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来源：小宇宙
你有没有想过，AI也能像老师傅一样通过“动手试错”来解决难题，或者像刚入社会的年轻人一样，通过“混社会”学会与同伴合作？最新的一些论文告诉我们，让AI变聪明的秘诀，可能不是一味地堆算力，而是要教它学会“复盘”，帮它找到那张指挥自己的“隐藏地图”，甚至用“以小博大”的智慧，实现效率的飞跃。今天，就让我们一起探索AI如何学会像人一样思考和成长。
00:00:32 AI界的“刻意练习”，它如何像个老师傅一样解决难题？
00:05:57 想让AI变善良？让它多见见世面
00:11:08 AI的“隐藏地图”，为什么你总也指挥不好它？
00:16:19 AI预测这事，不一定非得大力出奇迹
00:21:29 AI怎样才能不犯“二过”？
本期介绍的几篇论文：
[LG] FAMOSE: A ReAct Approach to Automated Feature Discovery
[Amazon]
https://arxiv.org/abs/2602.17641
---
[LG] Multi-agent cooperation through in-context co-player inference
[Google]
https://arxiv.org/abs/2602.16301
---
[LG] The Information Geometry of Softmax: Probing and Steering
[University of Chicago& INSEAD]
https://arxiv.org/abs/2602.15293
---
[LG] Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
[MIT & Allen Institute for AI & Qube Research & Technologies]
https://arxiv.org/abs/2602.17634
---
[LG] Experiential Reinforcement Learning
[University of Southern California & Microsoft & University of Pennsylvania]
https://arxiv.org/abs/2602.13949
00:00:32 AI界的“刻意练习”，它如何像个老师傅一样解决难题？
00:05:57 想让AI变善良？让它多见见世面
00:11:08 AI的“隐藏地图”，为什么你总也指挥不好它？
00:16:19 AI预测这事，不一定非得大力出奇迹
00:21:29 AI怎样才能不犯“二过”？
本期介绍的几篇论文：
[LG] FAMOSE: A ReAct Approach to Automated Feature Discovery
[Amazon]
https://arxiv.org/abs/2602.17641
---
[LG] Multi-agent cooperation through in-context co-player inference
[Google]
https://arxiv.org/abs/2602.16301
---
[LG] The Information Geometry of Softmax: Probing and Steering
[University of Chicago& INSEAD]
https://arxiv.org/abs/2602.15293
---
[LG] Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
[MIT & Allen Institute for AI & Qube Research & Technologies]
https://arxiv.org/abs/2602.17634
---
[LG] Experiential Reinforcement Learning
[University of Southern California & Microsoft & University of Pennsylvania]
https://arxiv.org/abs/2602.13949
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