AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination. LLM-powered agents typically require invoking LLM APIs and employing artificially designed complex prompts, which results in high inference latency. While this paradigm works well in scenarios with minimal interactive demands, such as code generation, it is unsuitable for highly interactive and real-time applications, such as gaming. Traditional gaming AI often employs small models or reactive policies, enabling fast inference but offering limited task completion and interaction abilities. In this work, we consider Overcooked as our testbed where players could communicate with natural language and cooperate to serve orders. We propose a Hierarchical Language Agent (HLA) for human-AI coordination that provides both strong reasoning abilities while keeping real-time execution. In particular, HLA adopts a hierarchical framework and comprises three modules: a proficient LLM, referred to as Slow Mind, for intention reasoning and language interaction, a lightweight LLM, referred to as Fast Mind, for generating macro actions, and a reactive policy, referred to as Executor, for transforming macro actions into atomic actions. Human studies show that HLA outperforms other baseline agents, including slow-mind-only agents and fast-mind-only agents, with stronger cooperation abilities, faster responses, and more consistent language communications.

翻译：基于大型语言模型（LLM）的AI智能体取得了显著进展，使其能够协助人类完成各种复杂任务，并引发了人机协作领域的革命。LLM驱动的智能体通常需要调用LLM应用程序接口并使用人工设计的复杂提示词，这导致推理延迟较高。虽然这种范式在代码生成等交互需求较低的场景中表现良好，但它不适用于游戏等高度交互和实时性强的应用。传统游戏AI通常采用小型模型或反应式策略，能够实现快速推理，但任务完成能力和交互能力有限。在本研究中，我们以Overcooked为测试平台，玩家可通过自然语言交流并协作完成订单。我们提出了一种用于人机协作的分层语言智能体（HLA），它在保证强推理能力的同时实现了实时执行。具体而言，HLA采用分层框架，包含三个模块：一个被称为慢思维的高性能LLM，用于意图推理和语言交互；一个被称为快思维的轻量级LLM，用于生成宏动作；以及一个被称为执行器的反应式策略，用于将宏动作转化为原子动作。人类研究表明，HLA在协作能力、响应速度和语言交互一致性方面均优于其他基线智能体，包括仅含慢思维的智能体和仅含快思维的智能体。