本期节目，我们将一起打开一个“AI智慧工具箱”，看看几篇最新论文如何为我们揭示AI思考的底层秘密。我们将探讨，AI如何用一把名为“柯尔莫哥洛夫复杂度”的终极尺子去寻找最简单的答案，又为何在真实世界中，学会“探路”远比“背地图”更重要。我们还会看到，科学家们如何从急诊室的真实病例中，为进入物理世界的机器人设计“驾照考试”。最后，我们会拆解代码这颗“大力丸”的补脑秘方，并揭示AI是如何通过“双核大脑”训练，同时拥有深思考和快反应这两种超能力的。00:00:44 AI的“奥卡姆剃刀”：如何找到那个最简单也最深刻的答案？00:06:25 AI学“规划”：背地图和自己探路，哪个更高明？00:11:52 AI“下凡”入世：我们如何教会机器人“趋利避害”？00:17:20 AI的“大力丸”：代码里究竟藏着什么“补脑”秘方？00:22:06 鱼与熊掌如何兼得？让AI拥有“深思考”和“快反应”本期介绍的几篇论文：[LG] Bridging Kolmogorov Complexity and Deep Learning: Asymptotically Optimal Description Length Objectives for Transformers [Google DeepMind & Google Research] https://arxiv.org/abs/2509.22445 ---[LG] Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective [Microsoft Research Asia & Peking University] https://arxiv.org/abs/2509.22613 ---[LG] Can AI Perceive Physical Danger and Intervene? [Google DeepMind Robotics] https://arxiv.org/abs/2509.21651 ---[CL] On Code-Induced Reasoning in LLMs [Carnegie Mellon University (CMU)] https://arxiv.org/abs/2509.21499 ---[CL] Dual-Head Reasoning Distillation: Improving Classifier Accuracy with Train-Time-Only Reasoning [Google] https://arxiv.org/abs/2509.21487

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

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