本期节目，我们将潜入AI的“思想厨房”，看看它如何像分子料理大师一样，在抽象的“风味空间”里低成本地创造出绝妙品味。我们还会揭秘AI的“通感”天赋，探索为何只“读书”的AI，竟能通过代码和数学“看懂”世界。更进一步，我们将见证AI世界里“侦探”与“法官”的诞生，看两种AI如何协作，确保推理的铁证如山。最后，我们将探讨一种让AI学会“无知之智”的深刻方法，明白承认“不知道”为何是更高级的智慧，让我们马上开始！00:00:39 “造句”不如“造意”：一种让AI低成本学会“好品味”的新方法00:05:12 AI界的“万能诊断仪”：大道至简，用“读心术”取代“望闻问切”00:10:22 AI的“通感”：只“读书”的AI，为何能“看懂”世界？00:15:26 AI当“侦探”，谁来当“法官”？—— 一种让AI的推理靠谱起来的新方法00:20:35 AI的“无知之智”：最高级的智慧，是承认“我不知道”[CL] Limited Preference Data? Learning Better Reward Model with Latent Space Synthesis [University of Wisconsin-Madison & Nanyang Technological University] https://arxiv.org/abs/2509.26074 ---[CL] Regression Language Models for Code [Cornell University & Google] https://arxiv.org/abs/2509.26476 ---[LG] Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training [Meta Superintelligence Labs] https://arxiv.org/abs/2509.26625 ---[LG] Towards Verified Code Reasoning by LLMs [University of Texas at Austin & Google DeepMind] https://arxiv.org/abs/2509.26546 ---[CL] TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning [Meta Reality Labs] https://arxiv.org/abs/2509.25760

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

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