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 ---[LG] ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models [Meta Superintelligence Labs (MSL)] https://arxiv.org/abs/2512.07843 ---[CV] Beyond the Noise: Aligning Prompts with Latent Representations in Diffusion Models [NOVA University of Lisbon & Google Research] https://arxiv.org/abs/2512.08505 ---[AI] Democratizing ML for Enterprise Security: A Self-Sustained Attack Detection Framework [Google LLC] https://arxiv.org/abs/2512.08802 ---[LG] Using reinforcement learning to probe the role of feedback in skill acquisition [ETH Zürich] https://arxiv.org/abs/2512.08463

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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