你有没有想过，一个学得更久的AI“尖子生”，为什么反而忘得更快？或者，想让AI更懂英语，最好的方法竟然是教它别的语言？本期节目，我们将一口气解锁五篇最新论文带来的“反常识”洞见。我们会发现，决定AI效率的瓶颈可能不是算力而是“管理”，与AI对话的成本可以靠一本“字典”轻松打个二折，而一个好的AI模拟世界，追求的不是“长得像”，而是“反应像”。00:00:32 大模型训练的悖论，为什么学得越久，忘得越快？00:06:02 AI的效率瓶颈，不是算力，是“管理”00:12:33 想让AI更懂英语？那就别只喂它英语00:18:46 跟AI对话，如何省下80%的话费？00:24:39 你的“差不多”不是我的“差不多”，如何让AI的模拟世界更靠谱？本期介绍的几篇论文：[LG] All elementary functions from a single binary operator [Jagiellonian University] https://arxiv.org/abs/2603.21852 ---[LG] Sample Complexity of Autoregressive Reasoning: Chain-of-Thought vs. End-to-End [Purdue University & The Hebrew University & Technion and Google Research] https://arxiv.org/abs/2604.12013 ---[CL] Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature [Central University of Finance and Economics & Beijing Institute of Technology & TsingyuAI] https://arxiv.org/abs/2604.12243 ---[CL] LoSA: Locality Aware Sparse Attention for Block-Wise Diffusion Language Models [UC Berkeley] https://arxiv.org/abs/2604.12056 ---[LG] The Linear Centroids Hypothesis: How Deep Network Features Represent Data [Rice University & Google Research & Brown University] https://arxiv.org/abs/2604.11962

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

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