Paper List
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Discovery of a Hematopoietic Manifold in scGPT Yields a Method for Extracting Performant Algorithms from Biological Foundation Model Internals
This work addresses the core challenge of extracting reusable, interpretable, and high-performance biological algorithms from the opaque internal repr...
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MS2MetGAN: Latent-space adversarial training for metabolite–spectrum matching in MS/MS database search
This paper addresses the critical bottleneck in metabolite identification: the generation of high-quality negative training samples that are structura...
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Toward Robust, Reproducible, and Widely Accessible Intracranial Language Brain-Computer Interfaces: A Comprehensive Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions
This review addresses the core challenge of fragmented and heterogeneous evidence that hinders the clinical translation of intracranial language BCIs,...
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Less Is More in Chemotherapy of Breast Cancer
通过纳入细胞周期时滞和竞争项，解决了现有肿瘤-免疫模型的过度简化问题，以定量比较化疗方案。
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Fold-CP: A Context Parallelism Framework for Biomolecular Modeling
This paper addresses the critical bottleneck of GPU memory limitations that restrict AlphaFold 3-like models to processing only a few thousand residue...
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Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
This paper addresses the core pain point of fragmented biomedical data by constructing and federating large-scale, open knowledge graphs to enable sea...
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Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data
This paper addresses the critical need for continuous, real-time monitoring of diabetic foot health by developing an unsupervised anomaly detection fr...
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Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics
This paper addresses the core challenge of achieving accurate, interpretable, and training-free nanoparticle counting in digital diagnostic assays, wh...
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.