00:01:19 AI的“偏科”难题：学好数理化，走遍天下真的不怕吗？00:05:08 AI 也会“复盘”？聊聊如何让机器像高手一样思考 00:09:19 语言的“橡皮泥”：我们如何“捏”出更智能的AI？00:13:57 AI科学家的新玩法：它不猜答案，专找“意外” 00:17:42 AI“长篇阅读”的秘密：如何让机器像螺旋一样思考？本期介绍的五篇论文：[LG] Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning [CMU & University of Washington & M-A-P] https://arxiv.org/abs/2507.00432 ---[LG] ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context [AI at Meta] https://arxiv.org/abs/2507.00417 ---[LG] Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows [Apple] https://arxiv.org/abs/2507.00425 ---[LG] Open-ended Scientific Discovery via Bayesian Surprise [University of Massachusetts Amherst & Allen Institute for AI] https://arxiv.org/abs/2507.00310 ---[LG] HelixPipe: Efficient Distributed Training of Long Sequence Transformers with Attention Parallel Pipeline Parallelism [National University of Singapore] https://arxiv.org/abs/2507.00394

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

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