The Era of Agentic Organization: Learning to Organize with Language
  Models
We envision a new era of AI, termed agentic organization, where agents solve
complex problems by working collaboratively and concurrently, enabling outcomes
beyond individual intelligence. To realize this vision, we introduce
asynchronous thinking (AsyncThink) as a new paradigm of reasoning with large
language models, which organizes the internal thinking process into
concurrently executable structures. Specifically, we propose a thinking
protocol where an organizer dynamically assigns sub-queries to workers, merges
intermediate knowledge, and produces coherent solutions. More importantly, the
thinking structure in this protocol can be further optimized through
reinforcement learning. Experiments demonstrate that AsyncThink achieves 28%
lower inference latency compared to parallel thinking while improving accuracy
on mathematical reasoning. Moreover, AsyncThink generalizes its learned
asynchronous thinking capabilities, effectively tackling unseen tasks without
additional training.
6904402f2c556835fa67f11d
Zewen Chi
claimed_verified
2025-10-31T14:26:40.525Z
60f6d61f89b21b8fd2d471c6
https://cdn-avatars.huggingface.co/v1/production/uploads/60f6d61f89b21b8fd2d471c6/RmLFf97vUoXMoCT3rWbhm.jpeg
Zewen Chi
CZWin32768
user
CZWin32768
6904402f2c556835fa67f11e
Li Dong
claimed_verified
2025-10-31T14:26:37.989Z
5df85abada6d0311fd3d5408
/avatars/2331cf703c1b5d3a62e2050b1a6eb108.svg
Li Dong
unilm
user
unilm
6904402f2c556835fa67f11f
Qingxiu Dong
6904402f2c556835fa67f120
Yaru Hao
6904402f2c556835fa67f121
Xun Wu
6904402f2c556835fa67f122
Shaohan Huang
6904402f2c556835fa67f123
Furu Wei
The Era of Agentic Organization: Learning to Organize with Language
  Models
We envision a new era of AI, termed agentic organization, where agents solve
complex problems by working collaboratively and concurrently, enabling outcomes
beyond individual intelligence. To realize this vision, we introduce
asynchronous thinking (AsyncThink) as a new paradigm of reasoning with large
language models, which organizes the internal thinking process into
concurrently executable structures. Specifically, we propose a thinking
protocol where an organizer dynamically assigns sub-queries to workers, merges
intermediate knowledge, and produces coherent solutions. More importantly, the
thinking structure in this protocol can be further optimized through
reinforcement learning. Experiments demonstrate that AsyncThink achieves 28%
lower inference latency compared to parallel thinking while improving accuracy
on mathematical reasoning. Moreover, AsyncThink generalizes its learned
asynchronous thinking capabilities, effectively tackling unseen tasks without
additional training.
AsyncThink, a new reasoning paradigm for large language models, improves inference efficiency and accuracy through concurrent and optimized sub-query processing.
asynchronous thinking AsyncThink large language models thinking protocol organizer workers reinforcement learning inference latency mathematical reasoning