工作流程
计算机科学
标杆管理
人工智能
一般化
自主代理人
建筑
自动计划和调度
智能代理
人工智能应用
人工生命
知识表示与推理
动作(物理)
数据科学
软件工程
多智能体系统
人机交互
作者
Kexin Huang,Serena Zhang,Hanchen Wang,Yuanhao Qu,Yingzhou Lu,Ryan Li,Yusuf Roohani,Lin Qiu,Shiyi Cao,G. L. Li,Junze Zhang,Di Yin,Rick P. Wierenga,Deniz Kavi,Liu S,Tianwei She,Shruti Marwaha,Jennefer N. Carter,Xin Zhou,Matthew Thomas Wheeler
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2026-07-09
卷期号:393 (6813): eadz4351-eadz4351
被引量:39
标识
DOI:10.1126/science.adz4351
摘要
Biomedical research is increasingly constrained by repetitive, fragmented workflows that slow discovery. We introduce Biomni, a general-purpose biomedical artificial intelligence agent that autonomously executes diverse research tasks. To map the biomedical action space, Biomni's action-discovery agent mines tools, databases, and protocols from thousands of publications across 25 domains, building a unified agentic environment. Its general-purpose architecture integrates large language model reasoning with retrieval-augmented planning and code-based execution, dynamically composing workflows without predefined templates. Systematic benchmarking shows strong generalization across heterogeneous tasks-causal gene prioritization, drug repurposing, rare-disease diagnosis, microbiome analysis, and molecular cloning-without task-specific tuning. Real-world case studies demonstrate Biomni interpreting multimodal datasets, optimizing protein stability, orchestrating wet-lab instruments, and generating experimentally testable protocols. Biomni envisions artificial intelligence augmenting human scientists and accelerating discovery.
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