你有没有想过，AI不仅能当一个好员工，还能自己进化成项目经理，开“复盘会”优化工作流？或者，指挥一个复杂的机器人，也许只需要像在屏幕上“画重点”一样简单？本期节目，我们将从五篇最新的AI论文出发，聊聊AI如何突破效率瓶颈：从揭秘AI服务“拼单”背后隐藏的隐私风险，到看AI如何像图书管理员一样高效整理海量知识，再到探索不同世界里的学习速度极限。准备好，让我们一起看看AI是如何学会“化繁为简”与“自我进化”的。00:00:39 想把东西卖出高价？你得懂点学习的规律00:07:21 拼单的代价，AI服务如何泄露你的秘密00:12:23 AI的瓶颈，不在大脑，在“书房”00:17:48 让AI自己进化，不止是大力出奇迹00:23:32 给机器人“画重点”，让复杂变简单本期介绍的几篇论文：[LG] On the Learning Curves of Revenue Maximization [Purdue University & Yale University & Technion] https://arxiv.org/abs/2604.26922 ---[LG] Quantamination: Dynamic Quantization Leaks Your Data Across the Batch [University of Cambridge & AI Sequrity Company] https://arxiv.org/abs/2604.26505 ---[LG] Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving [Microsoft Research] https://arxiv.org/abs/2604.26837 ---[CL] FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients [Naver Search US & MIT] https://arxiv.org/abs/2604.26258 ---[CV] Lifting Embodied World Models for Planning and Control [New York University & UC Berkeley] https://arxiv.org/abs/2604.26182

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

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