Real-Time Reasoning Agents in Evolving Environments
Agents in the real world must make not only logical but also timely
judgments. This requires continuous awareness of the dynamic environment:
hazards emerge, opportunities arise, and other agents act, while the agent's
reasoning is still unfolding. Despite advances in language model reasoning,
existing approaches fail to account for this dynamic nature. We introduce
real-time reasoning as a new problem formulation for agents in evolving
environments and build Real-Time Reasoning Gym to demonstrate it. We study two
paradigms for deploying language models in agents: (1) reactive agents, which
employ language models with bounded reasoning computation for rapid responses,
and (2) planning agents, which allow extended reasoning computation for complex
problems. Our experiments show that even state-of-the-art models struggle with
making logical and timely judgments in either paradigm. To address this
limitation, we propose AgileThinker, which simultaneously engages both
reasoning paradigms. AgileThinker consistently outperforms agents engaging only
one reasoning paradigm as the task difficulty and time pressure rise,
effectively balancing reasoning depth and response latency. Our work
establishes real-time reasoning as a critical testbed for developing practical
agents and provides a foundation for research in temporally constrained AI
systems, highlighting a path toward real-time capable agents.
691261e9a644ba07c499c69d
Yule Wen
691261e9a644ba07c499c69e
Yixin Ye
691261e9a644ba07c499c69f
Yanzhe Zhang
claimed_verified
2025-12-08T08:36:16.125Z
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/avatars/28d3ba4b0fa00acb1eed623ab3620789.svg
Yanzhe Zhang
zyanzhe
user
zyanzhe
691261e9a644ba07c499c6a0
Diyi Yang
691261e9a644ba07c499c6a1
Hao Zhu
Real-Time Reasoning Agents in Evolving Environments
Agents in the real world must make not only logical but also timely
judgments. This requires continuous awareness of the dynamic environment:
hazards emerge, opportunities arise, and other agents act, while the agent's
reasoning is still unfolding. Despite advances in language model reasoning,
existing approaches fail to account for this dynamic nature. We introduce
real-time reasoning as a new problem formulation for agents in evolving
environments and build Real-Time Reasoning Gym to demonstrate it. We study two
paradigms for deploying language models in agents: (1) reactive agents, which
employ language models with bounded reasoning computation for rapid responses,
and (2) planning agents, which allow extended reasoning computation for complex
problems. Our experiments show that even state-of-the-art models struggle with
making logical and timely judgments in either paradigm. To address this
limitation, we propose AgileThinker, which simultaneously engages both
reasoning paradigms. AgileThinker consistently outperforms agents engaging only
one reasoning paradigm as the task difficulty and time pressure rise,
effectively balancing reasoning depth and response latency. Our work
establishes real-time reasoning as a critical testbed for developing practical
agents and provides a foundation for research in temporally constrained AI
systems, highlighting a path toward real-time capable agents.
Real-Time Reasoning Gym demonstrates the challenges of deploying language models in dynamic environments, introducing AgileThinker to balance reasoning depth and response latency.
real-time reasoning Reactive agents Planning agents AgileThinker temporally constrained AI systems