[人人能懂] 不止一把刷子：解密AI的思考工具箱与风险显微镜

你有没有想过，当AI把所有练习册都做完时该怎么办？那些被它丢弃的“废稿”里，又藏着怎样的智慧？这一期，我们将一起探索AI如何像炼金术士一样点石成金，学会从百次尝试中预见万次风险，并揭开它思考时那个深藏不露的“工具箱”，看看它是如何学会聪明地“偷懒”的。

00:00:27 给AI一本永远也做不完的“练习册”

00:05:50 AI的“废稿”里，藏着通往智慧的捷径

00:11:28 大模型思考的秘密，它有几把刷子？

00:16:01 只需百次尝试，如何预见AI的万次风险？

00:20:50 AI的“降本增效”，一个聪明的偷懒办法

本期介绍的几篇论文：

[LG] Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text

[NVIDIA]

https://arxiv.org/abs/2601.22975

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[CL] Residual Context Diffusion Language Models

[UC Berkeley]

https://arxiv.org/abs/2601.22954

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[CL] Context Structure Reshapes the Representational Geometry of Language Models

[Google DeepMind]

https://arxiv.org/abs/2601.22364

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[LG] Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling

[Microsoft Research]

https://arxiv.org/abs/2601.22636

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[LG] EUGens: Efficient, Unified, and General Dense Layers

[Seoul National University]

https://arxiv.org/abs/2601.22563