The Era of Agentic Organization: Learning to Organize with Language
  Models
Authors: name: Zewen Chi, name: Li Dong, name: Qingxiu Dong, name: Yaru Hao, name: Xun Wu, name: Shaohan Huang, name: Furu Wei

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.