本期「人人能懂的AI前沿」，我们重点介绍五篇最新的AI论文：00:00:27 高手过招：AI是如何在游戏中“悟道”的？00:04:31 造图的“慢炖”与“快炒”：AI绘画的新思路00:08:51 AI也懂“看情况办事”了？ 00:13:34 用对锤子：AI工具的正确使用说明书 00:18:08 AI点餐的智慧：如何花小钱办大事 详细论文信息供参考：[LG] SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning [National University of Singapore & A*STAR & Northeastern University] https://arxiv.org/abs/2506.24119 ---[LG] Transition Matching: Scalable and Flexible Generative Modeling [Weizmann Institute of Science & FAIR at Meta] https://arxiv.org/abs/2506.23589 ---[LG] Curious Causality-Seeking Agents Learn Meta Causal World [Chinese Academy of Sciences & Peking University] https://arxiv.org/abs/2506.23068 ---[LG] Use Sparse Autoencoders to Discover Unknown Concepts, Not to Act on Known Concepts [Cornell Tech & UC Berkeley] https://arxiv.org/abs/2506.23845 ---[LG] BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute [The University of British Columbia & Microsoft & Pennsylvania State University] https://arxiv.org/abs/2506.22716
--------
22:50
--------
22:50
[感悟]警惕！那个正在“喂养”你大脑的隐形厨师
有一个我们不易察觉的变化，正在重塑我们和知识、和世界相处的方式。
--------
1:47
--------
1:47
你以为AI在理解语言？其实它在开一场“关系”派对
[LG] Transformers are Graph Neural Networks[University of Cambridge]arxiv.org
--------
4:48
--------
4:48
AI的“顿悟”：机器如何学会了举一反三？
[LG] Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning[University of Texas at Austin]arxiv.org
--------
6:13
--------
6:13
想看懂未来？先学会给机器讲个好故事
[LG] Performance Prediction for Large Systems via Text-to-Text Regression[Google Research]arxiv.org
New Zealand