我们该如何教会AI看世界，同时避免它养成“视觉懒惰症”？为什么一个看过答案的“完美家教”，反而会让聪明的AI学生变得更笨？本期我们还将探讨，AI为何会像人类高手一样遭遇“跨界”难题，以及我们如何教会它像个老道的工匠一样“看人下菜碟”，智能地选择工具。今天，四篇最新论文将带我们深入AI成长的烦恼与智慧。

00:00:29 给AI装上眼睛，我们踩过哪些坑？

00:07:08 聪明学生的困境，为什么完美的家教反而会让你变笨？

00:12:58 AI的“跨界”难题，为什么高手也会栽跟头？

00:19:04 AI干活，也得学会“看人下菜碟”

00:24:20 那个“最懂你”的AI，可能只是个热情的陌生人

本期介绍的几篇论文：

[CV] Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes

[FAIR, Meta]

https://arxiv.org/abs/2608.05000

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[LG] Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation

[Microsoft Research]

https://arxiv.org/abs/2608.04794

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[CL] Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning

[Princeton University & CMU]

https://arxiv.org/abs/2608.05139

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[AI] COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation

[King’s College London]

https://arxiv.org/abs/2608.04336

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[CL] The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleads

[LIGHTSPEED & The Hong Kong University of Science and Technology]

https://arxiv.org/abs/2608.04570