你有没有想过，一个真正能干活的AI，需要的不是更多的考题，而是一间属于自己的“办公室”？我们又该如何扮演一个聪明的“甩手掌柜”，给手下的AI专家们高效分配任务？本期节目，我们将从几篇最新的AI论文出发，聊聊如何用“成本思维”给AI的训练省下一半的钱，如何通过一场“博弈”让AI自我进化，并最终一起探索AI思考的形状，看看它的“脑海”里究竟是字典，还是一幅幅由概念构成的几何地图。00:00:34 想让AI替你干活？得先给它一间“办公室”00:06:01 如何当一个聪明的“甩手掌柜”？00:11:46 AI训练太烧钱？你缺的不是算力，是“成本思维”00:17:43 AI进步的捷径，不只看结果，更要玩对博弈00:23:08 AI怎么思考？答案可能藏在几何里本期介绍的几篇论文：[LG] Synthetic Computers at Scale for Long-Horizon Productivity Simulation [Microsoft] https://arxiv.org/abs/2604.28181 ---[LG] Optimized Deferral for Imbalanced Settings [Google Research & Courant Institute of Mathematical Science] https://arxiv.org/abs/2604.27723 ---[LG] Cost-Aware Learning [Google Research] https://arxiv.org/abs/2604.28020 ---[LG] Distributional Alignment Games for Answer-Level Fine-Tuning [Google Research & Microsoft Research] https://arxiv.org/abs/2604.27166 ---[LG] Do Sparse Autoencoders Capture Concept Manifolds? [Harvard University] https://arxiv.org/abs/2604.28119

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

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