你有没有想过，一个AI不仅能成为科学家的“超级大脑”，还能像人一样“反思”自己学得好不好？本期节目，我们将从五篇最新的AI论文出发，揭秘AI如何通过“人机协作”加速科学发现，却又可能因为追求“差不多”而酿成大错；同时我们也会探讨，为何你请的AI“演员”可能演着演着就换了人，以及我们最终如何才能让AI调配出一碗最懂你的“光谱靓汤”。00:00:33 给牛顿一个AI，科学会快多少？00:07:11 AI训练场上的“反思怪”，一条更聪明的成长路径00:12:36 AI的“差不多”，为什么会酿成大错？00:18:28 为什么你请的AI“演员”，可能演着演着就换人了？00:25:25 想让AI懂你？试试给它煲一锅“光谱靓汤”本期介绍的几篇论文：[AI] A multi-agent system for automating scientific discovery [FutureHouse] https://www.nature.com/articles/s41586-026-10652-y ---[LG] Introspective X Training: Feedback Conditioning Improves Scaling Across all LLM Training Stages [NVIDIA] https://arxiv.org/abs/2605.20285 ---[LG] Mechanisms of Misgeneralization in Physical Sequence Modeling [Harvard College & Microsoft & Comcast AI] https://arxiv.org/abs/2605.20299 ---[CL] The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study [Google DeepMind] https://arxiv.org/abs/2605.20767 ---[LG] Spectral Souping: A Unified Framework for Online Preference Alignment [Google DeepMind & Google Research] https://arxiv.org/abs/2605.20408

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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