你有没有想过，如何教AI像我们一样“知错能改”，而不是只会“一锤子买卖”？当一群AI协作时，怎样才能让它们像顶尖团队一样开好“复盘会”，而不是人多添乱？这一期，我们将一口气聊透五篇最新论文，看科学家们如何教会AI从“自我纠错”的智慧，进化到拥有“内在记忆”，甚至跨界变身，将工厂难题精准翻译成数学代码。准备好，一场关于AI如何学习“思考”的头脑风暴，马上开始！00:00:34 AI界的“错题本”，如何教机器学会“三思而后行”？00:06:11 人多不一定力量大，但聪明的团队会开“复盘会”00:11:32 为什么你的AI“记不住事”？00:17:27 算力的“跨界”妙用，如何让AI芯片干好分外的活？00:23:58 AI“翻译官”，从工厂难题到数学代码本期介绍的几篇论文：[LG] Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought [University of Michigan] https://arxiv.org/abs/2604.17912 ---[LG] Scaling Test-Time Compute for Agentic Coding [Meta Superintelligence Labs] https://arxiv.org/abs/2604.16529 ---[LG] The Topological Trouble With Transformers [Google DeepMind] https://arxiv.org/abs/2604.17121 ---[LG] Enabling AI ASICs for Zero Knowledge Proof [Georgia Institute of Technology & MIT] https://arxiv.org/abs/2604.17808 ---[LG] AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems [X, The Moonshot Factory & University of Oxford] https://arxiv.org/abs/2604.16804

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

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