本期节目，我们来当一次AI的“首席优化官”，从里到外给它做个大升级。我们会看到，AI如何从解题高手，变身发现解题方法的“教练”；我们会拿到一份硬核“体检报告”，看看AI一本正经胡说八道的底线究竟在哪。我们还会发现，你和AI聊天时那些被浪费的“废话”，其实是喂饱它的宝贵养料；最后再深入AI的内部，看看万亿参数的它如何避免“大公司病”，以及一个惊人发现：困扰AI效率的瓶颈，可能不在“大脑”，而在“嘴巴”！00:00:38 AI当教练，数学家当陪练，我们如何找到世界的隐藏规则？00:06:42 AI会「一本正经地胡说八道」到什么程度？00:14:04 你扔掉的“废话”，正在喂饱AI00:19:14 万亿参数的大模型，是如何避免“公司越大，效率越低”的？00:27:08 你的模型为什么这么笨？问题可能出在“嘴”上本期介绍的几篇论文：[LG] Reinforced Generation of Combinatorial Structures: Ramsey Numbers [UC Berkeley & Google] https://arxiv.org/abs/2603.09172 ---[CL] How Much Do LLMs Hallucinate in Document Q&A Scenarios? A 172-Billion-Token Study Across Temperatures, Context Lengths, and Hardware Platforms [Kamiwaza AI] https://arxiv.org/abs/2603.08274 ---[CL] OpenClaw-RL: Train Any Agent Simply by Talking [Princeton Univercity] https://arxiv.org/abs/2603.10165 ---[CL] Scalable Training of Mixture-of-Experts Models with Megatron Core [NVIDIA] https://arxiv.org/abs/2603.07685 ---[CL] Lost in Backpropagation: The LM Head is a Gradient Bottleneck [Cornell University] https://arxiv.org/abs/2603.10145

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

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