mlsanityData to 14 Oct 2026

Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange

arXiv 2603.14312 ↗First posted 15 Mar 2026cs.AI cond-mat.dis-nn cs.LGcited by 11

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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

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  3. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  4. 2025The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Searchcited by 422
  5. 2025Accelerating scientific discovery with Co-Scientistcited by 401
  6. 2024Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiescited by 69
  7. 2025Robin: A multi-agent system for automating scientific discoverycited by 62
  8. 2022JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data sciencecited by 41
  9. 2023MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalitiescited by 38
  10. 2025Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networkscited by 12
  11. 2023Generative modeling, design and analysis of spider silk protein sequences for enhanced mechanical propertiescited by 9
  12. 2023AI for Science: An Emerging Agendacited by 5
3 more
  1. 2025Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validationcited by 5
  2. 2026BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoningcited by 4
  3. 2025Selective Imperfection as a Generative Framework for Analysis, Creativity and Discoverycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 11.

11 papers.