的可追溯推理过程。

原文摘要 · Abstract (English)

We present ScienceClaw + Infinite, a framework for autonomous scientific investigation in which independent agents conduct research without central coordination, and any contributor can deploy new agents into a shared ecosystem. The system is built around three components: an extensible registry of over 300 interoperable scientific skills, an artifact layer that preserves full computational lineage as a directed acyclic graph (DAG), and a structured platform for agent-based scientific discourse with provenance-aware governance. Agents select and chain tools based on their scientific profiles, produce immutable artifacts with typed metadata and parent lineage, and broadcast unsatisfied information needs to a shared global index. The ArtifactReactor enables plannerless coordination: peer agents discover and fulfill open needs through pressure-based scoring, while schema-overlap matching triggers multi-parent synthesis across independent analyses. An autonomous mutation layer actively prunes the expanding artifact DAG to resolve conflicting or redundant workflows, while persistent memory allows agents to continuously build upon complex epistemic states across multiple cycles. Infinite converts these outputs into auditable scientific records through structured posts, provenance views, and machine-readable discourse relations, with community feedback steering subsequent investigation cycles. Across four autonomous investigations, peptide design for the somatostatin receptor SSTR2, lightweight impact-resistant ceramic screening, cross-domain resonance bridging biology, materials, and music, and formal analogy construction between urban morphology and grain-boundary evolution, the framework demonstrates heterogeneous tool chaining, emergent convergence among independently operating agents, and traceable reasoning from raw computation to published finding.

多智能体科学发现可追溯性自主探索

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