你有没有想过，那些让人眼花缭乱的AI新方法，背后可能藏着同样的“武功心法”？我们又要如何像培养学徒一样，把AI训练成能炒股、会分析的“操盘手”，甚至给它请个“小助理”来把工作效率提升七倍？本期节目，我们就从几篇最新的论文出发，聊聊如何给AI“瘦身”、“加速”，并看清它究竟是全能学霸，还是个连数数都会搞错的“偏科生”。00:00:30 AI武林秘籍：天下武功，同出一门？00:06:02 AI炒股机器人进化论：从“学徒”到“操盘手”00:11:49 AI模型的“瘦身”魔法：让老模型焕发新生00:16:57 AI画画慢？给它请个“小助理”00:22:42 给AI当“统计学老师”：学霸还是偏科生？本期介绍的几篇论文：[LG] Opal: An Operator Algebra View of RLHF [Microsoft] https://arxiv.org/abs/2509.11298 ---[LG] Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning [University of California, Los Angeles & University of Washington] https://arxiv.org/abs/2509.11420 ---[LG] PHLoRA: data-free Post-hoc Low-Rank Adapter extraction from full-rank checkpoint [Amazon AGI & EdgeRunner AI] https://arxiv.org/abs/2509.10971 ---[LG] SpeCa: Accelerating Diffusion Transformers with Speculative Feature Caching [Shanghai Jiao Tong University & The Hong Kong University of Science and Technology] https://arxiv.org/abs/2509.11628 ---[CL] Reasoning Under Uncertainty: Exploring Probabilistic Reasoning Capabilities of LLMs [University of Maryland] https://arxiv.org/abs/2509.10739

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

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