今天我们要聊一个特别有意思的话题：如何“看透”AI并让它变得更好？我们将通过几篇最新论文，揭示一些反常识的智慧：比如，有时让AI“盲目”一点，它反而画得更好；想让它变聪明，关键可能不是“教”得多，而是“教”得巧。我们还会看到，攻击AI的最高境界，可能不是塞给它坏东西，而是对好东西做一次肉眼看不见的“微创手术”！

00:00:31 AI“投毒”新姿势，不是塞坏东西，而是让好人变坏

00:07:00 让AI变聪明的秘密，不是加法，是减法

00:11:29 AI的瘦身难题，如何高效地“抓重点”？

00:17:14 AI的“思想慢镜头”，我们如何看懂它在想什么？

00:22:54 AI绘画新思路，有时候，少即是多

本期介绍的几篇论文：

[LG] Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions

[University of Oxford & UCL]

https://arxiv.org/abs/2602.09987

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[CL] Effective Reasoning Chains Reduce Intrinsic Dimensionality

[Google DeepMind & UNC Chapel Hill]

https://arxiv.org/abs/2602.09276

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[LG] WildCat: Near-Linear Attention in Theory and Practice

[Imperial College London & Microsoft Research]

https://arxiv.org/abs/2602.10056

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[LG] Step-resolved data attribution for looped transformers

[University of Potsdam & Technical University of Munich & MunichHarvard University]

https://arxiv.org/abs/2602.10097

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[LG] Blind denoising diffusion models and the blessings of dimensionality

[Simons Foundation & Yale University]

https://arxiv.org/abs/2602.09639