AI可可AI生活

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

在小宇宙查看该单集文稿

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

在小宇宙查看该单集文稿

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

在小宇宙查看该单集文稿

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

在小宇宙查看该单集文稿

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

在小宇宙查看该单集文稿