arXiv — NLP / Computation & Language · · 3 min read

DocNavRAG: Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answering

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Computer Science > Computation and Language

arXiv:2608.01565 (cs)
[Submitted on 3 Aug 2026]

Title:DocNavRAG: Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answering

Authors:Dongyang Xie (1), Yao Tian (2), Hao Zhang (3), Yifei Yuan (4), Tieyun Qian (1), Ming Zhong (1), Jiawei Jiang (1), Yuanyuan Zhu (1) ((1) School of Computer Science, Wuhan University, (2) The Hong Kong University of Science and Technology, (3) The Chinese University of Hong Kong, (4) ETH Zurich)
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Abstract:Answering complex questions over large document collections requires assembling complementary evidence across sections and documents. GraphRAG offers structured retrieval but typically uses fixed traversal, while agentic RAG operates over weakly structured interfaces. Our key insight is that agents should navigate document structure within and across documents rather than repeatedly search from scratch. We introduce DocNavRAG, which organizes document hierarchies and cross-region relations into a navigable graph, exposes graph operations for locating, navigating, expanding, and fetching, and maintains an evolving evidence state to guide retrieval until sufficient evidence is collected. Across four long- and multi-document QA benchmarks, DocNavRAG improves answer quality and context sufficiency over the strongest baseline by 7.8\% and 17.7\% on average.
Comments: 19 pages, 5 figures, 16 tables
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.01565 [cs.CL]
  (or arXiv:2608.01565v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.01565
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Dongyang Xie [view email]
[v1] Mon, 3 Aug 2026 00:48:05 UTC (3,464 KB)
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