本期，我们来聊聊AI如何从一个“普通学生”被系统地培养成编程竞赛的世界冠军，甚至超越了人类状元。但与此同时，为什么我们身边的AI助理，处理复杂任务时却常常“走着走着就散架”了？我们又该如何教会AI管理自己的“注意力”，像人一样划重点？以及，如何通过精准定位它“第一次犯错的瞬间”，让它的学习效率实现飞跃？四篇最新论文，带我们深入AI的“学霸心法”，揭示智能背后的策略、局限与成长之道。00:00:37 AI学会考试了，而且比状元考得还好00:06:06 你的AI助理，为啥走着走着就“散架”了？00:11:30 AI的注意力，该由谁做主？00:16:47 如何让机器学会聪明，抓住第一次犯错的瞬间00:22:08 知识的“断舍离”，我们究竟该记住什么？本期介绍的几篇论文：[LG] Post-Training Language Models for Gold-Medal Performance in Coding Competitions[NVIDIA]https://arxiv.org/abs/2609.02849 ---[AI] How Fast Do Agents Rot? An Empirical Study of Long-Horizon Degradation in LLM Agents for Production Decision-Making[Microsoft AI]https://arxiv.org/abs/2609.01660 ---[CL] Language Models Can Control Their Own Attention[KAIST AI & Google DeepMind]https://arxiv.org/abs/2609.02737 ---[LG] Cliff: Learning Process Rewards from the First Mistake[Amazon Web Services]https://arxiv.org/abs/2609.02817 ---[LG] What Is Worth Representing? Representational Empowerment for Continual Model Construction[UC Berkeley & University of Tübingen]https://arxiv.org/abs/2609.02322 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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