Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange

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.

• The paper introduces a novel framework for decentralized agent coordination using an extensible skill registry and artifact lineage DAG.
• It employs emergent need signals and a pressure-scored ArtifactReactor to trigger multi-agent synthesis without central planning.
• Empirical results in peptide design and materials discovery validate robust, auditable, and reproducible multi-domain scientific workflows.

Autonomous Distributed Discovery via Emergent Artifact Exchange: Architecture, Coordination Mechanisms, and Empirical Outcomes

Introduction

The paper "Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange" (2603.14312) presents a decentralized agent framework, ScienceClaw + Infinite, enabling persistent, plannerless scientific investigation. The central innovation is the transformation of autonomous, multi-domain agents into a collaborative ecosystem through an extensible, composable tool registry, explicit computational provenance (artifact-layer DAGs), and a machine-legible discourse and governance layer. This system operationalizes emergent coordination, distributed reasoning, and provenance-aware governance, supporting robust and auditable multi-agent science workflows.

System Architecture and Algorithmic Foundations

The ScienceClaw + Infinite framework comprises three fundamental components: an extensible skill registry (over 300 domain-specific, chainable scientific tools), an artifact system encoding computational lineage as a DAG, and a structured discourse platform for agent exchange, credit assignment, and provenance-aware governance.

Agents instantiate from declarative scientific personality profiles, guiding skill selection and pipeline construction. Tool invocation results in the creation of immutable artifacts—records containing not only outputs, but cryptographically addressable lineage linking parent and child operations (Figure 1). Agents embed explicit "need signals" in artifacts, specifying unsatisfied data/analysis requirements, which are broadcast to a global index for peer fulfillment.

Figure 1: ScienceClaw + Infinite system loop for agent skill invocation, artifact production, global need discvoery, and community- and agent-driven feedback.

Coordination eschews any centralized planner in favor of pressure-scored, emergent prioritization. The ArtifactReactor evaluates global needs using weighted novelty, centrality, DAG depth, and age. Coordination arises as multiple agents independently fulfill open needs (need-driven reactions) or as the reactor merges compatible outputs (schema-overlap/multi-parent synthesis), establishing explicit, multi-agent artifact lineage (Figures 3, 4).

Figure 2: Multi-agent artifact workflow: fulfillment of needs, cross-agent data propagation, and multi-parent synthesis with explicit parent tracking.

Figure 3: Decentralized ArtifactReactor workflow showing needs broadcasting, open need ranking, multi-agent fulfillment, and topology mutation for redundancy/conflict elimination.

A mutation layer induces topological adaptation—pruning redundancy, mitigating stagnation, and merging conflict—while persistent memory structures (journal, investigation tracker, knowledge graph) facilitate cumulative, cycle-spanning agent reasoning.

Emergent Coordination and Distributed Scientific Reasoning

Agents interact via Infinite, a provenance-governed platform encoding structured hypotheses, methods, results, and explicit artifact linkages. All discourse is machine-actionable; actions, votes, and typed post-linking (cite/contradict/extend/replicate) directly influence the exploration frontier via agent and community feedback.

Figure 4: Infinite data model with typed scientific posts, artifact back-links (provenance), and threaded agent/human engagement.

Governance incorporates layered incentive mechanisms—karma accrual, tiered privileges, and capability proofs—driving reproducibility and deterring spam or adversarial manipulation.

Figure 5: Governance and incentive feedback loop: engagement with evidence surfaces and provenance yields karma, closing the investigation cycle.

The architecture supports full investigation cycles, with minimal human intervention (redirect/chat actions for targeted steering). Heartbeat daemons trigger recurring agent reasoning and investigation updates, ensuring persistent, asynchronous workflow.

Case Studies: Quantitative and Qualitative Assessment

Peptide Design (SSTR2 Ligands)

In the ligand design campaign for SSTR2, agents independently explored the peptide design space using structure retrieval, evolutionary analysis, and sequence mutation fitness scoring (via ESM-2 LMs). Structural analysis mapped the K-T-C motif as the principal contact site, tightly conserved across evolutionary variants and highly intolerant to mutation according to language-model scores (Figures 7).

Figure 6: (A) SSTR2–peptide interaction fingerprint highlighting dominant K-T-C motif; (B) evolutionary conservation and LM-derived fitness, identifying mutational tolerance landscape.

Agents integrated sequence, structural, and physicochemical analyses, surfaced both improved candidate sequences and concrete design limitations (e.g., increased MW incompatible with approved drugs), and published the complete computational provenance and reasoning chains.

Workflow provenance is captured by integrating outputs from up to 23 computational tools, 177 unique artifact nodes, and dense multi-agent synthesis (57 synthesis ops across 10 agents, >30% synthesis density, Figure 7), all without central orchestration.

Figure 7: Protein design interaction network: multi-agent synthesis density and artifact-passing pathways through 23 computational tools.

Materials Discovery

Agents addressed a multi-constraint screening over 212 ceramic phases, integrating structure databases, elasticity tensors, thermodynamic stability, and Bayesian synthesis planning. The campaign yielded a set of seven stable, lightweight, superhard ceramics with outlier properties (e.g., B4C, B6O), plus previously unexplored, theoretically stable, boron-rich candidates (Figure 8). Full candidate filtering, ranking, and synthesis feasibility estimation relied on coordinated agent action, again evidenced by a high synthesis fraction and tight multi-agent workflow structure (Figure 9).

Figure 8: (A) Log-log density vs. bulk modulus: separation of outlier candidates; (B) predicted synthesis condition landscape for top materials.

Figure 9: Materials science agent interaction network: concentrated artifact flow and coordinated candidate refinement.

Cross-Domain Resonance

Thirteen agents mapped the resonance feature space of biological, engineered material, musical instrument, and Bach chorale systems. PCA-based embedding revealed distinct clustering: biological structures occupy high-hierarchy, high-periodicity space absent from material designs (Figure 10).

Figure 10: Agent-generated PCA resonance landscape: domain-specific clustering, biologically-inspired design gaps, and quantitative feature mapping.

Agents identified the unexplored latent space "gap," generated a novel, bio-inspired multi-scale ribbed membrane lattice (bridging biology and materials), and ran FEM modal analysis via JAX-FEM entirely autonomously (Figure 11). The resulting candidate achieves a modal density and frequency spectrum matching natural analogs, closing the design gap in a quantitative fashion.

Figure 11: FEM-derived modal analysis and validation of agent-designed hierarchical resonators.

Emergent workflows are captured via radial interaction networks with high parallelism (Figure 12).

Formal Cross-Domain Analogy Construction

A cohort of nine agents extracted and formalized analogy structures between urban street networks and grain boundary coarsening, synthesizing a six-rule L-system grammar (Figure 13) and validating its structural/topological correspondence through reduced concept networks (Figure 14, 18). This process demonstrates capacity for cross-domain symbolic hypothesis generation and explicit formalism, rather than simple candidate optimization.

Figure 14: Degree, betweenness, clustering comparison of urban vs. grain concept networks; statistical evidence for coarse structural similarity.

Figure 13: L-system rewrite grammar, executable as a generative model for both domains.

Dense hub-like synthesis dominates (48% synthesis density, Figure 15).

Discussion: Theoretical and Practical Implications

The empirical analyses show that emergent, decentralized agent coordination can generate non-redundant, multi-stage, and cross-domain scientific workflows. High synthesis densities and average DAG depths >2 across domains confirm that artifact-based coordination mechanisms obviate central planning while preserving critical dependency tracking, essential for scientific auditing and replication.

The platform foregrounds mechanism over heuristic shortcutting. Explicit artifact provenance, schema-based compatibility, and cross-agent credit assignment enable robust, non-circular knowledge accumulation and direct evaluation/auditability by both machines and human scientists.

From a practical viewpoint, ScienceClaw + Infinite enables rapid extension with new skills, domains, and agent personas, facilitating broader participation and agile method adoption. Platform-governed incentive structures allow for crowd moderation and reputation-based privilege assignment, mitigating breakdowns endemic to weakly governed multi-agent systems.

Theoretically, the architecture provides a concrete instantiation of distributed, cumulative science in silico—operationalizing Popper- and Kuhn-like process cycles at machine speed and scale, and serving as a substrate for persistent, crowd-sourced knowledge discovery. By establishing alignment between agent-internal epistemic progress and platform-level reward, the system minimizes emergent misalignment modes observed in less-governed swarms.

Limitations and Future Directions

Certain investigative modes (e.g., agent self-critique, mechanistic law extraction) are limited by the expressiveness of the underlying skill registry and the LM analysis engine. Extension toward more robust experimental design, automated error correction, simulation pipelines, and stronger RL-based meta-control remains ongoing. The artifact mutation layer may require additional adversarial stress-testing for pathological topologies in larger-scale deployments.

Expanding to more cross-domain analogical discovery (e.g., bio-material-Music-Urban co-design), as demonstrated with the analogy case study, represents a future direction for cumulative conceptual reasoning. Long-term, persistent ecosystem operation and human-in-the-loop steering experiments will further refine mechanisms for trust, argumentation, and sustained scientific value.

Conclusion

This work establishes a blueprint for auditable, persistent, multi-agent scientific investigation that is both plannerless and extensible. It codifies artifact-centric provenance as the fundamental coordination and credit mechanism, uses explicit need signals and schema overlap to induce distributed convergence, and realizes decentralized governance and incentivization natively at the platform layer.

By demonstrating reproducible, high-density synthesis across domains as disparate as protein design, materials discovery, resonance engineering, and cross-domain analogy construction, the ScienceClaw + Infinite ecosystem highlights the practical and theoretical feasibility of emergent crowdsourced machine science. This architecture is likely to serve as a foundation for next-generation systems capable of collaborative, open-ended, and interpretable autonomous discovery.