00:01:29 AI的“人设”：谷歌“腹黑”，OpenAI“傻白甜”？
00:06:09 给AI“减肥餐”：为什么数据越多，模型可能越笨？
00:10:32 AI训练场上的新策略：先当“神算子”，再做“阅读家”
00:14:53 功劳簿到底该怎么写？
今天介绍的四篇论文：
[LG] Strategic Intelligence in Large Language Models: Evidence from evolutionary Game Theory
K Payne, B Alloui-Cros
[King’s College London & University of Oxford]
https://arxiv.org/abs/2507.02618
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[LG] Data Uniformity Improves Training Efficiency and More, with a Convergence Framework Beyond the NTK Regime
Y Wang, S Gu
[Johns Hopkins University & UC Berkeley]
https://arxiv.org/abs/2506.24120
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[CL] Should We Still Pretrain Encoders with Masked Language Modeling?
H Gisserot-Boukhlef, N Boizard, M Faysse, D M. Alves...
[Artefact Research Center & Diabolocom & Illuin Technology]
https://arxiv.org/abs/2507.00994
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[LG] Disentangled Feature Importance
J Du, K Roeder, L Wasserman
[CMU]
https://arxiv.org/abs/2507.00260