Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange
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Abstract (arXiv)
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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 15 of its 45 references that are in the map. First posted on arXiv 15 Mar 2026.
Builds on
15 of 45 references.
References · 15
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3 more
- 2025Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validationcited by 5
- 2026BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoningcited by 4
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Built on by
Citing papers per year: 2026: 11.
11 papers.
- 2026The Last Human-Written Paper: Agent-Native Research Artifactscited by 16
- 2026Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligencecited by 4
- 2026Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombinationcited by 4
- 2026SwarmWorld: Stigmergic technological evolution in societies of language-model agentscited by 4
- 2026Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agentscited by 3
- 2026Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Modelcited by 1
- 2026AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questionscited by 1
- 2026Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidencecited by 0
- 2026Rethinking Scientific Discovery in the Agentic Eracited by 0
- 2026OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systemscited by 0
- 2026Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systemscited by 0
- 2026SwarmWorld: Stigmergic technological evolution in societies of language-model agents26 Aug 2026
- 2026AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions4 Aug 2026
- 2026Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems2 Aug 2026
- 2026Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model22 Jul 2026
- 2026Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents10 Jul 2026
- 2026Rethinking Scientific Discovery in the Agentic Era4 Jul 2026
- 2026Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination1 Jul 2026
- 2026Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence31 May 2026
- 2026Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidence21 May 2026
- 2026The Last Human-Written Paper: Agent-Native Research Artifacts27 Apr 2026
- 2026OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems16 Apr 2026