本期节目，我们将一起潜入AI的“思想内核”，看看科学家们是如何像物理学家一样，为AI搭建“比萨斜塔”来找到最关键的架构“补丁”；如何为AI的思考过程立下“定律”，让它不再“乱使劲”；我们还会聊聊，怎样将我们模糊的“感觉”变成一把精准的AI“标尺”；如何找到AI训练中那条介于“跳跃”和“龟行”之间的最优路径；以及如何打造一个既能学得像人类专家，又能开得稳的AI“老司机”团队。准备好了吗？让我们一起出发！00:00:37 AI研究的“比萨斜塔”：我们看清模型强弱的方式可能错了00:08:29 给AI立规矩：聪明的大脑是如何炼成的？00:14:59 AI训练的“最优解”：在跳跃和龟行之间找到第三条路00:20:32 你的“感觉”，如何变成AI的“标尺”？00:25:56 如何让AI司机，既学得像，又开得稳？本期介绍的几篇论文：[CL] Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers[FAIR at Meta]https://arxiv.org/abs/2512.17351---[CL] When Reasoning Meets Its Laws[University of Illinois Urbana-Champaign & University of Pennsylvania]https://arxiv.org/abs/2512.17901---[LG] Smoothing DiLoCo with Primal Averaging for Faster Training of LLMs[Meta Superintelligence Lab]https://arxiv.org/abs/2512.17131---[CL] AutoMetrics: Approximate Human Judgements with Automatically Generated Evaluators[Stanford University & American Express]https://arxiv.org/abs/2512.17267---[LG] Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy[University of Illinois Urbana-Champaign & University of Pennsylvania]https://arxiv.org/abs/2512.17899

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