Real-Time Reasoning Agents in Evolving Environments
Yule Wen, Yixin Ye, Yanzhe Zhang, Diyi Yang, Hao Zhu
2025-11-10
Summary
This paper explores how to make AI agents, powered by large language models, react and think effectively in situations that change quickly, like the real world.
What's the problem?
Current AI agents, even those using advanced language models, struggle to make good decisions *and* make them quickly. They either take too long to think through a problem, or they react without enough careful consideration, especially when things are constantly changing around them. The issue is that existing AI isn't designed to balance thoughtful reasoning with the need for speed in dynamic environments.
What's the solution?
The researchers created a testing environment called 'Real-Time Reasoning Gym' to study this problem. They then developed a new approach called 'AgileThinker'. AgileThinker doesn't just rely on one way of thinking; it combines two: quick, reactive responses for immediate issues and more in-depth planning for complex problems. It switches between these two modes depending on how urgent the situation is and how difficult the problem is, allowing it to be both fast and smart.
Why it matters?
This work is important because it highlights a major limitation of current AI – its inability to handle real-time situations effectively. By introducing 'real-time reasoning' as a specific challenge and proposing AgileThinker, the researchers are paving the way for AI agents that can truly operate in the real world, where quick and logical decisions are crucial. It sets the stage for building AI systems that aren't just intelligent, but also responsive and adaptable.
Abstract
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.