大大方方地做自己吧，带着你所有的“不完美”和“异常值”。

00:00:34 AI侦探养成记：如何让机器学会“死磕到底”？ 00:04:22 AI也需要“元学习”：如何打造一把能开万能锁的钥匙？ 00:07:56 拆解AI大脑：它如何学会“绕个弯”解决问题？ 00:12:21 AI学会“举一反三”的秘密：两层楼就够了？ 00:16:34 AI思考的秘密：为什么“少”就是“多”？ 本期介绍的五篇论文： [CL] Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL [Tsinghua University] https://arxiv.org/abs/2508.079 --- [LG] AdaptFlow: Adaptive Workflow Optimization via Meta-Learning [Peking University & University of Chinese Academy of Sciences] https://arxiv.org/abs/2508.08053 --- [LG] Multi-head Transformers Provably Learn Symbolic Multi-step Reasoning via Gradient Descent [CMU & UPenn & OSU] https://arxiv.org/abs/2508.08222 --- [LG] What One Cannot, Two Can: Two-Layer Transformers Provably Represent Induction Heads on Any-Order Markov Chains [MIT & EPFL & UC Berkeley] https://arxiv.org/abs/2508.07208 --- [CL] Less Is More: Training-Free Sparse Attention with Global Locality for Efficient Reasoning [Princeton University & CMU] https://arxiv.org/abs/2508.07101

那个能一键“扭转乾坤”的开发者，不是别人，就是你自己。

00:00:30 AI界的“学霸”是怎么炼成的？ 00:04:45 你的下一个AI，为什么必须是个“行动派”？ 00:08:35 AI当上帝：从零开始创造一门语言 00:13:09 如何给AI大模型“摸骨”，看透它的知识边界？ 本期介绍的四篇论文： [CL] UR²: Unify RAG and Reasoning through Reinforcement Learning [Tsinghua University & Hebei University of Economics and Business] https://arxiv.org/abs/2508.06165 --- [CL] GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models [Zhipu AI & Tsinghua University] https://arxiv.org/abs/2508.06471 --- [CL] ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline [Tel Aviv University & UC Berkeley] https://arxiv.org/abs/2508.06094 --- [CL] Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddings [Georgia Institute of Technology & MIT] https://arxiv.org/abs/2508.06030

你最强大的对手，和你最伟大的盟友，都是你。

00:00:35 我们永远无法根除AI的“幻觉”，但可以学会与它共舞 00:04:28 人工智能的“笨功夫”：一个鸟类识别模型教给我们的事 00:08:44 AI世界的“计分板”，正在悄悄升级 00:12:25 AI如何学会当数学家？三个你也能用的“笨”办法 00:17:15 AI码农进化论：如何“调教”一个更聪明的程序员？ 本期介绍的五篇论文： [CL] A comprehensive taxonomy of hallucinations in Large Language Models [Universitat de Barcelona] https://arxiv.org/abs/2508.01781 --- [LG] Perch 2.0: The Bittern Lesson for Bioacoustics [Google DeepMind] https://arxiv.org/abs/2508.04665 --- [CL] CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [Shanghai AI Laboratory] https://arxiv.org/abs/2508.03686 --- [LG] Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction [Princeton University & Tsinghua University] https://arxiv.org/abs/2508.03613 --- [LG] Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning [Nebius AI] https://arxiv.org/abs/2508.03501

大多数人缺少的，不是天赋和雄心，而是一种人为创造的“紧迫感”。

00:00:31 AI的“思考”，会不会只是个高明的“学霸”？ 00:04:41 AI写稿，快一点和好一点，哪个更重要？ 00:09:15 AI进化论：如何把“草台班子”训练成“梦之队”？ 00:13:51 AI的“左右互搏”：不靠人类，如何自我进化？ 00:17:31 AI的“读心术”：我们能看懂它的“脑回路”吗？ 本期介绍的五篇论文： [LG] Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens [Arizona State University] https://arxiv.org/abs/2508.01191 --- [CL] Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference [ByteDance Seed & Tsinghua University] https://arxiv.org/abs/2508.02193 --- [CL] Multi-module GRPO: Composing Policy Gradients and Prompt Optimization for Language Model Programs [University of Notre Dame & Stanford University & UC Berkeley] https://arxiv.org/abs/2508.04660 --- [CL] R-Zero: Self-Evolving Reasoning LLM from Zero Data [Tencent AI Seattle Lab] https://arxiv.org/abs/2508.05004 --- [LG] Decomposing Representation Space into Interpretable Subspaces with Unsupervised Learning [Saarland University] https://arxiv.org/abs/2508.01916

放弃一个“不适合自己的生活方式”，就意味着一个“全新的开始”。

00:41:15 AI防忽悠指南：如何让聪明的机器不说胡话？ 00:05:37 想变强？别再刷旧题了，你得学会自己“造”难题 00:10:06 AI进阶的秘密：一行代码如何让“学霸”真正开窍？ 00:14:46 AI的新玩法：从“搬运工”到“侦探” 00:19:10 AI也会“想太多”？聊聊如何给模型一颗“定心丸” 本期介绍的五篇论文： [CL] Learning to Reason for Factuality [FAIR at Meta] https://arxiv.org/abs/2508.05618 --- [CL] MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy [Tsinghua University] https://arxiv.org/abs/2508.05592 --- [LG] On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification [Southeast University & University of California, Los Angeles] https://arxiv.org/abs/2508.05629 --- [LG] GRAIL: Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoning [Tsinghua University] https://arxiv.org/abs/2508.05498 --- [CL] Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression [Peking University & The Hong Kong University of Science and Technology] https://arxiv.org/abs/2508.05337

从一个微小的意愿，到一个根植于你灵魂深处的习惯，这趟旅程，就像一场炼金术。

00:00:37 AI也需要“断舍离” 00:05:14 AI也懂抄近道：当你的电脑助理学会了编程 00:09:00 你的下一件乐器，何必是乐器？ 00:12:50 如何给AI装上一个“高情商”的大脑？ 00:17:01 你的下一位同事，可能就是你的电脑本身 本期介绍的五篇论文： [CL] Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management [Tsinghua University] https://arxiv.org/abs/2508.04664 --- [AS] Live Music Models [Google DeepMind] https://arxiv.org/abs/2508.04651 --- [CL] CoAct-1: Computer-using Agents with Coding as Actions [University of Southern California & Salesforce Research] https://arxiv.org/abs/2508.03923 --- [CL] Sotopia-RL: Reward Design for Social Intelligence [University of Illinois Urbana-Champaign & Carnegie Mellon University] https://arxiv.org/abs/2508.03905 --- [LG] OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use https://arxiv.org/abs/2508.04482