Paper List

期刊: ArXiv Preprint
发布日期: 2026-03-15
AI for ScienceBioinformatics

Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange

Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology | Department of Biological Engineering, MIT | Department of Mechanical Engineering, MIT | Department of Civil and Environmental Engineering, MIT | Department of Materials Science and Engineering, MIT | Center for Computational Science and Engineering, Schwarzman College of Computing, MIT

Fiona Y. Wang, Lee Marom, Subhadeep Pal, Rachel K. Luu, Wei Lu, Jaime A. Berkovich, Markus J. Buehler
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IN SHORT: This paper addresses the fundamental limitation of current AI-assisted scientific research by enabling truly autonomous, decentralized investigation where multiple AI agents coordinate without central planning through emergent artifact exchange.

核心创新

  • Methodology Introduces ArtifactReactor for plannerless coordination using pressure-based scoring (novelty, centrality, depth, age) to prioritize needs fulfillment and schema-overlap matching for multi-parent synthesis across independent analyses.
  • Methodology Implements a comprehensive artifact layer with immutable records, SHA-256 content hashes, and DAG-based provenance tracking that enables full computational lineage from raw outputs to published findings.
  • Methodology Develops a persistent ecosystem with autonomous mutation layer that actively prunes redundant workflows and resolves conflicts, plus persistent memory allowing agents to build upon complex epistemic states across multiple cycles.

主要结论

  • The framework successfully demonstrated heterogeneous tool chaining across 300+ interoperable scientific skills spanning biology, materials science, chemistry, and genomics domains.
  • Four autonomous investigations showed emergent convergence among independently operating agents, with cross-domain applications including peptide design for SSTR2 receptor and resonance bridging across biology, materials, and music.
  • The system enables traceable reasoning from raw computation to published findings through comprehensive provenance tracking in artifact DAGs, creating auditable scientific records with full computational lineage.
研究空白： Current AI systems in science remain fundamentally interactive and human-prompted, lacking true autonomy and emergent coordination capabilities for distributed scientific discovery without central planning.

摘要: 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.