你有没有想过，AI画画是不是也需要打草稿？面对一个“马虎”的AI，我们除了让它变聪明，还能不能帮它“划重点”？本期节目，我们将一口气解锁五篇最新论文里的智慧：看AI如何用“笨功夫”画出惊艳作品，如何从“作弊”中学到创造力，甚至如何用“供应链”思维组建一个高效的AI团队。准备好，我们一起看看AI是如何学会更聪明地工作的。00:00:32 AI画画的“笨功夫”00:05:58 人工智能的“马虎”病，我们找到了一个药方00:10:45 让AI设计个东西，它居然学会了“作弊”？00:17:04 AI搞团队建设，为什么总像拉一个草台班子？00:23:00 AI大模型的美食家难题，如何调配完美的学习菜单？本期介绍的几篇论文：[CV] One Pass Is Not Enough: Recursive Latent Refinement for Generative Models [Simon Fraser University] https://arxiv.org/abs/2605.15309 ---[CV] Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding [Google DeepMind] https://arxiv.org/abs/2605.15342 ---[CL] Optimized Three-Dimensional Photovoltaic Structures with LLM guided Tree Search [Google Research] https://arxiv.org/abs/2605.16191 ---[LG] AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs [CMU] https://arxiv.org/abs/2605.15565 ---[CL] Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time [New York University & CMU] https://arxiv.org/abs/2605.15220

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

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