AI开始「想自己该怎么想」
让 AI 做科研，多数做法是拼命生成更多点子；这篇反过来，先让系统盯住自己「懂到哪、哪里没底、什么还没解决」，再据此决定下一步查什么、用哪套推理方法。它在 265 个跨十学科任务上八项指标全拿第一，总分 54.0 对最强基线 47.9。更硬的是落地：化学里把钴催化羰基化的产率从痕量提到 84%，光催化烯丙胺化从 13% 提到 69%，还有五个专家牵头的合作把提案接到了实验验证。它不是你明天能用上的工具，但它是「AI 从出主意走向真做出东西」这条路上一个值得记住的路标。
📄 原文摘要(英文)
We introduce Sage-Taiyi, a metacognition-inspired architecture that uses accumulated scientific understanding to guide what to investigate next and how to investigate it. Through explicit monitoring and control, the system revises its understanding of evidence, assumptions, and unresolved questions, then uses these assessments to adapt research targets and select reusable reasoning methods. On Taiyi-Bench, an AI co-scientist ideation benchmark comprising 265 tasks from 50 papers across ten disciplines, Sage-Taiyi ranks first on all eight metrics of historical rediscovery and open-ended ideation, achieving an Overall score of 54.0 compared with 47.9 for the strongest baseline, AI Scientist-v2. It also performs well on two external scientific ideation benchmarks. Five expert-guided collaborations further connect its proposals to experimental and computational validation across chemistry, materials science, and engineering. In chemistry, reaction-condition optimization raises the yield of cobalt-catalyzed carbonylation from trace amounts to 84% and photocatalytic allylic amination from 13% to 69%. These results support metacognitive regulation as an approach to scientific ideation and its progression toward practical validation.