@inproceedings{shen-etal-2025-gear,
    title = "{G}e{AR}: Graph-enhanced Agent for Retrieval-augmented Generation",
    author = "Shen, Zhili  and
      Diao, Chenxin  and
      Vougiouklis, Pavlos  and
      Merita, Pascual  and
      Piramanayagam, Shriram  and
      Chen, Enting  and
      Graux, Damien  and
      Melo, Andre  and
      Lai, Ruofei  and
      Jiang, Zeren  and
      Li, Zhongyang  and
      Qi, Ye  and
      Ren, Yang  and
      Tu, Dandan  and
      Pan, Jeff Z.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.624/",
    pages = "12049--12072",
    ISBN = "979-8-89176-256-5",
    abstract = "Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce $\text{G\small{E}\normalsize{AR}}$, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates $\text{G\small{E}\normalsize{AR}}${`}s superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10{\%} on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io."
}