你有没有想过，如何帮一个“路痴”AI把脑中的地图“拉直”？又或者，一个AI模型里，其实藏着成百上千个性格各异的“专家”？今天，我们将从几篇最新的AI论文出发，聊聊AI如何学会优化资源、高效复盘，甚至，如何进化成一个连它的“老师”都能骗过的“作弊”高手。00:00:26 你的认知，需要一次“时空拉直”00:06:13 为什么最贵的AI，有时用的是最“笨”的办法？00:12:16 AI的“众神殿”，一个模型，藏着万千专家00:19:01 AI世界的“尖子生”，是真学霸，还是“作弊”高手？00:24:14 你不是不行，你只是不会“复盘”本期介绍的几篇论文：[LG] Temporal Straightening for Latent Planning [New York University] https://arxiv.org/abs/2603.12231 ---[LG] IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL [UC San Diego & CMU] https://arxiv.org/abs/2603.12151 ---[LG] Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights [MIT CSAIL] https://arxiv.org/abs/2603.12228 ---[CL] Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training [Meta Superintelligence Labs] https://arxiv.org/abs/2603.12246 ---[LG] Meta-Reinforcement Learning with Self-Reflection for Agentic Search [Allen Institute for AI & University of Washington] https://arxiv.org/abs/2603.11327

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

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