AI可可AI生活 - 节目列表

今天，我们要深入AI的“大脑”，看看它是如何真正学会“思考”的。我们会探讨，是给AI请个“陪练”逐步放手，还是给它一张“地图”指引全局更有效？我们还会见证一场AI学习的“龟兔赛跑”，看看“快功夫”和“慢功夫”哪个更有前途。最后，我们将一起揭开AI如何从死记硬背走向融会贯通，以及我们该如何科学地看待它的“成绩单”。五篇最新论文，带你洞悉AI学习的底层智慧。 00:00:34 给AI请个“陪练”，然后悄悄撤走 00:04:54 AI干活，为什么喂给它地图比喂给它字典更管用？ 00:10:35 AI的快功夫与慢功夫 00:16:03 AI对齐，一份被误解的成绩单 00:20:42 AI怎么才能“活”起来，从死记硬背到融会贯通 本期介绍的几篇论文： [LG] CanvasAnneal：Curriculum Reinforcement Learning for Diffusion Language Models [Google DeepMind] https://arxiv.org/abs/2609.13060 --- [AI] Beyond Vector Similarity：Hierarchical Context-Aware Graph RAG vs Standard RAG in Enterprise Code Migration [Google Cloud] https://arxiv.org/abs/2609.12464 --- [CL] Breaking the Token Ceiling：Distilling Smaller， Stronger Byte Models [Meta FAIR & University of Washington, Seattle] https://arxiv.org/abs/2609.12303 --- [LG] Distortion of AI Alignment Revisited：RLHF is a Decent Utilitarian Aligner [UC Berkeley] https://arxiv.org/abs/2609.12651 --- [AI] Hierarchical Prototype Emergence in Modern Hopfield Models [Stanford University] https://arxiv.org/abs/2609.12079

你有没有想过，一群“健忘”的AI如何自发形成群体智慧？我们又该如何为AI量身定做一套“习题集”，培养出“四两拨千斤”的编程高手？本期节目，我们将一起探究几篇最新论文，看看科学家是如何通过“画地图”式的新方法进行信息检索，如何给AI做“信念体检”来判断它是否言行一致，以及如何引导AI从一团乱麻的“噪点”中走出清晰的思考路径。准备好，让我们一起解码AI思考与学习的底层智慧！ 00:00:36 AI界的“四两拨千斤”，如何养出一个小个子编程高手？ 00:05:48 AI的群体智慧，一个动作解释所有 00:11:12 信息检索的内功，从存照片到画地图 00:17:19 AI有“信念”吗？一张体检表告诉你答案 00:22:42 从一团乱麻到清晰答案的思考路径 本期介绍的几篇论文： [AI] FrogNano: Training a 4B Coding Agent via Online Task Synthesis [Froggy Team – Microsoft Research Montréal] https://arxiv.org/abs/2609.07925 --- [CL] Copying explains the collective behavior of AI agents in the wild [University of Konstanz & Intesa Sanpaolo] https://arxiv.org/abs/2609.09150 --- [IR] Generative Late-Interaction Embeddings For Visual Document Retrieval [King Abdullah University of Science and Technology (KAUST)] https://arxiv.org/abs/2609.11808 --- [AI] Beliefs and Behavior in Language Models [Toulouse School of Economics & CMU] https://arxiv.org/abs/2609.07943 --- [LG] Thinking with Looped Flows [EPFL & KAIST & University of Amsterdam] https://arxiv.org/abs/2609.11801

你有没有想过，AI天才也需要“岗前培训”才能上岗？本期我们将从几篇最新论文出发，揭秘如何为AI搭建高效的“培训工厂”，并探索如何教AI学会“做事的方法论”，而不仅仅是“堆知识”。我们还会聊聊一个有趣的问题：AI会为了讨好你而放弃原则、变成一个“老好人”吗？最后，我们将从一个全新的角度，看看合作的本质，也许就藏在最底层的成本计算里。 00:00:32 AI天才出厂后，谁给它做“岗前培训”？ 00:06:42 如何让AI的“学徒”跟上“大师”的脚步 00:11:35 AI的进化，从“知道什么”到“该做什么” 00:17:26 为什么AI会变成一个“老好人”？ 00:22:03 合作的秘密，藏在成本里 本期介绍的几篇论文： [LG] Miles v0.1: Production-Level Post-Training [RadixArk] https://arxiv.org/abs/2609.08368 --- [LG] Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-Training [NVIDIA] https://arxiv.org/abs/2609.07108 --- [AI] Procedural Graphs: Self-Evolving Execution Structures for LLM Agents [Google] https://arxiv.org/abs/2609.09153 --- [CL] Measuring LLM Sycophancy under Sustained Multi-Turn Pressure [Texas A&M University & University of Cincinnati] https://arxiv.org/abs/2609.09090 --- [AI] Tapes Together Strong: The Co-evolution of Computation and Cooperation [Google] https://arxiv.org/abs/2609.10817

AI如何才能学会自我进化，最终成为自己的师傅？为什么解决顶级难题要靠“AI专家团”，而不是一个超级大脑？本期节目，我们将从几篇最新论文出发，探讨AI如何从别人的失败中汲取智慧，看懂“剩饭”为何难倒英雄汉，并理解“看得懂”与“会动手”之间那道巨大的鸿沟。 00:00:26 那个“笨”徒弟，正在悄悄学会自己当师傅 00:07:02 AI解题的秘密，不是一个大脑，而是一套系统 00:11:44 AI的“偏食症”，为什么聪明的模型更讨厌“剩饭”？ 00:17:21 人工智能看得懂，但不会干 00:21:56 如何变得更聪明？答案是，多看看笨蛋是怎么想的 本期介绍的几篇论文： [LG] The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement [Shanghai Jiao Tong University] https://arxiv.org/abs/2609.11873 --- [AI] An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics [NVIDIA] https://arxiv.org/abs/2609.10712 --- [LG] Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data [Stanford University] https://arxiv.org/abs/2609.11917 --- [AI] MindTopo: Can Foundation Models Reason in Topological Space? [Northwestern University] https://arxiv.org/abs/2609.11900 --- [CL] Negative Self-Distillation: Learning to Reason by Avoiding Flaws [University of Virginia] https://arxiv.org/abs/2609.11699

今天，我们将一起“拆开”AI的大脑，看看做决策的竟然只有8个“员工”？我们还会揭秘AI排行榜的“偏科”陷阱，并为它请来一位完美的“虚拟陪练”和一位全能“秘书”。最后，再用一个简单又奇妙的几何学秘密，看穿AI决策的本质。让我们一同进入AI内部，一探究竟！ 00:00:23 AI做决策，到底需要多少“人”帮忙？ 00:05:38 AI排行榜的秘密，为什么第一名可能不是你想要的全才？ 00:11:01 给AI请个“陪练”，它就能开窍？ 00:16:09 给高手配个秘书，怎样才能让他越用越顺手？ 00:21:46 AI决策的“保守”秘密 本期介绍的几篇论文： [CL] Through the Looking Glass: Directly Reading and Writing Transformers [University of Washington] https://arxiv.org/abs/2609.10210 --- [CL] What Does MMLU Actually Measure? A Psychometric Audit of Difficulty Structure in Aggregate Benchmark Scores [Stanford University] https://arxiv.org/abs/2609.09372 --- [LG] World-Time Compute with Verified Code World Models [Quome, Inc.] https://arxiv.org/abs/2609.09163 --- [CL] Osprey: Target-agnostic Pre-training Makes Stronger Drafters in Speculative Decoding [Together AI] https://arxiv.org/abs/2609.09338 --- [LG] Exact-Form Regret for Gradient Descent, Mirror Descent and Follow-the-Regularized-Leader [MIT] https://arxiv.org/abs/2609.09466

想知道AI的“大脑”里，是不是真的在上演一场场激烈的内部辩论？为什么有时候教它，掐头去尾、只给起点和终点，反而能让它学得更快？而面对超级难题，又是怎样一个“笨办法”在引导它一步步走向正确答案？本期节目，我们将深入几篇最新论文，聊聊AI“师傅”如何带出超越自己的“徒弟”，并揭开为什么你手机里的AI和新闻里的跑分冠军，可能是两回事。 00:00:31 AI的“自我否定”，我们误解了它的工作方式 00:05:22 老师傅的旧地图，怎么给新车导航？ 00:10:17 你用的AI，和新闻里的AI，是两回事 00:16:00 为什么掐头去尾，反而教得更好？ 00:19:50 为什么聪明的AI，也需要一个笨办法？ 本期介绍的几篇论文： [CL] LLM Layers Immediately Correct Each Other [UC Berkeley] https://arxiv.org/abs/2609.07876 --- [LG] Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation [KAIST AI] https://arxiv.org/abs/2609.08798 --- [AI] API Benchmark Scores Do Not Reliably Transfer to Chatbot Interfaces [Stanford University] https://arxiv.org/abs/2609.08861 --- [CL] Revisiting Complete Reasoning Traces for Post-Training [NAVER AI Lab] https://arxiv.org/abs/2609.07103 --- [LG] Long-Horizon Language Model Reinforcement Learning via Progressive Point Matching [UC Berkeley] https://arxiv.org/abs/2609.07303

你是否想过，AI不仅能当助手，更能成为科学家的“寻宝图”，预测未来的新发现？本期我们将一起探讨，AI如何学会从“挤牙膏”式写作进化到“一步到位”的神奇魔法，并首次“窥探”它的大脑，看看它是否真的理解了“2+5”和“二加五”的区别。我们还会揭示，如何通过一张“地图”让AI读懂万卷书，以及它学习掌握“祖传手艺”的秘密。 00:00:29 AI 如何成为科学家的「寻宝图」 00:06:12 语言模型，告别“挤牙膏”时代 00:11:33 会做“2+5”，为何不会“二加五”？我们终于有办法偷看AI的大脑了 00:17:29 给AI一张地图，它能更好地为你读书 00:21:54 AI如何拥有“祖传手艺”？ 本期介绍的几篇论文： [LG] Hakken: Predicting future discoveries to fill the gaps in today's knowledge [SonyAI] https://arxiv.org/abs/2609.04494 --- [LG] Distilled Continuous Diffusion Language Models Can Write Code in Few Steps---or One [Duke University & Tsinghua University] https://arxiv.org/abs/2609.04531 --- [CL] Shared circuits predict whether LLMs generalize across formats in arithmetic reasoning [MIT] https://arxiv.org/abs/2609.04463 --- [AI] STAIR (STructure Aware Information Retriever): A novel dataset and LLM based retriever for document structure augmentation [IBM] https://arxiv.org/abs/2609.03874 --- [AI] SkillGLoW: Procedural-Family Skill Consolidation for Self-Improving Agents on Long-Horizon Task Streams [National University of Singapore & Institute of Advanced Intelligence and Computing (IAIC)] https://arxiv.org/abs/2609.02217

你有没有觉得，AI时而像个无所不能的天才，时而又像个会钻牛角尖的“笨小孩”？本期节目，我们将通过几篇最新论文，一探究竟：为何AI会固执地采纳错误答案，又为何会被最简单的“跟我读”骗术“催眠”？同时，我们也将看到AI如何化身“科研总管”，以及一份好的“设计图”为何在未来可能比代码本身更值钱。准备好，让我们一起揭开AI这些既矛盾又迷人的行为背后的秘密。 00:00:33 AI的“小固执”，为什么它信你，却不听你的？ 00:07:48 未来，你的代码可能一文不值 00:13:33 为什么AI解难题，也会钻牛角尖？ 00:18:37 为什么AI会被最简单的骗术“带偏”？ 00:23:09 让AI当科研总管，是一种什么体验？ 本期介绍的几篇论文： [CL] Evidence Integration in Large Language Models [MIT] https://arxiv.org/abs/2609.04290 --- [AI] Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool [Google DeepMind & MIT] https://arxiv.org/abs/2609.05364 --- [LG] Fractal basins trap latent reasoning [The University of Texas at Austin] https://arxiv.org/abs/2609.04963 --- [AI] Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection [UC Berkeley & FAIR at Meta] https://arxiv.org/abs/2609.04533 --- [AI] La Agente Óptima: Towards Agentic Self-Driving Laboratories [University of Toronto & 700 University Ave] https://arxiv.org/abs/2609.04564

你有没有想过，我们能用音乐均衡器的思路，让AI画画提速40%？本期节目，我们将一起钻进AI的“大脑”，看看给它一条笔直的“高速公路”为什么反而会“堵车”，以及如何用一个“科学沙盒”来分辨AI究竟是真正的科学家，还是只会刷题的学霸。我们还会聊到一篇最新论文，它发现了一个几乎被所有人忽略的“小开关”，却能成为大模型训练的超级加速器。让我们一起从这些最新论文中，发现那些大道至简的AI智慧吧！ 00:00:34 AI绘画的“均衡器” 00:04:47 大道至简，AI 设计蛋白质，需要绕多大的弯？ 00:09:13 解锁AIGC的终极速度，从颠簸小路到笔直高速 00:14:36 给AI一个沙盒，看它能不能成为科学家 00:20:56 大模型微调，一个被忽略的开关 本期介绍的几篇论文： [CV] Balancing Frequencies and Pixels in Flow Matching [CNRS] https://arxiv.org/abs/2609.02748 --- [LG] SimpleDesign：A Joint Model for Protein Sequence and Structure Codesign [Apple] https://arxiv.org/abs/2609.03377 --- [CV] A Lagrangian View of Flow Matching [Google] https://arxiv.org/abs/2609.00198 --- [AI] Science sandboxes measure the scientific capability of AI agents [The Broad Institute of MIT and Harvard & The Jackson Laboratory & Sutter Hill Ventures] https://arxiv.org/abs/2608.30165 --- [LG] Normalized Low-Rank Adaptation [Yuanshi Intelligence & Microsoft Research] https://arxiv.org/abs/2608.31036 ---[CV] Balancing Frequencies and Pixels in Flow Matching [CNRS] https://arxiv.org/abs/2609.02748 --- [LG] SimpleDesign：A Joint Model for Protein Sequence and Structure Codesign [Apple] https://arxiv.org/abs/2609.03377 --- [CV] A Lagrangian View of Flow Matching [Google] https://arxiv.org/abs/2609.00198 --- [AI] Science sandboxes measure the scientific capability of AI agents [The Broad Institute of MIT and Harvard & The Jackson Laboratory & Sutter Hill Ventures] https://arxiv.org/abs/2608.30165 --- [LG] Normalized Low-Rank Adaptation [Yuanshi Intelligence & Microsoft Research] https://arxiv.org/abs/2608.31036

你有没有想过，我们能不能像给人指路一样，只对机器人“指一下”就让它心领神会？怎样才能给AI一本“武功秘籍”，让它告别“瞎忙”，拥有真正高手的“手感”？本期节目，我们将通过几篇最新论文，揭示AI如何抛开事物的表象、看见动作的“骨骼”，并探索如何用一个更统一、不“精神分裂”的大脑，来更高效地理解这个世界。 00:00:28 让机器人认路，只需要教它“指一下”？ 00:05:23 让AI告别“瞎忙”，给它一本“武功秘籍” 00:10:35 大模型提速的“第三条路” 00:15:35 抛开皮囊，看见骨骼，机器人怎么学“手艺” 00:20:43 AI的大脑，怎样才能不精神分裂 本期介绍的几篇论文： [RO] LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation [Light Origins Team] https://arxiv.org/abs/2608.30935 --- [AI] Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills [Beijing Academy of Artificial Intelligence] https://arxiv.org/abs/2609.02749 --- [LG] Unlocking Lossless Speedups in LLMs via Discrete Diffusion [Institue of Foundation Models] https://arxiv.org/abs/2609.04010 --- [CV] RoboTok: An Internet-Scale Data Engine for Human Demonstration Retrieval and Dexterous Manipulation Learning [Rice University] https://arxiv.org/abs/2609.03199 --- [IR] NeoMME: A Single-Tower Multimodal-Native Multilingual Foundation Encoder for Efficient Fine-Tuning and Inference [H Company] https://arxiv.org/abs/2609.01657

本期节目，我们将一同潜入几篇最新论文，看看AI如何抛弃“二手经验”直击真实世界，又如何在虚拟社会里学会了作弊与“吹哨”。我们还会发现，AI正通过巧妙的任务拆分和精准分工，努力挣脱“平均分”的陷阱，去追求那极少数的“高光时刻”。这些来自AI的进化心法，或许能给我们带来意想不到的人生启发。 00:28:07 抛弃“二手经验”，直击真实世界，一次预测未来的思维升级 00:05:18 当100个AI被关进同一个房间，它们没有毁灭世界，而是学会了作弊与“吹哨” 00:12:17 把两件事拆开做，到底有多爽？——一篇前沿AI论文里的人生算法 00:18:19 别让所有人都来开会，从AI“混合专家”模型看极简管理与分工智慧 00:24:03 别被“平均分”骗了，从平庸到顶尖，你只需要换一种计分牌 本期介绍的几篇论文： [LG] WeatherNext 3：Increasing resolution and performance of global weather models with raw observations [Google DeepMind & Google Research] https://arxiv.org/abs/2609.03582 --- [AI] A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms [Google DeepMind] https://arxiv.org/abs/2609.04170 --- [LG] Free Pause Tokens [Microsoft & Cornell University] https://arxiv.org/abs/2609.03807 --- [LG] Towards a Statistical Understanding of Mixture-of-Experts [Tsinghua University] https://arxiv.org/abs/2609.03501 --- [LG] Tail-Likelihood Reinforcement Learning [Carnegie Mellon University (CMU)] https://arxiv.org/abs/2609.02987

本期，我们来聊聊AI如何从一个“普通学生”被系统地培养成编程竞赛的世界冠军，甚至超越了人类状元。但与此同时，为什么我们身边的AI助理，处理复杂任务时却常常“走着走着就散架”了？我们又该如何教会AI管理自己的“注意力”，像人一样划重点？以及，如何通过精准定位它“第一次犯错的瞬间”，让它的学习效率实现飞跃？四篇最新论文，带我们深入AI的“学霸心法”，揭示智能背后的策略、局限与成长之道。 00:00:37 AI学会考试了，而且比状元考得还好 00:06:06 你的AI助理，为啥走着走着就“散架”了？ 00:11:30 AI的注意力，该由谁做主？ 00:16:47 如何让机器学会聪明，抓住第一次犯错的瞬间 00:22:08 知识的“断舍离”，我们究竟该记住什么？ 本期介绍的几篇论文： [LG] Post-Training Language Models for Gold-Medal Performance in Coding Competitions [NVIDIA] https://arxiv.org/abs/2609.02849 --- [AI] How Fast Do Agents Rot? An Empirical Study of Long-Horizon Degradation in LLM Agents for Production Decision-Making [Microsoft AI] https://arxiv.org/abs/2609.01660 --- [CL] Language Models Can Control Their Own Attention [KAIST AI & Google DeepMind] https://arxiv.org/abs/2609.02737 --- [LG] Cliff: Learning Process Rewards from the First Mistake [Amazon Web Services] https://arxiv.org/abs/2609.02817 --- [LG] What Is Worth Representing? Representational Empowerment for Continual Model Construction [UC Berkeley & University of Tübingen] https://arxiv.org/abs/2609.02322