MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2505.20096v1
• Date: Mon, 26 May 2025 15:05:18 GMT
• Title: MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning
• Authors: Thang Nguyen, Peter Chin, Yu-Wing Tai,
• Abstract summary: MA-RAG orchestrates a collaborative set of specialized AI agents to tackle each stage of the RAG pipeline with task-aware reasoning.<n>Our design allows fine-grained control over information flow without any model fine-tuning.<n>This modular and reasoning-driven architecture enables MA-RAG to deliver robust, interpretable results.
• Score: 43.66966457772646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present MA-RAG, a Multi-Agent framework for Retrieval-Augmented Generation (RAG) that addresses the inherent ambiguities and reasoning challenges in complex information-seeking tasks. Unlike conventional RAG methods that rely on either end-to-end fine-tuning or isolated component enhancements, MA-RAG orchestrates a collaborative set of specialized AI agents: Planner, Step Definer, Extractor, and QA Agents, to tackle each stage of the RAG pipeline with task-aware reasoning. Ambiguities may arise from underspecified queries, sparse or indirect evidence in retrieved documents, or the need to integrate information scattered across multiple sources. MA-RAG mitigates these challenges by decomposing the problem into subtasks, such as query disambiguation, evidence extraction, and answer synthesis, and dispatching them to dedicated agents equipped with chain-of-thought prompting. These agents communicate intermediate reasoning and progressively refine the retrieval and synthesis process. Our design allows fine-grained control over information flow without any model fine-tuning. Crucially, agents are invoked on demand, enabling a dynamic and efficient workflow that avoids unnecessary computation. This modular and reasoning-driven architecture enables MA-RAG to deliver robust, interpretable results. Experiments on multi-hop and ambiguous QA benchmarks demonstrate that MA-RAG outperforms state-of-the-art training-free baselines and rivals fine-tuned systems, validating the effectiveness of collaborative agent-based reasoning in RAG.

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