你不是宇宙中的一粒微尘，整个宇宙，都是你思想中的一件‘玩具’。

00:01:45 你的走法，决定了你是谁 00:05:57 AI翻译哪家强？我们终于有了个“懂行”的裁判 00:10:03 成长的秘密：如何让机器像高手一样思考和探索 00:14:13 AI的“口是心非”：我们如何看穿它？ 00:18:11 让 AI 既能读书破万卷，又能下笔如有神 今天介绍的五篇论文： [LG] Optimizers Qualitatively Alter Solutions And We Should Leverage This [Google DeepMind] https://arxiv.org/abs/2507.12224 --- [CL] TransEvalnia: Reasoning-based Evaluation and Ranking of Translations [Sakana.ai] https://arxiv.org/abs/2507.12724 --- [LG] Spectral Bellman Method: Unifying Representation and Exploration in RL [Technion & Georgia Institute of Technology] https://arxiv.org/abs/2507.13181 --- [CL] LLMs Encode Harmfulness and Refusal Separately [Northeastern University & Stanford University] https://arxiv.org/abs/2507.11878 --- [CL] Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs https://arxiv.org/abs/2507.09477

毕竟，人生不是一段需要严格执行的代码，而是一首允许即兴、允许跑调，却依然动人的歌。

00:01:36 你的勤奋，是在“拼宽度”还是在“拼深度”？ 00:06:00 AI 变聪明的秘密：不止是“喂饱”，更要“喂好” 00:11:19 AI的“学霸修炼手册”：如何跳出成长平台期？ 00:16:15 给AI一个“提示”，解锁更高阶的智慧 00:20:23 你怎么用“临时工”大脑，摆平全世界？ 00:05:30 喂AI，也是一门技术活 今天介绍的六篇论文： [LG] The Serial Scaling Hypothesis [UC Berkeley] https://arxiv.org/abs/2507.125 --- [LG] Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved) C Qin, J T Springenberg https://arxiv.org/abs/2507.12856 --- [LG] Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training M Liu, S Diao, J Hu, X Lu... [NVIDIA] https://arxiv.org/abs/2507.12507 --- [CL] QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation J Li, H Lu, K Wen, Z Yang... [Ant Research & Shanghai Qi Zhi Institute & Stanford University & Tsinghua University] https://arxiv.org/abs/2507.13266 --- [CL] Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models L Wong, K M. Collins, L Ying, C E. Zhang... [Stanford University & MIT] https://arxiv.org/abs/2507.12547 --- [CL] A Survey of Context Engineering for Large Language Models https://arxiv.org/abs/2507.13334

清晰，不是一种天赋，而是一种可以被刻意练习的纪律。

00:01:37 你的天气APP，正在经历一场认知革命 00:05:44 机器人学做家务，最快的老师原来是我们自己 00:09:50 AI的“偏科”秘籍：如何精准定制一个“特长生”？ 00:14:10 揭秘大脑“黑箱”：我们是如何学会说话的？ 00:20:16 AI“一目十行”的秘密 今天介绍的五篇论文： [LG] FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale [NVIDIA & Lawrence Berkeley National Laboratory] https://arxiv.org/abs/2507.12144 --- [RO] EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos [UC San Diego, UIUC & MIT] https://arxiv.org/abs/2507.12440 --- [CL] Language Models Improve When Pretraining Data Matches Target Tasks [Apple] https://arxiv.org/abs/2507.12466 --- [CL] Simulated Language Acquisition in a Biologically Realistic Model of the Brain [MIT & Columbia University] https://arxiv.org/abs/2507.11788 --- [CL] Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential [Apple] https://arxiv.org/abs/2507.11851

在一个数据和算法无处不在的时代，决定我们生活品质和事业高度的，恰恰是那些无法被量化的东西。

00:01:35 AI的“悄悄话”：我们还能“偷听”多久？ 00:06:10 AI：那个懂所有菜谱，却不会做饭的大厨？ 00:11:08 AI训练老大难：如何让机器“学徒”少走弯路？ 00:16:00 给AI动“开心手术”：我们如何让机器更懂“人情世故”？ 00:19:34 AI的下一个金矿，藏在一只虫子的大脑里？ 今天介绍的五篇论文： [LG] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety [UK AI Security Institute & Apollo Research] https://arxiv.org/abs/2507.11473 --- [LG] Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning [Amazon Web Service] https://arxiv.org/abs/2507.106 --- [LG] Relative Entropy Pathwise Policy Optimization [University of Toronto & Technische Universitat Wien & University of Pennsylvania] https://arxiv.org/abs/2507.11019 --- [CL] Internal Value Alignment in Large Language Models through Controlled Value Vector Activation [University of Science and Technology of China & Renmin University of China Beijing] https://arxiv.org/abs/2507.11316 --- [LG] Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures [Johns Hopkins University] https://arxiv.org/abs/2507.10951

00:01:44 AI偷懒的艺术：好钢如何用在刀刃上 00:05:46 组个“AI梦之队”，比单打独斗强在哪？ 00:10:51 AI玩“跑团”：下一个世界是如何被设计出来的？ 00:14:40 炼成“大模型”高手，需要计划表还是指南针？ 00:19:09 让机器人学会“试错”：不是瞎猜，而是高手过招 今天介绍的五篇论文： [CL] Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation [KAIST AI & Mila] https://arxiv.org/abs/2507.10524 --- [LG] Fusing LLM Capabilities with Routing Data [University of Illinois Urbana-Champaign] https://arxiv.org/abs/2507.10540 --- [AI] Multi-Actor Generative Artificial Intelligence as a Game Engine [Google DeepMind] https://arxiv.org/abs/2507.08892 --- [LG] Through the River: Understanding the Benefit of Schedule-Free Methods for Language Model Training [KAIST & Seoul National University & Microsoft Research] https://arxiv.org/abs/2507.09846 --- [LG] Behavioral Exploration: Learning to Explore via In-Context Adaptation [UC Berkeley] https://arxiv.org/abs/2507.09041

在过去，要找到一千个铁杆粉丝，你需要巨大的运气和成本。但在今天，AI就像一个超级精准的声呐，能帮你从七十亿人的海洋里，找到那些能与你同频共振的灵魂。

00:01:54 四两拨千斤：让小模型变聪明的“内存魔法” 00:07:11 AI调教指南：不说假话的秘密，竟然是“相信自己”？ 00:11:07 AI思考的“地图”与“导航” 00:15:16 AI大模型的“阿喀琉斯之踵”？ 00:18:49 AI写作高手进阶：人多，不如方法好 本期节目介绍的五篇论文： [CL] KV Cache Steering for Inducing Reasoning in Small Language Models [University of Amsterdam & University of Technology Nuremberg] https://arxiv.org/abs/2507.08799 --- [CL] The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality [University of Washington] https://arxiv.org/abs/2507.08371 --- [LG] CTRLS: Chain-of-Thought Reasoning via Latent State-Transition [UC San Diego] https://arxiv.org/abs/2507.081 --- [LG] One Token to Fool LLM-as-a-Judge [Tencent AI Lab] https://arxiv.org/abs/2507.08794 --- [LG] Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling [Stanford University] https://arxiv.org/abs/2507.08390

那个从不麻烦别人、看起来无坚不摧的人，并不是真的刀枪不入。他们只是过早地学会了把海啸般的痛苦，调成了静音模式。