We address the challenge of multi-agent cooperation, where agents achieve a common goal by cooperating with decentralized agents under complex partial observations. Existing cooperative agent systems often struggle with efficiently processing continuously accumulating information, managing globally suboptimal planning due to lack of consideration of collaborators, and addressing false planning caused by environmental changes introduced by other collaborators. To overcome these challenges, we propose the RElevance, Proximity, and Validation-Enhanced Cooperative Language Agent (REVECA), a novel cognitive architecture powered by GPT-4o-mini. REVECA enables efficient memory management, optimal planning, and cost-effective prevention of false planning by leveraging Relevance Estimation, Adaptive Planning, and Trajectory-based Validation. Extensive experimental results demonstrate REVECA's superiority over existing methods across various benchmarks, while a user study reveals its potential for achieving trustworthy human-AI cooperation.

翻译：我们针对多智能体协作中的挑战展开研究，该场景要求智能体在复杂的局部观测条件下，通过与分散的智能体协作实现共同目标。现有的协作智能体系统常面临以下问题：难以高效处理持续积累的信息；因未充分考虑协作者而导致全局次优规划；以及因其他协作者引发的环境变化而产生错误规划。为克服这些挑战，我们提出基于相关性、邻近度与验证增强的协作语言智能体（REVECA），这是一种由GPT-4o-mini驱动的新型认知架构。REVECA通过相关性评估、自适应规划与轨迹验证三大机制，实现了高效记忆管理、最优规划和具有成本效益的错误规划预防。大量实验结果表明，REVECA在多种基准测试中均优于现有方法，用户研究则揭示了其实现可信人机协作的潜力。