想知道如何像做笔迹鉴定一样，一眼看穿AI的“真身”吗？想了解怎样能让AI开会时，奇迹般地省下97%的“桌子”吗？本期我们就来聊聊几篇最新论文，看看AI如何学会“读心术”来高效协作，如何避免因“谜之自信”而犯下大错，甚至，为什么一个“会犯错”的老师，反而能教出更厉害的AI学生。00:00:28 如何给AI做“笔迹鉴定”？00:06:27 AI开会，如何省下97%的桌子？00:14:10 AI界的“青出于蓝”，是惊喜还是惊吓？00:19:30 你还在让AI“写报告”？它们已经开始直接交换“想法”了00:24:16 为什么“犯错”的老师，能教出更好的AI？本期介绍的几篇论文：[CL] The Surprising Universality of LLM Outputs: A Real-Time Verification Primitive [Evolutionairy AI] https://arxiv.org/abs/2604.25634 ---[LG] PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference [No University Provided] https://arxiv.org/abs/2604.24971 ---[AI] Evaluating Risks in Weak-to-Strong Alignment: A Bias-Variance Perspective [University of Illinois Urbana-Champaign & Microsoft & InstaDeep] https://arxiv.org/abs/2604.25077 ---[CL] Recursive Multi-Agent Systems [UIUC] https://arxiv.org/abs/2604.25917 ---[LG] When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient [Princeton University] https://arxiv.org/abs/2604.25872

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

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