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

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[CL] When Reasoning Meets Its Laws

[University of Illinois Urbana-Champaign & University of Pennsylvania]

https://arxiv.org/abs/2512.17901

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[LG] Smoothing DiLoCo with Primal Averaging for Faster Training of LLMs

[Meta Superintelligence Lab]

https://arxiv.org/abs/2512.17131

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[CL] AutoMetrics: Approximate Human Judgements with Automatically Generated Evaluators

[Stanford University & American Express]

https://arxiv.org/abs/2512.17267

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[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

[人人能懂] AI的卡农、定律与标尺

AI可可AI生活

[人人能懂] AI的卡农、定律与标尺