我们总以为AI越强，就意味着模型越大、思考越久，但今天我们要聊点不一样的。本期几篇最新论文告诉我们，AI有时也会“近视”，只看眼前；但它也能学会“团队作战”，把一个难题拆开并行处理。我们还会看到，AI画画不必等到最后一刻才知好坏，企业抓坏人也不再需要昂贵的“博士专家”。最后，我们会通过一个巧妙的实验，揭示学习的本质——有时，搭好“脚手架”比闷头苦练更重要。

00:00:35 大模型其实是“近视眼”？

00:05:36 从单核思考到团队作战：AI的效率革命

00:11:34 AI绘画的“中场战事”：如何省下一半力气，画出更好的图？

00:16:31 给你的网络安个“最强大脑”，但不用请博士

00:22:17 学习的“脚手架”：为什么“学会”比“做会”需要更多信息？

本期介绍的几篇论文：

[CL] Short-Context Dominance: How Much Local Context Natural Language Actually Needs?  

[University of British Columbia & Google DeepMind]  

https://arxiv.org/abs/2512.08082 

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[LG] ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models  

[Meta Superintelligence Labs (MSL)]  

https://arxiv.org/abs/2512.07843 

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[CV] Beyond the Noise: Aligning Prompts with Latent Representations in Diffusion Models  

[NOVA University of Lisbon & Google Research]  

https://arxiv.org/abs/2512.08505 

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[AI] Democratizing ML for Enterprise Security: A Self-Sustained Attack Detection Framework  

[Google LLC]  

https://arxiv.org/abs/2512.08802 

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[LG] Using reinforcement learning to probe the role of feedback in skill acquisition  

[ETH Zürich]  

https://arxiv.org/abs/2512.08463