@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG
%A Hu, Xianming
%A Chen, Jingyang
%A Tang, Bin
%A Liu, Yihe
%A Huang, Yihong
%A Zhao, Hongbo
%A Chen, Nuoyi
%A Zhang, Jie
%A Li, Ping
%A Zhang, Kai
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hu-etal-2026-zoomrag
%X 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.
%U https://aclanthology.org/2026.findings-acl.1643/
%P 32840-32859
Markdown (Informal)
[ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG](https://aclanthology.org/2026.findings-acl.1643/) (Hu et al., Findings 2026)
ACL
- Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, and Kai Zhang. 2026. ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG. In Findings of the Association for Computational Linguistics: ACL 2026, pages 32840–32859, San Diego, California, United States. Association for Computational Linguistics.