你是否想过，AI要如何才能像武林高手一样“左右互搏”，自己给自己出题，实现无限成长？当它学习一项新技能时，又要如何避免像我们一样，一紧张就把基本功忘得一干二净？更神奇的是，我们还将看到AI如何不靠开颅手术，就能精准“读懂”我们大脑里的句子。本期节目，我们将通过几篇最新论文，一起探寻AI世界里关于学习、成长与解决复杂问题的非凡智慧。00:00:33 当AI学会“读心”，我们离未来还有多远？00:07:00 突破成长天花板，AI如何学会“左右互搏”，做自己最好的老师？00:12:39 告别“狗熊掰棒子”式的努力，从一台AI机器手的进化，看高手的“底层能力”构建00:16:14 破局“好与快”的死结，从猜答案到重塑底层逻辑的认知飞跃00:21:06 当麻烦“祸不单行”时，我们该如何破局？，，来自前沿AI算法的生存智慧本期介绍的几篇论文：[CL] Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings[Meta AI & Université PSI]https://arxiv.org/abs/2608.18114 ---[CL] SPADE: Self-Play in Adaptive Synthetic Executable Environments[University of Washington & Northeastern University & CMU]https://arxiv.org/abs/2608.19197 ---[RO] ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning[NVIDIA]https://arxiv.org/abs/2608.19182 ---[AI] Coupled-cluster molecular properties across the main group that extrapolate beyond training size[MIT]https://arxiv.org/abs/2608.18346 ---[LG] Continuous-Time Reinforcement Learning for Controlled Hawkes Jump-Diffusions[UC Berkeley]https://arxiv.org/abs/2608.19151 在小宇宙查看该单集文稿

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

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