Paper List
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Nyxus: A Next Generation Image Feature Extraction Library for the Big Data and AI Era
This paper addresses the core pain point of efficiently extracting standardized, comparable features from massive (terabyte to petabyte-scale) biomedi...
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Topological Enhancement of Protein Kinetic Stability
This work addresses the long-standing puzzle of why knotted proteins exist by demonstrating that deep knots provide a functional advantage through enh...
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A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
This paper addresses the critical limitation of existing TF binding prediction methods that treat transcription factors as independent entities, faili...
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Social Distancing Equilibria in Games under Conventional SI Dynamics
This paper solves the core problem of proving the existence and uniqueness of Nash equilibria in finite-duration SI epidemic games, showing they are a...
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Binding Free Energies without Alchemy
This paper addresses the core bottleneck of computational expense in Absolute Binding Free Energy calculations by eliminating the need for numerous al...
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SHREC: A Spectral Embedding-Based Approach for Ab-Initio Reconstruction of Helical Molecules
This paper addresses the core bottleneck in cryo-EM helical reconstruction: eliminating the dependency on accurate initial symmetry parameter estimati...
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Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
This paper addresses the critical gap in evaluating AI-guided scientific selection strategies under realistic budget constraints, where existing metri...
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Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration
This paper addresses the core challenge of accurately decomposing shared (joint) and dataset-specific (individual) sources of variation in multi-modal...
Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange
Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology | Department of Biological Engineering, MIT | Department of Mechanical Engineering, MIT | Department of Civil and Environmental Engineering, MIT | Department of Materials Science and Engineering, MIT | Center for Computational Science and Engineering, Schwarzman College of Computing, MIT
30秒速读
IN SHORT: This paper addresses the fundamental limitation of current AI-assisted scientific research by enabling truly autonomous, decentralized investigation where multiple AI agents coordinate without central planning through emergent artifact exchange.
核心创新
- Methodology Introduces ArtifactReactor for plannerless coordination using pressure-based scoring (novelty, centrality, depth, age) to prioritize needs fulfillment and schema-overlap matching for multi-parent synthesis across independent analyses.
- Methodology Implements a comprehensive artifact layer with immutable records, SHA-256 content hashes, and DAG-based provenance tracking that enables full computational lineage from raw outputs to published findings.
- Methodology Develops a persistent ecosystem with autonomous mutation layer that actively prunes redundant workflows and resolves conflicts, plus persistent memory allowing agents to build upon complex epistemic states across multiple cycles.
主要结论
- The framework successfully demonstrated heterogeneous tool chaining across 300+ interoperable scientific skills spanning biology, materials science, chemistry, and genomics domains.
- Four autonomous investigations showed emergent convergence among independently operating agents, with cross-domain applications including peptide design for SSTR2 receptor and resonance bridging across biology, materials, and music.
- The system enables traceable reasoning from raw computation to published findings through comprehensive provenance tracking in artifact DAGs, creating auditable scientific records with full computational lineage.
摘要: 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.