@inproceedings{hu-etal-2026-zoomrag,
    title = "{Z}oom{RAG}: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate {RAG}",
    author = "Hu, Xianming  and
      Chen, Jingyang  and
      Tang, Bin  and
      Liu, Yihe  and
      Huang, Yihong  and
      Zhao, Hongbo  and
      Chen, Nuoyi  and
      Zhang, Jie  and
      Li, Ping  and
      Zhang, Kai",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1643/",
    pages = "32840--32859",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrieval-Augmented Generation is a powerful tool for NLP applications. Yet, it is challenging to encode large knowledge bases as compact offline structures while simultaneously achieving accurate, low-latency online retrieval. We propose **ZoomRAG**, a coarse-to-fine, hierarchical graph inference method to tackle the challenges. ZoomRAG formulates the retrieval task as random walks across multi-scale relational graphs. *At the coarse level*, it constructs a global relational graph and performs a query-initiated random walk to quickly locate a few relevant documents over the entire corpus. *At the finer level*, it ``zooms into'' the selected documents to capture fine-grained semantic and temporal relations, and conducts a second random walk to pinpoint salient evidence chunks for generation. This coarse-to-fine strategy substantially reduces offline indexing costs and accelerates online retrieval. Moreover, random-walk based topological reasoning over rich, multi-scale relational structures enables ZoomRAG to effectively aggregate multi-hop evidence while suppressing noise. Finally, we address the difficulty of handling concurrent RAG queries by **algorithm-parallel ZoomRAG**. Overall, ZoomRAG avoids building expensive knowledge graphs while achieving 2.2{\%} {--} 4.9{\%} absolute gains in accuracy over SOTA RAG models, with an average online retrieval latency per-query as low as 0.019 secs by processing hundreds of queries concurrently."
}