你有没有想过，整个科学计算器也许只需要两个按键就能实现？或者，AI偷懒的秘诀竟是只用20%的精力，就能完成90%的工作？最新的一些研究，正从这些奇妙的角度，刷新我们对智能、效率和知识的认知。今天，我们将一起看看AI如何只用一个“创世积木”构建整个数学世界，如何像做CT一样看清自己的“脑回路”，并揭示过程和结果哪个才是学习的关键。准备好，一场思维风暴马上开始！00:00:36 你的科学计算器，其实只需要两个键00:05:01 学会一个本事，过程和结果哪个更重要？00:13:05 如何像高手一样，“看见”知识的未来？00:19:31 AI偷懒的艺术，为什么只做20%的工作，能得到90%的结果？00:25:08 给AI大脑做CT，我们找到了更清晰的脑回路图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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