你有没有想过，最简单的数学平均值，竟然能打败最复杂的压缩算法？或者，在教AI“做什么”之前，我们其实可以先给它“喂”一套完整的思想和人设？本期节目，我们将从四篇最新的AI论文出发，一起探寻如何让AI自己长出可拆分的“乐高模块”，以及如何像一位顶级名师那样，把奖励精准地“夸”到AI的灵光一闪之处。00:00:29 你的记忆能被压缩多少，藏在一个几何定律里00:06:42 训练AI，从“喂”指令到“喂”思想00:11:47 AI减肥记，如何让一个大模型只带“脑子”出门？00:17:54 AI也需要“夸到点子上”？本期介绍的几篇论文：[LG] The Geometry of Consolidation A Bharadwaj Vangara, A Gopinath https://github.com/niashwin/geometry-of-consolidation/blob/main/paper/arxiv/main.pdf ---[AI] Model Spec Midtraining: Improving How Alignment Training Generalizes C Li, S Price, S Marks, J Kutasov https://arxiv.org/abs/2605.02087 ---[CL] EMO: Pretraining Mixture of Experts for Emergent Modularity R Wang, A Bhagia, S Min https://arxiv.org/abs/2605.06663 ---[LG] DGPO: Distribution Guided Policy Optimization for Fine Grained Credit Assignment H Jin, R Zhu, Z Du, X Jiang… https://arxiv.org/abs/2605.03327

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

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