00:00:34 让AI更“有谱儿”：不止一条路通罗马 00:05:21 如何让AI更聪明？一个“求稳”的智慧 00:09:14 造车新智慧：如何用“搬沙子”的办法，算出最省油的外形？ 00:12:31 AI也会“路径依赖”？一个简单动作，让它“老树发新芽” 00:17:01 AI炼丹术：我们如何“教会”机器遵守化学规则？ 00:21:50 AI进化论：从“死记硬背”到“自我成长” 本期介绍的几篇论文：[LG] Flow Matching Policy Gradients [UC Berkeley] https://arxiv.org/abs/2507.21053 ---[CL] Geometric-Mean Policy Optimization [Microsoft Research] https://arxiv.org/abs/2507.20673 ---[LG] Geometric Operator Learning with Optimal Transport [California Institute of Technology & Nvidia] https://arxiv.org/abs/2507.20065 ---[LG] What Can Grokking Teach Us About Learning Under Nonstationarity? [Google DeepMind] https://arxiv.org/abs/2507.20057 ---[LG] Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling [MIT] https://arxiv.org/abs/2507.19799 ---[LG] A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence https://arxiv.org/abs/2507.21046

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