The Era of Agentic Organization: Learning to Organize with Language Models

This paper introduces **Asynchronous Thinking (AsyncThink)**, a novel paradigm for large language model (LLM) reasoning designed to enable **agentic organization** and collaborative problem-solving. AsyncThink employs an **organizer-worker thinking protocol** where an LLM acts as an organizer that dynamically structures concurrent processes using **Fork and Join actions**, while workers execute sub-queries. The authors compare AsyncThink favorably to traditional sequential and parallel thinking approaches, demonstrating that it achieves **higher accuracy and reduced critical-path latency** across complex tasks like multi-solution countdown and mathematical reasoning. Training is accomplished through a two-stage process involving **cold-start format fine-tuning** followed by **reinforcement learning (RL)**, which optimizes the model for correctness, format compliance, and thinking concurrency. Furthermore, the results show that AsyncThink's capability for organizing thought processes **generalizes well** to previously unseen domains and problem types.