00:01:47 AI的“断舍离”：如何让机器像人一样阅读？ 00:06:27 AI健忘症：为什么你的聊天机器人越聊越糊涂？ 00:10:21 AI大模型的“记忆”难题：鱼和熊掌如何兼得？ 00:15:28 想把事做对？你得先学会“挑错” 00:18:54 你的AI管家，终于告别“金鱼记忆”了 今天介绍的5篇论文： [LG] Dynamic Chunking for End-to-End Hierarchical Sequence Modeling [CMU & Cartesia AI] https://arxiv.org/abs/2507.07955 --- [LG] Understanding and Improving Length Generalization in Recurrent Models [CMU & Cartesia AI] https://arxiv.org/abs/2507.02782 --- [CL] A Systematic Analysis of Hybrid Linear Attention [UC Santa Cruz & University of Groningen] https://arxiv.org/abs/2507.06457 --- [CL] CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization [ByteDance Seed & Nanjing University] https://arxiv.org/abs/2507.06181 --- [CL] MIRIX: Multi-Agent Memory System for LLM-Based Agents [MIRIX AI] https://arxiv.org/abs/2507.07957

你以为自己已经习惯了，但其实，它们在不知不觉中，偷走了你最好的精力和最平静的心情。

00:02:21 AI巨头们的“乐高”心法：应对复杂世界的终极武器 00:07:24 AI小助理的养成记：笨徒弟如何变高徒？ 00:12:47 你家AI聪不聪明，得看它“跑”得稳不稳 00:18:43 AI也会“看走眼”？一招教它“看真切” 00:22:03 AI的“笨”办法：像婴儿一样学习世界 00:26:54 AI也“开小差”？不说话的思考，可能更强大 今天介绍的六篇论文： [LG] AXLearn: Modular Large Model Training on Heterogeneous Infrastructure [Apple] https://arxiv.org/abs/2507.05411 --- [LG] MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment [Tencent AI Seattle Lab] https://arxiv.org/abs/2507.05720 --- [LG] A Dynamical Systems Perspective on the Analysis of Neural Networks [Freie Universitat Berlin & Universiteit van Amsterdam] https://arxiv.org/abs/2507.05164 --- [CL] Perception-Aware Policy Optimization for Multimodal Reasoning [University of Illinois Urbana-Champaign] https://arxiv.org/abs/2507.064 --- [LG] Thousand-Brains Systems: Sensorimotor Intelligence for Rapid, Robust Learning and Inference [Thousand Brains Project] https://arxiv.org/abs/2507.04494 --- [CL] A Survey on Latent Reasoning https://arxiv.org/abs/2507.06203

当你不再要求谁给你一份满分答卷时，你才真正开始书写自己的故事。

00:01:30 人工智能的安全带，真的能系牢吗？ 00:06:39 给人工智能算算账：它能力的边界在哪？ 00:11:34 你一“喜欢”，人工智能就变乖？这事儿没那么简单 00:16:07 高手与普通人的差距，不在于答案，而在于“清单” 00:20:26 想成事？别总想细节，试试“打包”你的行动 本期介绍的五篇论文： [LG] On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment [Ludwig-Maximilians-Universität in Munich & UC Berkeley] https://arxiv.org/abs/2507.07341 --- [CL] Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models [Stanford University & VianAI Systems] https://arxiv.org/abs/2507.07505 --- [LG] Principled Foundations for Preference Optimization [Google DeepMind] https://arxiv.com/abs/2507.07855 --- [LG] PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving [Google] https://arxiv.com/abs/2507.07495 --- [LG] Reinforcement Learning with Action Chunking [UC Berkeley] https://arxiv.com/abs/2507.07969

我们拼命管理着自己的金钱、时间、人脉，却对自己最重要的核心资产——“情绪资产”——视而不见。

00:01:50 人工智能界的“分工”智慧：如何让天才更天才？ 00:06:40 人工智能预测那么准，它真的“懂”了吗？ 00:11:52 人工智能的“最强大脑”？不，是“最省大脑” 00:16:14 训练人工智能，少吃多餐还是狼吞虎咽？ 00:20:39 从笨拙到精通：机器人如何“看”会我们的本事？ 本期介绍的五篇论文： [LG] Towards Solving More Challenging IMO Problems via Decoupled Reasoning and Proving [Tencent AI Lab] arxiv.org --- [LG] What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models [Harvard University & MIT] arxiv.org --- [CL] Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation [Microsoft] arxiv.org --- [LG] Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful [New York University & Columbia University] arxiv.org --- [LG] Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement [Google Deepmind] arxiv.org

23分15秒，是一个普通人，在注意力被一次微小的干扰打断后，重新回到之前深度专注状态所需要付出的平均时间。

00:01:33 “差生”配对，如何“炼”出优等生？ 00:06:09 AI的“刻意练习”：怎样探索才最高效？ 00:10:19 让AI学会顶尖“手艺活”，这事儿靠谱吗？ 00:14:35 黑箱里的光：我们好像找到了AI学习的秘密开关 00:19:47 AI 程序员的“心事”：它真的懂你的需求吗？ 今天介绍的五论文： [LG] The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains [University of Washington] https://arxiv.org/abs/2507.06187 --- [LG] Epistemically-guided forward-backward exploration [ETH Zurich & University of Tübingen] https://arxiv.org/abs/2507.05477 --- [LG] AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs [Tsinghua University] https://arxiv.org/abs/2507.05687 --- [LG] FACT: the Features At Convergence Theorem for neural networks [MIT & UCSD & UC Berkeley] https://arxiv.org/abs/2507.05644 --- [CL] Coding Triangle: How Does Large Language Model Understand Code? [Shanghai AI Laboratory] https://arxiv.org/abs/2507.06138

我们真正要警惕的，不是犯错，而是那种‘万一错了怎么办’的恐惧。

00:01:37 你的 AI，是“记性好”还是“真会学”？ 00:06:19 管好AI的“注意力”：一个“分而治之”的智慧 00:10:22 造个“世界”给AI：为什么光看视频还不够？ 00:16:51 AI团队管理的终极难题：既要、又要、还要，怎么办？ 00:21:45 想把AI训练好？别等“秋后算账” 今天介绍的五篇论文： [LG] Memory Mosaics at scale [New York University & FAIR, Meta Inc] https://arxiv.org/abs/2507.03285 --- [CL] RAT: Bridging RNN Efficiency and Attention Accuracy in Language Modeling [CLAIRE, EPFL & Google DeepMind] https://arxiv.org/abs/2507.04416 --- [LG] Critiques of World Models [CMU] https://arxiv.org/abs/2507.05169 --- [LG] Optimas: Optimizing Compound AI Systems with Globally Aligned Local Rewards [Stanford University] https://arxiv.org/abs/2507.03041 --- [LG] Discrete Diffusion Trajectory Alignment via Stepwise Decomposition [Stanford University & Caltech] https://arxiv.org/abs/2507.04832

“我此刻的行为，是在点燃一支烟火，还是在种下一棵树？是在透支未来的我，还是在投资未来的我？”