你有没有想过，要让AI变得更聪明，除了让它“读万卷书”，我们还能不能让它在虚拟世界里“行万里路”，像玩“模拟人生”一样学会物理？当很多聪明的算法凑在一起反而“掉链子”时，我们如何用“乐高积木”的思路化繁为简？这一期，我们将一起探寻几份最新论文带来的启发：从像婴儿一样在“思想实验”中探索世界，到用一张“知识地图”代替“知识词典”来解决复杂问题，甚至让AI学会“自我怀疑”，从而变得又快又好。准备好了吗？让我们一起出发！00:00:38 AI版“模拟人生”让机器在虚拟世界里学会物理00:05:56 从1到N如何让你的数据分析稳上加稳？00:12:14 AI养娃我们可能找到了让机器像婴儿一样学习的秘密00:18:01 高手解决问题，靠的是地图，而不是词典00:24:14 AI的自我怀疑，一个让大模型又快又好的新思路本期介绍的几篇论文：[LG] Solving Physics Olympiad via Reinforcement Learning on Physics Simulators [CMU & Lambda] https://arxiv.org/abs/2604.11805 ---[LG] Replicable Composition [University of Maryland & Google Research] https://arxiv.org/abs/2604.10423 ---[LG] Zero-shot World Models Are Developmentally Efficient Learners [Stanford University] https://arxiv.org/abs/2604.10333 ---[CL] Structure-Grounded Knowledge Retrieval via Code Dependencies for Multi-Step Data Reasoning [Microsoft & Simon Fraser University & University of Science and Technology of China] https://arxiv.org/abs/2604.10516 ---[LG] Introspective Diffusion Language Models [Together AI] https://arxiv.org/abs/2604.11035

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

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