00:00:37 AI界的乐高革命：如何让你的模型“活”在当下 00:05:41 AI的“手术刀”：我们如何精准“切除”它的坏心思？ 00:10:22 机器人学功夫：抄近道，还是练笨功？ 00:15:03 AI怎么当学徒：让机器学会看“领导脸色” 00:20:18 给AI动个“小手术”，治好它的“选择困难症” 本期介绍的几篇论文： [LG] SequenceLayers: Sequence Processing and Streaming Neural Networks Made Easy [Google DeepMind] https://arxiv.org/abs/2507.23292 --- [LG] The Geometry of Harmfulness in LLMs through Subconcept Probing [Algoverse AI Research] https://arxiv.org/abs/2507.21141 --- [LG] Retrieve-Augmented Generation for Speeding up Diffusion Policy without Additional Training [The University of Tokyo] https://arxiv.org/abs/2507.21452 --- [LG] NPO: Learning Alignment and Meta-Alignment through Structured Human Feedback [Microsoft & Amrita Vishwa Vidyapeetham] https://arxiv.org/abs/2507.21131 --- [LG] TokenBlowUp: Resolving Representational Singularities in LLM Token Spaces via Monoidal Transformations [University of Washington] https://arxiv.org/abs/2507.19747

在你的生活中，是否也有一套需要重写的“人生代码”？那个让你内耗的“BUG”，或许就藏在你对世界过高的期待里。

00:00:37 你的AI管家，靠谱吗？一份来自未来的安全报告 00:04:40 AI“发疯”？科学家找到了它的“性格开关” 00:09:33 比结果更重要的，是“想明白”的过程 00:14:09 AI的“降维打击”：复杂世界里的简单活法 00:18:23 AI的“暖男”人设，可能是个陷阱？ 本期介绍的几篇论文： [LG] Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [Gray Swan AI] https://arxiv.org/abs/2507.20526 --- [CL] Persona Vectors: Monitoring and Controlling Character Traits in Language Models [Anthropic Fellows Program & Constellation] https://arxiv.org/abs/2507.21509 --- [LG] RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents [Tencent] https://arxiv.org/abs/2507.22844 --- [LG] Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces [Brown University & Amazon Web Services] https://arxiv.org/abs/2507.20853 --- [CL] Training language models to be warm and empathetic makes them less reliable and more sycophantic [University of Oxford] https://arxiv.org/abs/2507.21919 --- [CL] On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey [Not explicitly stated, survey paper] https://arxiv.org/abs/2507.20783

“你思索之事，决定了你心智的品质。你的灵魂，会沾染上你思想的色彩。”

00:00:31 你的AI有多聪明？关键看它会不会“彩排” 00:05:06 AI内卷时代，如何找到“天选之子”？ 00:09:38 AI的“自我修养”：如何让机器自己教自己？ 00:13:34 给AI装个方向盘，指哪打哪 00:17:37 AI巨头里的“扫地僧”：没有他，AI秒变“人工智障” 本期介绍的五篇论文： [LG] SimuRA: Towards General Goal-Oriented Agent via Simulative Reasoning Architecture with LLM-Based World Model [Mohamed bin Zayed University of Artificial Intelligence & Samsung Research] https://arxiv.org/abs/2507.237 --- [LG] Consensus-Driven Active Model Selection [MIT & UMass Amherst] https://arxiv.org/abs/2507.23771 --- [CL] CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks [FAIR at Meta & NYU] https://arxiv.org/abs/2507.237 --- [CL] Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders [Columbia University] https://arxiv.org/abs/2507.23220 --- [CL] Unveiling Super Experts in Mixture-of-Experts Large Language Models [Meituan & Tsinghua University] https://arxiv.org/abs/2507.23279

或许，真正的成长，就始于你收回期待，敢于让某些人失望的那一刻。

00:00:38 AI进化论：从“打工人”到“CEO” 00:03:57 驯服AI的秘密：一句话，放在哪儿说效果大不同 00:07:58 你看到的，就是真相吗？——AI给我们的一个新警告 00:12:40 数据稀缺的时代，如何拼出一张完整的世界地图？ 00:17:23 AI正在偷偷学心理学，但好像学偏了 本期介绍的五篇论文： [LG] MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines [University of Wisconsin - Madison] https://arxiv.org/abs/2507.22606 --- [CL] Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning [University of Maryland] https://arxiv.org/abs/2507.22887 --- [LG] Representation biases: will we achieve complete understanding by analyzing representations? [Google DeepMind] https://arxiv.org/abs/2507.22216 --- [LG] AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data [Google DeepMind] https://arxiv.org/abs/2507.22291 --- [LG] The Incomplete Bridge: How AI Research (Mis)Engages with Psychology [Johns Hopkins University & Rice University & Microsoft Research Asia] https://arxiv.org/abs/2507.22847

我们花了无数精力去设计能战胜人类的AI，却很少思考，如何为自己设计一个能战胜‘人性’的系统。

00:00:33 高手与普通人的差距，在于“记忆预算”的分配 00:04:16 AI当“牛顿”：我们如何找到万物生长的公式？ 00:08:26 让AI不止听话，更要会提问 00:12:09 AI 思考的艺术：如何做到又快又好？ 00:18:06 AI识人心：20盘棋，就“看穿”了你 本期介绍的五篇论文： [LG] Capacity-Constrained Continual Learning [Google DeepMind] https://arxiv.org/abs/2507.21479 --- [LG] EvoSLD: Automated Neural Scaling Law Discovery With Large Language Models [Peking University & Tsinghua University] https://arxiv.org/abs/2507.21184 --- [LG] Teaching Language Models To Gather Information Proactively [Microsoft] https://arxiv.org/abs/2507.21389 --- [LG] TriangleMix: A Lossless and Efficient Attention Pattern for Long Context Prefilling [Microsoft Research] https://arxiv.org/abs/2507.21526 --- [LG] Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess [University of Toronto] https://arxiv.org/abs/2507.21488

关于成功，总有一个变量，是连最强的AI都难以计算的，这个变量，就藏在我们今天的故事里。

00:00:34 让AI更“有谱儿”：不止一条路通罗马 00:05:21 如何让AI更聪明？一个“求稳”的智慧 00:09:14 造车新智慧：如何用“搬沙子”的办法，算出最省油的外形？ 00:12:31 AI也会“路径依赖”？一个简单动作，让它“老树发新芽” 00:17:01 AI炼丹术：我们如何“教会”机器遵守化学规则？ 00:21:50 AI进化论：从“死记硬背”到“自我成长” 本期介绍的几篇论文： [LG] Flow Matching Policy Gradients [UC Berkeley] https://arxiv.org/abs/2507.21053 --- [CL] Geometric-Mean Policy Optimization [Microsoft Research] https://arxiv.org/abs/2507.20673 --- [LG] Geometric Operator Learning with Optimal Transport [California Institute of Technology & Nvidia] https://arxiv.org/abs/2507.20065 --- [LG] What Can Grokking Teach Us About Learning Under Nonstationarity? [Google DeepMind] https://arxiv.org/abs/2507.20057 --- [LG] Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling [MIT] https://arxiv.org/abs/2507.19799 --- [LG] A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence https://arxiv.org/abs/2507.21046

当你能清晰地分辨出什么是值得你全身心投入的‘信号’，并且有能力为它创造一个‘无菌舱’去深度运算时，你就不再是时间的囚徒，而是与时间共舞的伙伴。