Aug 2026· Proceedings of the 2026 ACM Symposium on Document Engineering· 0 citations· 38 references
TL;DR
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.
Abstract
Question answering over visually structured documents remains difficult when evidence is distributed across prose, tables, figures, captions, visual layout, and document structure. We present DOCLAYOUT-MM-RAG, a layout-aware annotation framework for grounded question answering over documents. The framework links each question-answer instance to supporting layout elements, preserving element-level provenance for annotation, retrieval, citation, generation, and evaluation. We instantiate the framework on annual reports and release an initial curated corpus of 650 accepted grounded question-answer instances across 30 documents. The corpus captures evidential complexity, with 36.6% of instances requiring cross-page support and 40.8% requiring multimodal support. Exploratory analyses compare flat-text, structure-aware, and layout-derived multimodal retrieval representations, and show how element-level provenance enables retrieval-to-generation and oracle-evidence analysis. DOCLAYOUT-MM-RAG provides a concrete basis for studying provenance-preserving retrieval-augmented generation over visually structured documents.
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 work presents DocMaster, a hierarchical structure-aware document analysis system that parses documents into hierarchical document trees preserving original layouts and constructs a structure-aware semantic index that enables accurate document filtering and in-depth analysis.
Ziqi Chen, Yingli Zhou, Fangyuan Zhang et al.· 0 citations
Retrieval-augmented generation (RAG) systems commonly operate on text extracted from documents, potentially losing information contained in figures, tables, layout, and relationships distributed across passages. We present an explainable multimodal graph-RAG architecture that augments a text-only baseline with LLM-extracted subject--relation--object triples and CLIP-based retrieval of figures and tables. The three evidence sources are retrieved independently and fused only at generation time, allowing the effects of graph evidence, visual evidence, and generator choice to be evaluated separately. We conduct a controlled four-way ablation on 1,000 PubLayNet pages across single-passage, multi-hop, and figure questions using two closed-weight and two open-weight multimodal generators. We also compare matched caption-answerable and pixel-only figure question sets to distinguish caption recovery from genuine visual question answering. Knowledge-graph augmentation provides no reliable accuracy improvement across generators or question types in this corpus. On pixel-only questions, text-only systems achieve zero accuracy, while multimodal systems reach 0.057--0.114 and are constrained by both image retrieval (Recall@3 = 0.371) and the generators'ability to interpret dense scientific figures and tables. Caption-derived questions substantially overestimate text-only visual question-answering capability. Processing the same image also produces an elevenfold variation in input tokens across generators, showing that image tokenisation can dominate deployment cost. Source code, experiment configurations, and evaluation resources are publicly available at https://github.com/sokistar24/multimodal-graph-rag. These findings show that the value of multimodal and graph augmentation depends on retrieval design, corpus structure, benchmark construction, and generator efficiency.
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.
Long-document question-answering experiments show that human-verified TOC hierarchies and contextual relationships improve reasoning, with their combination providing complementary benefits.
Yuefeng Zou, Yichen Lu, Jingxiao Yang et al.· 0 citations
Results align with a diagnostic perspective on chunking: using evidence at a task-appropriate level of granularity can improve grounding, auditability, and answer quality, but the observed patterns should be interpreted within the HotpotQA distractor setting, fixed generator, and tested context budgets.