00:01:26 AI的“心里话”，为什么也可能是装出来的？ 00:06:38 AI的“记忆力”，如何才能赶上一本小说？ 00:12:26 AI陪练：笨功夫如何“点化”天才？ 00:16:27 AI搞装修：如何盖一座又高又省钱的“摩天楼”？ 00:20:34 小模型，大智慧：AI的“学徒”进化论 本期介绍的五篇论文： [LG] Chain-of-Thought Is Not Explainability [Oxford & WhiteBox] https://arxiv.org/abs/2025.02 --- [LG] Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences [Snowflake AI Research] https://arxiv.org/abs/2506.13996 --- [CL] ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models [NVIDIA] https://arxiv.org/abs/2505.24864 --- [LG] Don't be lazy: CompleteP enables compute-efficient deep transformers [Cerebras Systems & ETH Zurich] https://arxiv.org/abs/2505.01618 --- [CL] Distilling LLM Agent into Small Models with Retrieval and Code Tools [KAIST & KRAFTON] https://arxiv.org/abs/2505.17612

迷路，不是一种状态，它是一种必要的暂停。它强迫我们从“自动驾驶”切换到“手动模式”，用自己的意图，去行走，去感受。

00:01:29 AI的“人设”：谷歌“腹黑”，OpenAI“傻白甜”？ 00:06:09 给AI“减肥餐”：为什么数据越多，模型可能越笨？ 00:10:32 AI训练场上的新策略：先当“神算子”，再做“阅读家” 00:14:53 功劳簿到底该怎么写？ 今天介绍的四篇论文： [LG] Strategic Intelligence in Large Language Models: Evidence from evolutionary Game Theory K Payne, B Alloui-Cros [King’s College London & University of Oxford] https://arxiv.org/abs/2507.02618 --- [LG] Data Uniformity Improves Training Efficiency and More, with a Convergence Framework Beyond the NTK Regime Y Wang, S Gu [Johns Hopkins University & UC Berkeley] https://arxiv.org/abs/2506.24120 --- [CL] Should We Still Pretrain Encoders with Masked Language Modeling? H Gisserot-Boukhlef, N Boizard, M Faysse, D M. Alves... [Artefact Research Center & Diabolocom & Illuin Technology] https://arxiv.org/abs/2507.00994 --- [LG] Disentangled Feature Importance J Du, K Roeder, L Wasserman [CMU] https://arxiv.org/abs/2507.00260

时间本身就是最高形式的财富，而你，需要成为第一个收款人。

00:01:33 AI育儿经：聪明的大脑，是“喂”出来的还是“练”出来的？ 00:07:02 换个方向看世界：当AI学会“倒着想” 00:10:52 AI变强有公式？别太天真了 00:14:53 抓住那个“不对劲”：比记住更多更重要的，是懂得如何“修正记忆” 本期介绍的四篇论文： [CL] NaturalThoughts: Selecting and Distilling Reasoning Traces for General Reasoning Tasks [FAIR at Meta] https://arxiv.org/abs/2507.01921 --- [CL] LEDOM: An Open and Fundamental Reverse Language Model [Peking University & University of California, Santa & BarbaraNational University of Singapore] https://arxiv.org/abs/2507.01335 --- [CL] Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check [New York University] https://arxiv.org/abs/2507.00885 --- [LG] xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection [AGH University of Krakow] https://arxiv.org/abs/2506.22837

你应该容忍、甚至鼓励那些可能带来巨大回报的、高失败率的实验。因为在商业世界里，一次本垒打的得分，远超你的想象。

00:01:45 AI 炼丹术：发现模型训练的“万能公式”？ 00:07:01 AI军备竞赛，弹药快打光了怎么办？ 00:12:16 让 AI 学会“反思”，会发生什么？ 00:16:48 AI“高考”也偏科？换个考法，学霸变学渣 00:22:08 AI的“外挂”：当你的短板，成了我的跳板 00:27:18 让AI“看人下菜碟”：下一代人工智能的效率革命 本期介绍的六篇AI前沿论文： [LG] Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks [Google DeepMind & New York University] https://arxiv.org/abs/2507.02119 --- [LG] Fast and Simplex: 2-Simplicial Attention in Triton [Meta & University of Texas at Austin] https://arxiv.org/abs/2507.02754 --- [LG] Energy-Based Transformers are Scalable Learners and Thinkers [Amazon & UVA] https://arxiv.org/abs/2507.02092 --- [CL] Answer Matching Outperforms Multiple Choice for Language Model Evaluation [Max Planck Institute for Intelligent Systems & Tübingen AI Center] https://arxiv.org/abs/2507.02856 --- [RO] MultiGen: Using Multimodal Generation in Simulation to Learn Multimodal Policies in Real [UC Berkeley] https://arxiv.org/abs/2507.02864 --- [LG] Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs https://arxiv.org/abs/2507.02076

我们每个人的人生，都充满了各种各样的“刺激”——可能是老板的一句批评，伴侣的一句抱怨，甚至只是社交媒体上的一条负面评论。而决定我们成为什么样的人，拥有怎样的人生的，并非这些刺激本身，而是我们选择如何利用那个宝贵的“空间”去回应。

00:01:19 高手过招，拼的不是“脑子”，而是“书房”？ 00:05:35 AI的“自我反思”：如何让机器像高手一样思考？ 00:10:31 AI的“第六感”：如何听懂线索背后的言外之意 00:16:06 AI的“犯罪现场调查”：我们能倒推出你对AI说了什么吗？ 00:20:50 你的“队友”是真懂还是装懂？人工智能辩论赛里的一个发现 本期介绍的五篇论文： [CL] Frustratingly Simple Retrieval Improves Challenging, Reasoning-Intensive Benchmarks [Allen Institute for AI & University of Southern California & University of Washington] https://arxiv.org/abs/2507.01297 --- [LG] Test-Time Scaling with Reflective Generative Model [MetaStone-AI & USTC] https://arxiv.org/abs/2507.01951 --- [LG] GradMetaNet: An Equivariant Architecture for Learning on Gradients [University of Oxford & Technion] https://arxiv.org/abs/2507.01649 --- [LG] GPT, But Backwards: Exactly Inverting Language Model Outputs [University of Manchester & Mohamed bin Zayed University of Artificial Intelligence] https://arxiv.org/abs/2507.01693 --- [CL] The Thin Line Between Comprehension and Persuasion in LLMs [Microsoft & The University of York] https://arxiv.org/abs/2507.01936

真正困住我们的，是我们为自己亲手建造的那座“心牢”。只要你还被关在这座牢里，那么无论你逃到天涯海角，换多少份工作，认识多少新的人，你都只是一个“带着牢笼赶路”的囚徒。

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

人生，没有真正的绝境。