本期我们要聊点脑洞大开的：如果让一群AI自己组建科研社区，甚至给它们“放假”，会涌现出怎样的科学发现？我们会看到，AI真正的成长秘诀，不在于修正答案，而在于递归式地优化自己的“思考方法”，甚至学会像孙悟空一样用“分身术”同时探索多种可能。接着，当AI团队犯错时，我们将化身侦探，精准定位“责任人”，并揭秘一个让AI提速的妙招——不是靠堆算力，而是靠精明的“预算”分配。准备好了吗？让我们一起从几篇最新论文中，探寻这些关于AI工作流、团队协作与自我进化的深刻洞见。00:00:43 AI也需要“放假”？科学发现的新模式00:06:03 成长的秘密，不是优化答案，而是优化方法00:10:58 让AI学会“分身术”，我们能快多少？00:16:06 AI犯错，我们应该怪谁？00:20:55 AI 为什么那么慢？这篇论文给了个巧妙的答案本期介绍的几篇论文：[AI] Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment[DualverseAI & University of California San Diego]https://arxiv.org/abs/2608.23691 ---[AI] Metan^n: Recursive Self-Improvement through Emergent Depth[University of Minnesota & Seoul National University]https://arxiv.org/abs/2608.24735 ---[AI] Parason: Revealing Subtask and Trial Parallelism in LLM Reasoning[Tsinghua University & NVIDIA]https://arxiv.org/abs/2608.24658 ---[CL] Who is the Agent to Blame? Localizing Faithfulness and Citation Mistakes in Agentic Deep Research[Bar-Ilan University & UNC Chapel Hill]https://arxiv.org/abs/2608.24306 ---[CL] AgentSpec: Speculative Decoding for Batch Inference of LLM Agents[The Ohio State University & Microsoft Research & University of Michigan]https://arxiv.org/abs/2608.24004 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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