AI的“内心世界”到底是什么样的？本期我们将一起打开AI的“黑箱”，看看它脑中的概念是不是像一张清晰的地图；它又是如何像玩乐高一样，用旧知识拼出新世界的？当AI学画画时，我们该给它请一位“博学”还是“懂行”的老师？又该怎样为它修一条又快又好的创作高速路？最后，我们还将探讨，如何让机器通过“抬杠”的方式，反过来帮我们理清自己的主观想法。 00:00:35 驯服“黑箱”：AI世界的一条极简法则 00:05:30 拼凑“旧知识”，创造“新世界” 00:10:22 AI绘画的“老师”，聪明和懂行哪个更重要？ 00:15:48 造物者的新工具：AI画画怎样才能又快又好？ 00:21:10 如何让机器学会你的“个人口味”？ 本期介绍的几篇论文： [LG] Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality [CMU] https://arxiv.org/abs/2512.10720 --- [LG] Learning by Analogy: A Causal Framework for Composition Generalization [CMU & Amazon] https://arxiv.org/abs/2512.10669 --- [CV] What matters for Representation Alignment: Global Information or Spatial Structure? [Adobe Research & ANU] https://arxiv.org/abs/2512.10794 --- [LG] Bidirectional Normalizing Flow: From Data to Noise and Back [MIT] https://arxiv.org/abs/2512.10953 --- [CV] Agile Deliberation: Concept Deliberation for Subjective Visual Classification [Google Research] https://arxiv.org/abs/2512.10821

你有没有想过，我们不仅能使用AI，还能看透它的“内心世界”吗？本期，我们将跟随几篇最新论文，扮演一次AI的建筑师、心理医生和侦探，看看如何重塑一个更省钱的推荐系统，如何看穿AI从无害信息中“悟”出的危险想法，以及如何用一把“手术刀”解剖并教会它举一反三。让我们一起出发！ 00:00:28 推荐系统的大难题：如何让它更聪明，还更省钱？ 00:05:59 你教AI画苹果，它却学会了当海盗 00:14:26 我们终于有了一把解剖AI的“手术刀” 00:20:44 AI侦探：你的模型到底跟谁学的？ 00:27:06 教AI举一反三：为什么聪明的模型，一到考场就蒙圈？ 本期介绍的几篇论文： [IR] Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations [Meta AI] https://arxiv.org/abs/2512.09200 --- [CL] Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs [Truthful AI] https://arxiv.org/abs/2512.09742 --- [LG] Provably Learning from Modern Language Models via Low Logit Rank [Microsoft Research & UC Berkeley & MIT] https://arxiv.org/abs/2512.09892 --- [LG] Natural Geometry of Robust Data Attribution: From Convex Models to Deep Networks [The University of Texas at Austin] https://arxiv.org/abs/2512.09103 --- [LG] Closing the Train-Test Gap in World Models for Gradient-Based Planning [Columbia University] https://arxiv.org/abs/2512.09929

我们总以为AI越强，就意味着模型越大、思考越久，但今天我们要聊点不一样的。本期几篇最新论文告诉我们，AI有时也会“近视”，只看眼前；但它也能学会“团队作战”，把一个难题拆开并行处理。我们还会看到，AI画画不必等到最后一刻才知好坏，企业抓坏人也不再需要昂贵的“博士专家”。最后，我们会通过一个巧妙的实验，揭示学习的本质——有时，搭好“脚手架”比闷头苦练更重要。 00:00:35 大模型其实是“近视眼”？ 00:05:36 从单核思考到团队作战：AI的效率革命 00:11:34 AI绘画的“中场战事”：如何省下一半力气，画出更好的图？ 00:16:31 给你的网络安个“最强大脑”，但不用请博士 00:22:17 学习的“脚手架”：为什么“学会”比“做会”需要更多信息？ 本期介绍的几篇论文： [CL] Short-Context Dominance: How Much Local Context Natural Language Actually Needs? [University of British Columbia & Google DeepMind] https://arxiv.org/abs/2512.08082 --- [LG] ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models [Meta Superintelligence Labs (MSL)] https://arxiv.org/abs/2512.07843 --- [CV] Beyond the Noise: Aligning Prompts with Latent Representations in Diffusion Models [NOVA University of Lisbon & Google Research] https://arxiv.org/abs/2512.08505 --- [AI] Democratizing ML for Enterprise Security: A Self-Sustained Attack Detection Framework [Google LLC] https://arxiv.org/abs/2512.08802 --- [LG] Using reinforcement learning to probe the role of feedback in skill acquisition [ETH Zürich] https://arxiv.org/abs/2512.08463

本期节目，我们将一起探索AI智能的几种迷人形态。一个从没上过网的AI，如何靠“顿悟”来解题？一个摇摆不定的AI，如何被调教得“心中有谱”？一个笨学生，又是如何通过一套“教育学”秘籍，成为推理高手的？最后，我们还会聊聊如何给AI团队“动手”纠错，并用一把尺子精确量出它的“记忆深度”。准备好了吗？让我们一起出发！ 00:00:31 造一个聪明的AI，需要喂它整个互联网吗？ 00:07:16 告别左右摇摆：如何让机器学会有个“准星”？ 00:12:27 如何把一个“笨学生”调教成解题高手？ 00:19:59 别再当事后诸葛亮，试试“动手”来纠错 00:25:43 你的AI有多健忘？我们终于有了一把尺子 本期介绍的几篇论文： [LG] ARC-AGI Without Pretraining [CMU] https://arxiv.org/abs/2512.06104 --- [LG] Average-reward reinforcement learning in semi-Markov decision processes via relative value iteration [University of Alberta] https://arxiv.org/abs/2512.06218 --- [CL] On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models [CMU] https://arxiv.org/abs/2512.07783 --- [AI] DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems [Microsoft & Chinese Academy of Sciences] https://arxiv.org/abs/2512.06749 --- [LG] Quantifying Memory Use in Reinforcement Learning with Temporal Range [MIT] https://arxiv.org/abs/2512.06204