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About this paper
Yule Wen, Yixin Ye, Yanzhe Zhang, Diyi Yang, Hao Zhu
This research formalizes the problem of real-time reasoning for Large Language Model agents in dynamic environments and introduces the Real-Time Reasoning Gym, a standardized evaluation platform. The proposed AgileThinker architecture, leveraging parallel planning and reactive threads, significantly surpasses single-paradigm agents in performance by making timely, informed decisions based on streaming reasoning traces.
Key insights
- Purely reactive agents lack foresight and decision quality under high cognitive load, while pure planning agents fail to adapt to rapidly changing environments due to outdated information.
- A dual-thread architecture, inspired by human dual-process theory, is essential for robust performance, enabling agents to blend deep strategic planning with prompt, situationally aware reactions.
- The ability of a fast, reactive thread to leverage partial and streaming reasoning traces from a slower, planning thread is critical for making informed real-time decisions without waiting for full plan completion.
Summary by alphaXiv.
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