你是否好奇，为何AI有时会“指鹿为马”？为何它面对难题，内部的神经元反而开始“集体偷懒”？本期节目，我们将通过几篇最新论文，一起给AI的大脑做一次“CT扫描”和“基因测序”，揭示它在感知、学习、思考和效率背后，那些出人意料的底层法则。

00:00:26 人工智能的“阿喀琉斯之踵”，一个关于维度的诅咒

00:05:34 AI绘画进化论，为什么高手不需要“题海战术”？

00:10:02 AI一思考，我们就发笑？不，是神经元在“偷懒”

00:15:44 如何用50倍的效率，给AI做一次“CT扫描”？

00:21:34 AI模型的“不可能三角”，算力、速度与智能

本期介绍的几篇论文：

[LG] Solving adversarial examples requires solving exponential misalignment

[Stanford University & Aisle]

https://arxiv.org/abs/2603.03507

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[LG] Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data

[University of Michigan & Google DeepMind & UC Berkeley]

https://arxiv.org/abs/2603.03700

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[CL] Farther the Shift，Sparser the Representation： Analyzing OOD Mechanisms in LLMs

[Rutgers University & Northwestern University & UKP Lab, TU Darmstadt]

https://arxiv.org/abs/2603.03415

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[CL] Compressed Sensing for Capability Localization in Large Language Models

[CMU]

https://arxiv.org/abs/2603.03335

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[LG] Why Are Linear RNNs More Parallelizable?

[Allen Institute for AI & Rheinland-Pfalzische Technische Universitat]

https://arxiv.org/abs/2603.03612