# Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange arXiv: https://arxiv.org/abs/2603.14312 ยท HTML: https://mlsanity.com/paper/2603.14312/ ## 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 (most cited first, 15 of 15) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024) - [The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search](https://mlsanity.com/paper/2504.08066/) (2025) - [Accelerating scientific discovery with Co-Scientist](https://mlsanity.com/paper/2502.18864/) (2025) - [Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities](https://mlsanity.com/paper/2409.03444/) (2024) - [Robin: A multi-agent system for automating scientific discovery](https://mlsanity.com/paper/2505.13400/) (2025) - [JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science](https://mlsanity.com/paper/2212.00964/) (2022) - [MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities](https://mlsanity.com/paper/2310.10445/) (2023) - [Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks](https://mlsanity.com/paper/2502.13025/) (2025) - [Generative modeling, design and analysis of spider silk protein sequences for enhanced mechanical properties](https://mlsanity.com/paper/2309.10170/) (2023) - [AI for Science: An Emerging Agenda](https://mlsanity.com/paper/2303.04217/) (2023) - [Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation](https://mlsanity.com/paper/2511.22311/) (2025) - [BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning](https://mlsanity.com/paper/2603.04124/) (2026) - [Selective Imperfection as a Generative Framework for Analysis, Creativity and Discovery](https://mlsanity.com/paper/2601.00863/) (2025) ## Built on by (most cited first, 11 of 11) - [The Last Human-Written Paper: Agent-Native Research Artifacts](https://mlsanity.com/paper/2604.24658/) (2026) - [Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence](https://mlsanity.com/paper/2606.01444/) (2026) - [Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination](https://mlsanity.com/paper/2607.00924/) (2026) - [SwarmWorld: Stigmergic technological evolution in societies of language-model agents](https://mlsanity.com/paper/2608.26081/) (2026) - [Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents](https://mlsanity.com/paper/2607.09195/) (2026) - [Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model](https://mlsanity.com/paper/2607.20058/) (2026) - [AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions](https://mlsanity.com/paper/2608.03283/) (2026) - [Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidence](https://mlsanity.com/paper/2605.22300/) (2026) - [Rethinking Scientific Discovery in the Agentic Era](https://mlsanity.com/paper/2607.03863/) (2026) - [OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems](https://mlsanity.com/paper/2607.28629/) (2026) - [Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems](https://mlsanity.com/paper/2608.14667/) (2026) ## 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.