![[人人能懂AI前沿] 从创世积木、思维成本到知识代谢：AI如何“思考”？](https://image.xyzcdn.net/FuDP4HpAp8ezgVZMmEel3mblKCmJ.jpg@middle)
主播
节目简介
来源：小宇宙
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
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
