动态环境中的实时推理智能体

摘要

现实世界中的智能体不仅需要做出合乎逻辑的判断，更需具备时效性决策能力。这要求智能体持续感知动态环境：在推理过程尚未完成时，危险可能突然出现，机遇可能转瞬即逝，其他智能体也在同时行动。尽管语言模型推理技术已取得长足进步，现有方法仍未能充分考虑这种动态特性。我们提出"实时推理"作为动态环境中智能体的新问题框架，并构建实时推理竞技场进行验证。我们研究了语言模型在智能体中的两种部署范式：（1）反应式智能体，采用计算资源受限的语言模型实现快速响应；（2）规划式智能体，允许扩展推理计算以解决复杂问题。实验表明，即使最先进的模型在这两种范式下都难以同时实现逻辑正确性与时效性。为此，我们提出AgileThinker框架，通过协同运用两种推理范式，在任务难度和时间压力增加时持续超越单一推理范式的智能体，有效平衡推理深度与响应延迟。本研究将实时推理确立为开发实用智能体的关键测试平台，为时间约束型AI系统研究奠定基础，指明了实现实时能力智能体的发展路径。

English

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.

动态环境中的实时推理智能体

Real-Time Reasoning Agents in Evolving Environments

摘要

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