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LabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI Agents

Researchers proposed LabAgent, a reproduce-and-discovery harness that uses AI agents to preserve and extend a laboratory's research methods, according to an arXiv paper (2609.13437v1). LabAgent ranked first over commercial generalist agents in every tested life-science domain — drug property prediction, biomedical problem analysis, protein variant effect prediction, and statistical genetics — and accurately reproduced a published figure. The system is designed to address knowledge loss when lab staff such as graduating students leave without successors able to replicate established methods.

by read1 min views1 publishedSep 15, 2026

arXiv:2609.13437v1 Announce Type: new Abstract: Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods. Methods that have been developed with significant effort and resources cannot be continued. To address these limitations, we propose LabAgent, a reproduce and discovery harness tailored for a lab's continuous work. LabAgent employs two mechanisms to guarantee that all skills can be executed and verified and to record the corrective methods and experiences, allowing for direct correction or avoidance of similar errors. We applied LabAgent to drug property prediction, biomedical problem analysis, protein variant effect prediction, and statistical genetics in life science domains. LabAgent ranks first over commercial generalist agents in every domain, and demonstrates accurate reproduction of a published figure. Overall, these results demonstrate that LabAgent can effectively integrate and reasonably expand laboratory knowledge.

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