AI可可AI生活

今天，我们要深入AI的“内心世界”，去探寻几个颠覆性的问题：聪明的AI，是该学会“胸有成竹”的规划，还是“选择性失忆”的智慧？我们该如何教会一个AI坦然承认“我不知道”，甚至让它比“学霸”更可靠？最新几篇论文，将带我们从AI的“顿悟”规律和推理模式中，找到这些问题的答案。00:00:28 AI的“顿悟”，它如何学会把“走一步看一步”变成“胸有成竹”？00:06:42 为什么说，聪明的AI要学会“选择性失忆”？00:13:03 AI为什么总在“卡关”和“顿悟”之间横跳？00:19:26 如何让一个“学渣”AI，比“学霸”更靠谱？00:25:26 从终点出发，如何让AI学会“开窍”本期介绍的几篇论文：[LG] Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning[Google]https://arxiv.org/abs/2512.20605---[CL] ABBEL: LLM Agents Acting through Belief Bottlenecks Expressed in Language[UC Berkeley]https://arxiv.org/abs/2512.20111---[LG] Saddle-to-Saddle Dynamics Explains A Simplicity Bias Across Neural Network Architectures[University College London]https://arxiv.org/abs/2512.20607---[LG] Mitigating LLM Hallucination via Behaviorally Calibrated Reinforcement Learning[ByteDance Seed]https://arxiv.org/abs/2512.19920---[LG] Learning to Reason in LLMs by Expectation Maximization[Adobe Research & KAIST]https://arxiv.org/abs/2512.20169

你有没有想过，当AI独自“思考”时，它的小脑袋里都在发生什么？本期节目，我们将深入AI的“内心世界”，看看最新论文是如何教会AI像武林高手一样“左右互搏”来自我进化，如何给它装上一个懂得“反思”的脑子来攻克数学难题，又是如何发现它在画画时竟然会悄悄“抄近道”的。更神奇的是，我们还会聊到如何用“坏指令”教出“好模型”，以及如何为AI请来一位绝对公正的“铁面裁判”。准备好了吗？让我们一起揭开AI“内心戏”的神秘面纱！00:00:39 顶级高手的训练秘籍，AI的“左右互搏术”00:06:00 AI也会算错数？给它一个“反思”的脑子00:11:10 AI训练的“左右互搏”，用坏指令，教出好模型00:16:29 如何让AI拥有一个既出题、又陪练、还绝对公正的“完美教练”？00:22:47 你的AI听话吗？它可能在悄悄“抄近道”本期介绍的几篇论文：[AI] Toward Training Superintelligent Software Agents through Self-Play SWE-RL [Meta FAIR & Meta TBD Lab] https://arxiv.org/abs/2512.18552 ---[CL] MDToC: Metacognitive Dynamic Tree of Concepts for Boosting Mathematical Problem-Solving of Large Language Models [University of Maryland] https://arxiv.org/abs/2512.18841 ---[LG] Recontextualization Mitigates Specification Gaming without Modifying the Specification [MATS] https://arxiv.org/abs/2512.19027 ---[AI] Propose, Solve, Verify: Self-Play Through Formal Verification [CMU] https://arxiv.org/abs/2512.18160 ---[LG] Is Your Conditional Diffusion Model Actually Denoising? [MIT & Yale University] https://arxiv.org/abs/2512.18736

本期节目，我们将一起潜入AI的“思想内核”，看看科学家们是如何像物理学家一样，为AI搭建“比萨斜塔”来找到最关键的架构“补丁”；如何为AI的思考过程立下“定律”，让它不再“乱使劲”；我们还会聊聊，怎样将我们模糊的“感觉”变成一把精准的AI“标尺”；如何找到AI训练中那条介于“跳跃”和“龟行”之间的最优路径；以及如何打造一个既能学得像人类专家，又能开得稳的AI“老司机”团队。准备好了吗？让我们一起出发！00:00:37 AI研究的“比萨斜塔”：我们看清模型强弱的方式可能错了00:08:29 给AI立规矩：聪明的大脑是如何炼成的？00:14:59 AI训练的“最优解”：在跳跃和龟行之间找到第三条路00:20:32 你的“感觉”，如何变成AI的“标尺”？00:25:56 如何让AI司机，既学得像，又开得稳？本期介绍的几篇论文：[CL] Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers[FAIR at Meta]https://arxiv.org/abs/2512.17351---[CL] When Reasoning Meets Its Laws[University of Illinois Urbana-Champaign & University of Pennsylvania]https://arxiv.org/abs/2512.17901---[LG] Smoothing DiLoCo with Primal Averaging for Faster Training of LLMs[Meta Superintelligence Lab]https://arxiv.org/abs/2512.17131---[CL] AutoMetrics: Approximate Human Judgements with Automatically Generated Evaluators[Stanford University & American Express]https://arxiv.org/abs/2512.17267---[LG] Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy[University of Illinois Urbana-Champaign & University of Pennsylvania]https://arxiv.org/abs/2512.17899

你有没有想过，AI的进化不只靠“大力出奇迹”？今天我们要聊点更聪明的：比如，给3D世界换上一种全新的“智能积木”；不造新车，而是给最强的大模型巧妙“换上新引擎”；甚至通过分离“骨架”与“灵魂”，让数字世界变得前所未有的高效。本期节目，我们将通过几篇最新论文，揭示那些重塑AI底层逻辑的优雅巧思，看看AI是如何在看不见的地方，悄悄完成自我进化的。00:00:33 一套“智能积木”如何解锁3D世界？00:06:23 AI大模型的新玩法：不造新车，只换发动机00:14:06 AI提速的关键：不只靠“算得快”00:22:05 3D世界的新法则：分离骨架与灵魂00:27:00 AI的“记忆”难题，决定了它离我们还有多远本期介绍的几篇论文：[CV] Native and Compact Structured Latents for 3D Generation [Tsinghua University & Microsoft Research] https://arxiv.org/abs/2512.14692 ---[CL] Bolmo: Byteifying the Next Generation of Language Models [Allen Institute for AI & University of Washington] https://arxiv.org/abs/2512.15586 ---[LG] SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations [Princeton University & UC Berkeley] https://arxiv.org/abs/2512.14080 ---[CV] Nexels: Neurally-Textured Surfels for Real-Time Novel View Synthesis with Sparse Geometries [University of Toronto & Simon Frasier University] https://arxiv.org/abs/2512.13796 ---[CL] Memory in the Age of AI Agents [National University of Singapore & Renmin University of China] https://arxiv.org/abs/2512.13564

今天，我们要从一个笨拙的机器人聊起，看科学家如何赋予它有趣的灵魂，再深入探讨如何让聪明的AI学会“守规矩”，而不是总给我们添乱。接着，我们会发现，让AI修图不再“P了个寂寞”的秘诀，竟然是让它学会像设计师一样思考；而让AI“看懂”世界的终极答案，可能和教它“说话”一样简单。最后，我们将把视角拉到未来，看看当无数AI组成一个“数字社会”时，我们该如何治理它，而不是空等一个AI大神的降临。00:00:36 笨拙的机器人，如何拥有有趣的灵魂？00:05:28 AI那么聪明，为什么还那么“笨”？00:12:26 你的AI修图，为什么总是“P了个寂寞”？00:17:39 AI视觉的“返璞归真”：从做拼图到学说话00:22:39 AI大神不会降临，但AI社会正在形成本期介绍的几篇论文：[RO] Olaf: Bringing an Animated Character to Life in the Physical World [Disney Research Imagineering] https://arxiv.org/abs/2512.16705 ---[LG] CAPE: Capability Achievement via Policy Execution [Superficial Labs] https://arxiv.org/abs/2512.14761 ---[CV] Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition [HKUST(GZ) & Alibaba] https://arxiv.org/abs/2512.15603 ---[CV] Next-Embedding Prediction Makes Strong Vision Learners [University of Michigan & Princeton University] https://arxiv.org/abs/2512.16922 ---[AI] Distributional AGI Safety [Google DeepMind] https://arxiv.org/abs/2512.16856