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

LabAgent：利用 AI Agent 定制科研中心以实现科学发现

---

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.

科学研究是一个强调传承的持续过程。前人开发的方法往往会被新研究人员进一步扩展，以探索更新颖、更深入的科学问题。然而，实验室人员的更替（如学生毕业）会导致缺乏能够复现这些方法的人员，使得投入大量精力和资源开发出的方法无法延续。

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.

为了解决这些局限性，我们提出了 LabAgent，这是一个专为实验室持续性工作而设计的复现与发现工具。LabAgent 采用了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，从而实现对类似错误的直接修正或规避。

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.

我们将 LabAgent 应用于生命科学领域的药物性质预测、生物医学问题分析、蛋白质变异效应预测以及统计遗传学研究。在每一个领域中，LabAgent 的表现均优于商业通用 Agent，排名第一，并成功实现了对已发表图表的精确复现。总而言之，这些结果表明 LabAgent 能够有效地整合并合理地扩展实验室的知识储备。