Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange

• URL: http://arxiv.org/abs/2603.14312v1
• Date: Sun, 15 Mar 2026 10:06:57 GMT
• Title: Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange
• Abstract summary: ScienceClaw + Infinite is a framework for autonomous scientific investigation in which independent agents conduct research without central coordination.<n>The system is built around three components: an interoperable registry of over 300 scientific skills, an artifact layer that preserves full computational lineage, and a structured platform for agent-based scientific discourse.
• Score: 3.923443951480117
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery [48.99744520105086]
  SciForge is a multimodal research-native AI workbench.<n>It reserves the graphical interface for human judgment while search, parsing, model routing, workflow execution, plotting, writing, and presentation generation run as modular agent-accessible services.
  arXiv  Detail & Related papers  (2026-07-17T15:13:03Z)
• Biological Motifs for Agentic Control [0.0]
  Current agentic architectures are often constructed ad-hoc, prone to cascades, infinite loops, and prompt injection attacks.<n>This paper argues that many of these failure modes can be analyzed using control motifs long studied in systems biology.<n>We develop a typed interface correspondence between Gene Regulatory Networks and agentic software systems using functors and wiring diagrams.
  arXiv  Detail & Related papers  (2026-07-05T11:30:45Z)
• Rethinking Scientific Discovery in the Agentic Era [69.85050591628048]
  SCION is an agentic scientific operating system that acts as an textbf organizational nexus.<n>At its core is the textbfResearch Execution Plan (REP), which compiles high-level scientific intent into staged objectives, dependencies, verification checkpoints, tool requirements, expected artifacts, and fallback conditions.<n>We formulate discovery under SCION as textbfTarget-conditioned Inverse Search and extend it to hidden-target settings through batch active search under finite experimental budgets.
  arXiv  Detail & Related papers  (2026-07-04T13:09:33Z)
• An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping [0.0]
  We present an end-to-end agentic AI framework that turns a data factory into an interactive discovery platform.<n>A conversational Co-Scientist Agent translates a scientist's natural-language question into a structured analysis plan.<n>A headless Compute Agent dispatches Vision Transformer segmentation and trait extraction on the Frontier exascale supercomputer.
  arXiv  Detail & Related papers  (2026-06-30T15:37:02Z)
• Agents-K1: Towards Agent-native Knowledge Orchestration [59.22667222947257]
  textbfAgents-K1 is an end-to-end knowledge orchestration pipeline.<n>It converts raw documents into agent-native scientific knowledge graphs.<n>We process 2.46 million scientific papers across six subjects to produce textbfScholar-KG, of which we release a one-million-paper subset.
  arXiv  Detail & Related papers  (2026-06-11T17:58:35Z)
• Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems [65.90791804095561]
  Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination amplifies a less explored risk.<n>Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined.<n>This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement.
  arXiv  Detail & Related papers  (2026-05-14T14:36:13Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• From Agent-Only Social Networks to Autonomous Scientific Research: Lessons from OpenClaw and Moltbook, and the Architecture of ClawdLab and Beach.Science [0.0]
  OpenClaw and Moltbook produced a large-scale dataset of autonomous AI-to-AI interaction in January 2026.<n>This study conducts a multivocal literature review of that ecosystem and presents two complementary platforms for autonomous scientific research.
  arXiv  Detail & Related papers  (2026-02-23T13:10:01Z)
• OR-Agent: Bridging Evolutionary Search and Structured Research for Automated Algorithm Discovery [10.217363774023033]
  OR-Agent is a multi-agent research framework designed for automated exploration in rich experimental environments.<n>We introduce an evolutionary-systematic mechanism that unifies evolutionary selection of research starting points, comprehensive research plan generation, and coordinated exploration within a research tree.<n>We conduct experiments across classical optimization benchmarks-including traveling salesman, capacitated vehicle routing, bin packing, orienteering, and multiple knapsack problems-as well as a simulation-based cooperative driving scenarios.
  arXiv  Detail & Related papers  (2026-02-14T13:32:03Z)
• GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Design [0.9332987715848714]
  Large Language Models (LLMs) promise to accelerate discovery by reasoning across the expanding scientific landscape.<n>In materials science, where innovation demands integrating concepts from molecular chemistry to mechanical performance, this is especially acute.<n>We introduce a multi-agent framework guided by large-scale knowledge graphs to find sustainable substitutes for per- and polyfluoroalkyl substances (PFAS)-chemicals.
  arXiv  Detail & Related papers  (2026-02-07T10:50:34Z)
• Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science [70.3658845234978]
  Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS)<n>Despite this rapid progress, the field still relies heavily on empirical trial-and-error.<n>This bottleneck stems from the ambiguity of attribution.<n>We propose a factor attribution paradigm to systematically identify collaboration-driving factors.
  arXiv  Detail & Related papers  (2026-02-05T04:19:52Z)
• InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs [72.5651722107621]
  InterAgent is an end-to-end framework for text-driven physics-based multi-agent humanoid control.<n>We introduce an autoregressive diffusion transformer equipped with multi-stream blocks, which decouples proprioception, exteroception, and action to cross-modal interference.<n>We also propose a novel interaction graph exteroception representation that explicitly captures fine-grained joint-to-joint spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-08T10:46:01Z)
• Cross-Disciplinary Knowledge Retrieval and Synthesis: A Compound AI Architecture for Scientific Discovery [1.5143261755366868]
  BioSage is a novel compound AI architecture that integrates LLMs with RAG, orchestrated specialized agents and tools to enable discoveries across AI, data science, biomedical, and biosecurity domains.<n>Our system features several specialized agents including the retrieval agent with query planning and response synthesis that enable knowledge retrieval across domains with citation-backed responses.<n>Our ongoing work focuses on multimodal retrieval and reasoning over charts, tables, and structured scientific data, along with developing comprehensive multimodal benchmarks for cross-disciplinary discovery.
  arXiv  Detail & Related papers  (2025-11-23T05:33:11Z)
• Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation [41.659285482346235]
  We present texttfreephdlabor, an open-source multiagent framework featuring textitfully dynamic determined by real-time agent reasoning.<n>The framework provides comprehensive infrastructure including textitautomatic context compaction, textitworkspace-based communication to prevent information degradation, textitmemory persistence across sessions, and textitnon-blocking human intervention mechanisms.
  arXiv  Detail & Related papers  (2025-10-17T13:13:32Z)
• A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers [251.23085679210206]
  Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research.<n>This survey reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate.<n>We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge.
  arXiv  Detail & Related papers  (2025-08-28T18:30:52Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.