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主播
节目简介
来源：小宇宙
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
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
