LabAgent：利用AI智能体为科学发现定制任意研究枢纽
LabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI Agents
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- Yale University（耶鲁大学）
- Northeastern University（东北大学）
- Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所）
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对实验室人员流动导致方法难以延续的问题，提出LabAgent工具，通过技能执行验证与经验记录机制，在多个生命科学任务上超越商业通用智能体并准确复现图表。
中文摘要 AI 辅助
科学研究是一个强调继承的持续过程。前人开发的方法常常被新研究人员扩展，以探索更新颖、更深入的科学问题。然而，实验室人员的变动（如学生毕业）导致缺乏能够复现方法的人员。那些投入大量精力和资源开发的方法无法得以延续。为解决这些局限，我们提出了LabAgent，一个专为实验室持续工作定制的复现与发现工具。LabAgent采用两种机制来确保所有技能都能被执行和验证，并记录纠正方法和经验，从而允许直接纠正或避免类似错误。我们将LabAgent应用于药物性质预测、生物医学问题分析、蛋白质变异效应预测以及生命科学领域的统计遗传学。LabAgent在每个领域都优于商业通用智能体，排名第一，并展示了对已发表图表的准确复现。总体而言，这些结果表明LabAgent能够有效整合并合理扩展实验室知识。
英文摘要
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.
