Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange
🤖 AI Summary
This work addresses the challenge of enabling efficient, traceable collaboration among multiple agents in open scientific ecosystems without centralized coordination. The authors propose ArtifactReactor, a framework that leverages a decentralized skill registry, directed acyclic graph (DAG)-based artifact tracking, and provenance-aware governance to empower agents to autonomously select tools and generate auditable scientific records. A novel multi-source synthesis mechanism—driven by pressure scoring and pattern matching—is introduced, complemented by an autonomous mutation layer that dynamically refines the knowledge graph. This architecture facilitates emergent coordination across heterogeneous, cross-domain toolchains. Empirical evaluations in peptide design and ceramic screening demonstrate the system’s ability to achieve convergent collaboration among independent agents and support end-to-end traceable reasoning.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
autonomous agents
distributed discovery
scientific collaboration
artifact exchange
emergent coordination
Innovation
Methods, ideas, or system contributions that make the work stand out.
autonomous agents
artifact DAG
plannerless coordination
emergent synthesis
provenance-aware governance
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