
11 月 10 日 · 26 分钟
![[人人能懂] AI思考的五种新姿势:它如何学会了“死磕”、“变通”与“偷懒”?](https://media-one.wavpub.com/covers/1b225f87-4eea-40e2-9522-fb4563ec9b2c/d48cb32e6dd1d52c.png?x-oss-process=image/resize,m_lfit,w_520,h_520/format,webp)
AI侦探养成记:如何让机器学会“死磕到底”?
AI也需要“元学习”:如何打造一把能开万能锁的钥匙?
拆解AI大脑:它如何学会“绕个弯”解决问题?
AI学会“举一反三”的秘密:两层楼就够了?
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
说明里标出的时间点,点一下跳过去

11 月 10 日 · 26 分钟

[人人能懂AI前沿] 3D持续打磨、多智能体博弈与微米级控制
11 月 9 日 · 24 分钟

[人人能懂AI前沿] 从元技能提炼、对抗性推理到物理世界的“柔性破界”
11 月 8 日 · 23 分钟

[人人能懂AI前沿] 从分配脑容量、无反馈破局到预测未来的代价
11 月 7 日 · 24 分钟
![[人人能懂] AI思考的五种新姿势:它如何学会了“死磕”、“变通”与“偷懒”?](https://media-one.wavpub.com/covers/1b225f87-4eea-40e2-9522-fb4563ec9b2c/d48cb32e6dd1d52c.png?x-oss-process=image/resize,m_lfit,w_400,h_400/format,webp)
AI可可AI生活 · 2025 年 8 月 13 日 · 21 分钟
AI侦探养成记:如何让机器学会“死磕到底”?
AI也需要“元学习”:如何打造一把能开万能锁的钥匙?
拆解AI大脑:它如何学会“绕个弯”解决问题?
AI学会“举一反三”的秘密:两层楼就够了?
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
说明里标出的时间点,点一下跳过去

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11 月 10 日 · 26 分钟

[人人能懂AI前沿] 3D持续打磨、多智能体博弈与微米级控制
11 月 9 日 · 24 分钟

[人人能懂AI前沿] 从元技能提炼、对抗性推理到物理世界的“柔性破界”
11 月 8 日 · 23 分钟