收录解读
Co-Scientist 把科学假设生成做成多 agent 系统：多个 agent 持续生成、批判、排序和改进假设，并通过 test-time compute scaling 提升假设质量。
系统面向研究目标和既有证据生成可实验验证的新假设；论文在药物重定位、新靶点发现和抗微生物耐药机制解释中验证，特别是 AML 候选药物和协同组合治疗经过体外实验确认。
它值得正式收录，因为这是 Nature 顶刊中对通用 AI co-scientist 架构、异步任务执行、tournament evolution 和真实生物医学验证的系统化报告。
它没有更高，是因为尽管具备范式意义，仍需要更多独立实验室复现、长期失败分析和跨学科验证来确认可靠性边界。
原始摘要与中文对照
中文对照翻译
利用Co-Scientist加速科学发现。科学发现是由科学家针对复杂问题提出新颖假设并经过严格实验验证所驱动的。为了增强这一过程，我们引入了Co-Scientist，这是一个基于Gemini构建的多智能体AI系统，用于结构化科学思维和假设生成。Co-Scientist旨在帮助科学家发现新的原创知识。在研究目标和现有科学证据的条件下，它能提出可证明的新颖研究假设以供实验验证。该系统的设计涉及智能体通过扩展测试时计算能力来持续生成、批判和完善假设。主要贡献包括：(1) 具有异步任务执行框架的多智能体架构，实现灵活的计算扩展；(2) 用于自我改进假设生成的锦标赛演化过程。自动化评估显示，测试时计算扩展持续带来益处，随着时间的推移提高了假设质量。尽管是通用目的，我们将其验证集中在三个生物医学应用中：药物再利用、新靶点发现以及解释抗菌素耐药性机制。具体而言，Co-Scientist帮助识别了急性髓系白血病的新药物再利用候选药物和协同联合疗法，这些均通过体外实验得到了验证。这些真实世界的验证表明Co-Scientist有潜力加速科学发现，并开启一个由AI赋能的科学家时代。
原始摘要
Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system’s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. While general purpose, we focus the validation in three biomedical applications: drug repurposing, novel target discovery , and explaining mechanisms of anti-microbial resistance . Specifically, Co-Scientist helped identify new drug repurposing candidates and synergistic combination therapies for acute myeloid leukemia, which were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI empowered scientists.