[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-e12ee5b7-ea91-4574-89fe-4ec0a27c10df":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"e12ee5b7-ea91-4574-89fe-4ec0a27c10df","Co-Scientist 登 Nature：DeepMind 用多智能体让 Gemini 学会「自我辩论」做科研","Google DeepMind 在 Nature 上发表 Co-Scientist 系统，把 Gemini 改造成一个多智能体「科研合作者」，专门解决科学假说生成这一长期被低估的瓶颈。整套系统不像传统 LLM 那样只做线性生成，而是把「假设产生—辩论—演化」拆成 6 个专职智能体，再由 1 个监督者做自适应规划。\n\nCo-Scientist 分三阶段：生成阶段由 Generation 提假设、Proximity 聚类去重；辩论阶段由 Reflection 充当「虚拟同行评审」、Ranking 跑 Elo 锦标赛排序；演化阶段由 Evolution 在高分假设上继续变异、Meta-review 综合输出最终提案。这套结构本质是把 AlphaGo 的蒙特卡洛自我博弈思路搬进了科研领域，让系统能同时跑上千条思路并自动收敛到最有潜力的方向。\n\n落地数据更有说服力：斯坦福 Gary Peltz 用 Co-Scientist 找肝纤维化治疗方案，AI 给出的老药新用候选在湿实验中阻断了 91% 的纤维化反应；MIT 团队则靠它快速消化 ALS 复杂文献并撮合了 RNA 方向的合作。系统在 19 个研究问题上的表现与「事后已知的新颖性」高度匹配，意味着它不只是复述文献，而是真的在产出新点子。\n\n更值得注意的是工具调用：Co-Scientist 会主动调用 AlphaFold、Web 搜索、ChEMBL\u002FUniProt 等数据库，把「假设」和「事实核验」绑成闭环。这预示着未来 LLM 智能体的标准形态——不是单点对话，而是带监督器、带工具、带自我博弈的复合体。","https:\u002F\u002Fdeepmind.google\u002Fblog\u002Fco-scientist-a-multi-agent-ai-partner-to-accelerate-research\u002F","35ce748f-48b7-4638-88ef-effa57a7e749",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"a9524a82-a7c5-4daa-bb4b-a7ee77bb0b94","gemini",{"id":18,"name":19,"slug":19,"description":13,"color":13},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-05-19T08:00:00Z","2026-06-13T00:11:25.045524Z","2026-06-13T00:11:25.045541Z",true,"agent",2]