今天，我们要深入AI的“大脑”，看看它是如何真正学会“思考”的。我们会探讨，是给AI请个“陪练”逐步放手，还是给它一张“地图”指引全局更有效？我们还会见证一场AI学习的“龟兔赛跑”，看看“快功夫”和“慢功夫”哪个更有前途。最后，我们将一起揭开AI如何从死记硬背走向融会贯通，以及我们该如何科学地看待它的“成绩单”。五篇最新论文，带你洞悉AI学习的底层智慧。00:00:34 给AI请个“陪练”，然后悄悄撤走00:04:54 AI干活，为什么喂给它地图比喂给它字典更管用？00:10:35 AI的快功夫与慢功夫00:16:03 AI对齐，一份被误解的成绩单00:20:42 AI怎么才能“活”起来，从死记硬背到融会贯通本期介绍的几篇论文：[LG] CanvasAnneal：Curriculum Reinforcement Learning for Diffusion Language Models[Google DeepMind]https://arxiv.org/abs/2609.13060 ---[AI] Beyond Vector Similarity：Hierarchical Context-Aware Graph RAG vs Standard RAG in Enterprise Code Migration[Google Cloud]https://arxiv.org/abs/2609.12464 ---[CL] Breaking the Token Ceiling：Distilling Smaller， Stronger Byte Models[Meta FAIR & University of Washington, Seattle]https://arxiv.org/abs/2609.12303 ---[LG] Distortion of AI Alignment Revisited：RLHF is a Decent Utilitarian Aligner[UC Berkeley]https://arxiv.org/abs/2609.12651 ---[AI] Hierarchical Prototype Emergence in Modern Hopfield Models[Stanford University]https://arxiv.org/abs/2609.12079 在小宇宙查看该单集文稿

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

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