This paper presents mRAG, a multi-agent retrieval-augmented generation (RAG) framework composed of specialized agents for subtasks such as planning, searching, reasoning, and coordination. Our system uses a self-training paradigm with reward-guided trajectory sampling to optimize inter-agent collaboration and enhance response generation. Evaluated on DataMorgana-derived datasets during the SIGIR 2025 LiveRAG competition, mRAG outperforms conventional RAG baselines. We further analyze competition outcomes and showcase the framework's strengths with case studies, demonstrating its efficacy for complex, real-world RAG tasks.

翻译：本文提出了mRAG，一个由规划、搜索、推理和协调等子任务专用智能体组成的多智能体检索增强生成框架。我们的系统采用自训练范式，结合奖励引导的轨迹采样，以优化智能体间协作并提升响应生成质量。在SIGIR 2025 LiveRAG竞赛中，基于DataMorgana衍生的数据集进行评估，mRAG的表现超越了传统的RAG基线方法。我们进一步分析了竞赛结果，并通过案例研究展示了该框架的优势，证明了其在处理复杂现实世界RAG任务中的有效性。