DocNavRAG is introduced, 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.
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.
GraphRAG improves long-document question answering by introducing structured representations beyond conventional retrieval. However, automatically constructed graphs are inherently incomplete projections of source documents, and treating them as independent knowledge sources may lead to unreliable retrieval and generation. We propose PAGE-RAG, a projection-aware adaptive graph retrieval framework for reliable long-document question answering. PAGE-RAG views graph structures as semantic skeletons that organize and navigate document knowledge, rather than replacing the original knowledge source. Based on this perspective, PAGE-RAG introduces a task-adaptive retrieval routing strategy that dynamically selects appropriate retrieval behaviors according to query requirements. Furthermore, PAGE-RAG incorporates strict knowledge boundary control, ensuring that generated responses remain grounded within available evidence and abstaining from unsupported information beyond the accessible knowledge scope. Experiments demonstrate that PAGE-RAG achieves competitive answer quality while improving retrieval efficiency and knowledge reliability, highlighting the importance of projection-aware graph modeling, adaptive retrieval, and explicit knowledge boundary control for trustworthy GraphRAG systems. The source code is publicly available at https://github.com/CXY0112/PAGE-RAG.
Xingyu Chen, Junxiu An, Jun Guo et al.· 0 citations
This paper proposes DocTrace, a hierarchical framework that progressively performs evidence localization, structured document parsing, and evidence graph reasoning to enable explicit evidence provenance, and develops a two-stage training framework.
Lei Xiang, Zhicheng Guan, Hong Chen et al.· 0 citations
Graph-based and multimodal retrieval frameworks provide a strong foundation for long-document question answering, but single-pass retrieval can remain brittle when queries are ambiguous, multi-step, or misaligned with the indexed evidence. We present Agentic-RAG, a structure-aware retrieval-augmented generation framework that combines a MinerU-LightRAG-based document processing and graph retrieval pipeline with an LLM-based agentic query-control layer. The base pipeline supports structure-aware parsing and VLM-based captioning for textual, visual, and tabular evidence, while the agentic layer performs query planning, LLM-based evidence reranking, answer generation, groundedness checking, relevance checking, and query reformulation. The framework does not modify the underlying graph construction or indexing mechanism; instead, it improves retrieval control by guiding the base retriever toward evidence that better matches the user's information need. Experiments on HotpotQA and ASQA show that Agentic-RAG improves context precision on HotpotQA from 0.1682 to 0.2462 and substantially improves context precision and context recall on ASQA from 0.5045/0.2783 to 0.6522/0.4783. The results indicate that agentic query control is especially useful for ambiguity-heavy long-form reasoning, while sparse multi-hop evidence chaining remains a bottleneck for future work.
D. Lam, Gia Hien Tran, Tien-Dung Do· 2026 11th International Conf...· 0 citations
Multi-hop retrieval must recover passages that provide sufficient evidence together. An initial passage often resolves an entity or relation implicit in the question, making the missing evidence easier to describe only after retrieval begins. Graph retrieval improves access to related evidence through stored corpus structure, but its retrieval signal is commonly derived from the original question. Complementary evidence must then be reached through stored relations even when an observed passage provides a more direct semantic cue. We introduce EviReform, which separates revising the retrieval request from aggregating evidence in the graph. Retrieved source passages formulate residual queries for the unresolved information need. The original and residual retrieval signals are normalized separately, combined, and propagated between propositions that share entities. On 2WikiMultiHopQA, HotpotQA, and MuSiQue, EviReform exceeds the strongest baseline by up to 5.59 Recall@5 points and 4.50 F1 points. These results show that observed evidence can guide graph retrieval toward the part of a supporting chain left underspecified by the original question. Code is available at https://github.com/XrazyMee/EviReform.
DOCLAYOUT-MM-RAG provides a concrete basis for studying provenance-preserving retrieval-augmented generation over visually structured documents and shows how element-level provenance enables retrieval-to-generation and oracle-evidence analysis.
Andrew Brown, Christopher Baker, Karen Rafferty et al.· Proceedings of the 2026 ACM...· 0 citations
Graph-based RAG improves multi-hop question answering by organizing evidence as a knowledge graph. However, most existing RAG systems process each query in isolation and discard useful reasoning from the LLM's response after inference. As a result, later related queries need to retrieve evidence and reason from scratch. We propose LivingRAG, a Graph RAG framework with writable and reusable reasoning experience. LivingRAG adds a writable experience store to a graph-based retrieval backbone, enabling verified experiences to be reused during inference in two ways. Stored graph signals help retrieval find entities and passages that were useful in earlier related queries. Stored summaries provide a reference reasoning pattern for answer generation. We analyze online QA streams and find reusable signals from shared entities, graph neighborhoods, and question templates. Experiments on multi-hop QA benchmarks show that LivingRAG improves accuracy over strong RAG baselines and reduces completion-token use when relevant prior experience is reused.
