我们的大脑拥有惊人的神经可塑性，一个微小的视角转变，就能重塑整个情感体验的版图。

00:00:36 AI“减肥”的秘密，被一个40年前的算法破解了 00:05:46 驯服AI的终极武器：不是模糊奖励，而是清晰清单 00:09:56 AI的“开窍”秘诀：如何教机器像高手一样思考？ 00:14:53 给AI请个好私教的秘密 00:19:50 给AI画张图，它能看得更明白吗？ 本期介绍的五篇论文： [LG] The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm [ISTA & ETH Zürich] https://arxiv.org/abs/2507.18553 --- [CL] Checklists Are Better Than Reward Models For Aligning Language Models [CMU & Apple] https://arxiv.org/abs/2507.18624 --- [LG] Revisiting LLM Reasoning via Information Bottleneck [The University of Sydney & ByteDance] https://arxiv.org/abs/2507.18391 --- [CL] TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards [EPFL] https://arxiv.org/abs/2507.18618 --- [LG] Does visualization help AI understand data? [Harvard University] https://arxiv.org/abs/2507.18022

当现实变得破碎、重复、无法忍受时，是故事，将这些碎片重新粘合，赋予我们一个可以继续走下去的理由。

00:00:30 给AI一支笔，它能撬动地球吗？ 00:04:45 给AI请个“外援”，裁判才能更靠谱 00:09:09 和AI说话的艺术：你以为是聊天，其实是盖楼 00:14:29 AI界的“庖丁解牛”：如何用“乐高积木”搞定复杂工程难题？ 本期介绍的四篇论文： [LG] Thinking Isn't an Illusion: Overcoming the Limitations of Reasoning Models via Tool Augmentations [UC Berkeley & Northeastern University] https://arxiv.org/abs/2507.17699 --- [CL] Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge? [University of Cambridge & Apple] https://arxiv.org/abs/2507.17015 --- [LG] Understanding Prompt Programming Tasks and Questions [CMU & JetBrains Research] https://arxiv.org/abs/2507.17264 --- [LG] A Learning-based Domain Decomposition Method [University of Cambridge & NVIDIA] https://arxiv.org/abs/2507.17328

生活的秘密，不在于“发现美”，而在于“不断发现”。它不是一个结果，而是一个动词，一种永不停止的探索姿态。

00:01:32 想让AI变聪明？关键不是喂饱它，而是教它如何“犯错” 00:07:12 你的“电量焦虑”，AI来想办法了？ 00:11:21 给AI装上一个“选择性失忆”的大脑 00:15:41 AI“现学现卖”的秘密，可能被揭开了 00:21:06 AI也需要“自知之明”：我们如何教会机器谦虚？ 本期介绍的几篇论文： [LG] From Reasoning to Super-Intelligence: A Search-Theoretic Perspective S Shalev-Shwartz, A Shashua https://arxiv.org/abs/2507.15865 --- [LG] Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization S Liu, H Xu, Y Ai, H Li... Université de Montréal & University of Oxford https://arxiv.org/abs/2507.16110 --- [CL] Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning H Luo, N Morgan, T Li, D Zhao... MIT CSAIL https://arxiv.org/abs/2507.16784 --- [CL] Learning without training: The implicit dynamics of in-context learning B Dherin, M Munn, H Mazzawi, M Wunder... Google Research https://arxiv.org/abs/2507.16003 --- [LG] Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty M Damani, I Puri, S Slocum, I Shenfeld... MIT https://arxiv.org/abs/2507.16806

通过给你即时的、廉价的多巴胺回馈，社交媒体的AI算法让你沉迷于“宣布”和“被看见”的快感，却在无形中，悄悄摧毁了你为真正达成目标所必需的、那套更为珍贵的内在奖赏系统。

00:01:15 AI变“聪明”的代价：那根看不见的绳子 00:05:47 给AI“长考”时间，它怎么反而变笨了？ 00:11:35 AI的“潜移默化”：看不见的信号，看得见的风险 00:15:45 AI教育的秘诀：一句“再试试”的力量 00:19:28 高手记忆的秘密：不是记得多，而是会“扔” 本期介绍的几篇论文： [LG] The Invisible Leash: Why RLVR May Not Escape Its Origin [Stanford University & University of Tokyo] https://arxiv.org/abs/2507.14843 --- [LG] Inverse Scaling in Test-Time Compute [University of Edinburgh & EPFL & University of Texas at Austin] https://arxiv.org/abs/2507.14417 --- [LG] Subliminal Learning: Language models transmit behavioral traits via hidden signals in data [Anthropic Fellows Program & Truthful AI] https://arxiv.org/abs/2507.14805 --- [LG] A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning [Imperial College London & Northwestern University & University of Washington] https://arxiv.org/abs/2507.14295 --- [LG] LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models [Georgia Tech] https://arxiv.org/abs/2507.14204

“人生的境界是在不断探索中拓展的，而非在既有框架中满足。” - 王国维《人间词话》

00:01:07 AI当学徒：如何把“笨学生”调教成“优化大师”？ 00:05:48 AI专家速成指南：换个姿势，我们能造出“神医”吗？ 00:10:48 从“大力出奇迹”到“精准打击”：AI的进化新思路 00:14:59 AI的“偏见”：它如何看透纷繁信息，抓住本质？ 00:19:50 让AI学会“多想一步”：笨办法里的真智慧 本期介绍的几篇论文： [LG] CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning [DeepReinforce Team] https://arxiv.org/abs/2507.14111 --- [CL] Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need [Princeton University] https://arxiv.org/abs/2507.13966 --- [LG] Reframing attention as a reinforcement learning problem for causal discovery [University of Tübingen & University of Amsterdam] https://arxiv.org/abs/2507.13920 --- [LG] Provable Low-Frequency Bias of In-Context Learning of Representations [University of Michigan & Harvard University] https://arxiv.org/abs/2507.13540 --- [LG] Change of Thought: Adaptive Test-Time Computation [Google Research & Georgia State University & Maynooth University] https://arxiv.org/abs/2507.13569

他一直向往着远方的冰岛，却没发现，真正持续为他‘充电’的，是方圆五百米内的这些微小互动和日常之美。

00:01:37 如何让你的机器人管家，不再健忘？ 00:05:43 你家的AI，可能有个“邪恶双胞胎” 00:09:30 AI：一面照见我们自己的镜子 00:13:20 AI训练场上的“复古”新潮流 00:18:14 AI的“复盘”神功：如何想得更深？ 00:21:26 驯养AI：我们是如何让机器“猜”出你心思的？ 本期介绍的几篇论文： [RO] Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering [Field AI] https://arxiv.org/abs/2507.12846 --- [CL] Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility [FAR.AI & Mila] https://arxiv.org/abs/2507.11630 --- [CL] Emergence of Hierarchical Emotion Organization in Large Language Models [Harvard University] https://arxiv.org/abs/2507.10599 --- [LG] ShiQ: Bringing back Bellman to LLMs [Cohere] https://arxiv.org/abs/2505.11081 --- [LG] AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling [University of Cambridge] https://arxiv.org/abs/2507.08567 --- [LG] Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities [Not explicitly stated, likely academic tutorial paper] https://arxiv.org/abs/2507.13158