这一期，我们将一起探索AI的“大脑”有没有可能像一张能被看懂的设计图，并看看AI如何反过来帮我们给维基百科“捉虫”。接着，我们会聊聊当AI“人多力量大”时，如何聪明地选出最佳方案，以及机器人模仿人类动作时，到底什么才是精髓。最后，一个颠覆性的发现会告诉我们：训练AI解题高手，最“笨”的方法有时竟然是最好的方法。00:00:31 AI的“大脑”长啥样？一份来自未来的设计图00:06:05 维基百科这位“巨人”，也会自己打自己吗？00:10:22 人多不一定力量大，除非你懂得怎么选00:14:43 机器人模仿秀：差的那口气，终于有人补上了00:20:22 高手之路：有时，“最笨”的方法就是最好的方法本期介绍的几篇论文：[LG] The Dragon Hatchling: The Missing Link between the Transformer and Models of the Brain [Pathway] https://arxiv.org/abs/2509.26507 ---[CL] Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models [Stanford University] https://arxiv.org/abs/2509.23233 ---[LG] The Unreasonable Effectiveness of Scaling Agents for Computer Use [Simular Research] https://arxiv.org/abs/2510.02250 ---[RO] OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction [Amazon FAR] https://arxiv.org/abs/2509.26633 ---[LG] Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards [Hong Kong University of Science and Technology & Kuaishou Technology] https://arxiv.org/abs/2509.24981

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

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