
11 月 10 日 · 26 分钟
![[人人能懂] 从“一根筋”到“老戏骨”,AI的进化超乎你想象](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)
你的AI有多聪明?关键看它会不会“彩排”
AI内卷时代,如何找到“天选之子”?
AI的“自我修养”:如何让机器自己教自己?
给AI装个方向盘,指哪打哪
AI巨头里的“扫地僧”:没有他,AI秒变“人工智障”
本期介绍的五篇论文:
[LG] SimuRA: Towards General Goal-Oriented Agent via Simulative Reasoning Architecture with LLM-Based World Model
[Mohamed bin Zayed University of Artificial Intelligence & Samsung Research]
https://arxiv.org/abs/2507.237
---
[LG] Consensus-Driven Active Model Selection
[MIT & UMass Amherst]
https://arxiv.org/abs/2507.23771
---
[CL] CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks
[FAIR at Meta & NYU]
https://arxiv.org/abs/2507.237
---
[CL] Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders
[Columbia University]
https://arxiv.org/abs/2507.23220
---
[CL] Unveiling Super Experts in Mixture-of-Experts Large Language Models
[Meituan & Tsinghua University]
https://arxiv.org/abs/2507.23279
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] 从“一根筋”到“老戏骨”,AI的进化超乎你想象](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 2 日 · 22 分钟
你的AI有多聪明?关键看它会不会“彩排”
AI内卷时代,如何找到“天选之子”?
AI的“自我修养”:如何让机器自己教自己?
给AI装个方向盘,指哪打哪
AI巨头里的“扫地僧”:没有他,AI秒变“人工智障”
本期介绍的五篇论文:
[LG] SimuRA: Towards General Goal-Oriented Agent via Simulative Reasoning Architecture with LLM-Based World Model
[Mohamed bin Zayed University of Artificial Intelligence & Samsung Research]
https://arxiv.org/abs/2507.237
---
[LG] Consensus-Driven Active Model Selection
[MIT & UMass Amherst]
https://arxiv.org/abs/2507.23771
---
[CL] CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks
[FAIR at Meta & NYU]
https://arxiv.org/abs/2507.237
---
[CL] Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders
[Columbia University]
https://arxiv.org/abs/2507.23220
---
[CL] Unveiling Super Experts in Mixture-of-Experts Large Language Models
[Meituan & Tsinghua University]
https://arxiv.org/abs/2507.23279
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