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

Paper · 2025 · Thang Nguyen, Peter Chin and Yu-Wing Tai

Summary

Retrieval-augmented QA often fails when a question is vague or needs several reasoning hops. Typical RAG pipelines tune retrieval, augmentation and generation separately and seldom reason across the whole chain. The Dartmouth authors propose MA-RAG, a training-free framework that splits the pipeline among cooperating LLM agents. A Planner turns the question into a sequence of reasoning steps. For each step, a Step Definer writes a concrete subquery, a Retrieval Tool fetches top-ranked documents, an Extractor keeps only step-relevant content, and a QA Agent answers that step. The loop runs until the plan is finished. Agents are invoked only when needed, and each reasons via chain-of-thought, so intermediate decisions remain inspectable. On NQ, HotpotQA, 2WikimQA and TriviaQA, MA-RAG beats plain LLMs and earlier RAG systems at every model size tested. Its LLaMA3-70B and GPT-4o-mini versions set new best results on multi-hop sets. The work shows that modular orchestration, rather than fine-tuning, can make RAG more accurate and transparent.

Key points

  • The framework has four agent roles (Planner, Step Definer, Extractor, QA Agent) plus a Retrieval Tool. It needs no fine-tuning, and code is available at github.com/thangylvp/MA-RAG.
  • The tested backends were LLaMA3-8B, LLaMA3-70B and GPT-4o-mini. Even the 8B model with MA-RAG outperforms larger standalone LLMs. The provided extract omits the paper's exact benchmark scores.
  • Ablations show that removing the Planner or the Extractor hurts multi-hop performance. Model capacity matters most for the QA Agent, as well as for planning and extraction. Smaller models suffice for the retrieval-side components, which lowers deployment cost.
  • Without any domain-specific training, MA-RAG transfers to medical/biomedical QA and is competitive with domain-specialised models.
  • The paper is arXiv:2505.20096 (v2, October 2025), by Thang Nguyen, Peter Chin and Yu-Wing Tai.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Dartmouth CollegeHanover · United States37 works · 42,625 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 worksInformation Systems · 1,437 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,426 worksInformation retrieval & search · 473 works

Cite

Cite as Thang Nguyen, Peter Chin and Yu-Wing Tai. 2025. MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning. arXiv:2505.20096.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2505.20096 · arXiv 2505.20096

@misc{nguyen2025ma,
  title = {{MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning}},
  author = {Thang Nguyen, Peter Chin and Yu-Wing Tai},
  year = {2025},
  doi = {10.48550/arxiv.2505.20096},
  eprint = {2505.20096},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.20096}
}

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