CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training

• URL: http://arxiv.org/abs/2506.10844v1
• Date: Thu, 12 Jun 2025 16:02:29 GMT
• Title: CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training
• Authors: Alireza Salemi, Mukta Maddipatla, Hamed Zamani,
• Abstract summary: mRAG is a multi-agent retrieval-augmented generation framework composed of specialized agents for subtasks such as planning, searching, reasoning, and coordination.<n> Evaluated on DataMorgana-derived datasets during the SIGIR 2025 LiveRAG competition, mRAG outperforms conventional RAG baselines.
• Score: 18.787703082459046
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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