DMAP: Human-Aligned Structural Document Map for Multimodal Document Understanding

• URL: http://arxiv.org/abs/2601.18203v2
• Date: Tue, 27 Jan 2026 03:18:17 GMT
• Title: DMAP: Human-Aligned Structural Document Map for Multimodal Document Understanding
• Authors: ShunLiang Fu, Yanxin Zhang, Yixin Xiang, Xiaoyu Du, Jinhui Tang,
• Abstract summary: Document-level structural Document MAP encodes both hierarchical organization and inter-element relationships within multimodal documents.<n>Building upon this representation, a Reflective Reasoning Agent performs structure-aware and evidence-driven reasoning.<n>Experiments on MMDocQA benchmarks demonstrate that DMAP yields document-specific structural representations aligned with human interpretive patterns.
• Score: 30.54420648726099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing multimodal document question-answering (QA) systems predominantly rely on flat semantic retrieval, representing documents as a set of disconnected text chunks and largely neglecting their intrinsic hierarchical and relational structures. Such flattening disrupts logical and spatial dependencies - such as section organization, figure-text correspondence, and cross-reference relations, that humans naturally exploit for comprehension. To address this limitation, we introduce a document-level structural Document MAP (DMAP), which explicitly encodes both hierarchical organization and inter-element relationships within multimodal documents. Specifically, we design a Structured-Semantic Understanding Agent to construct DMAP by organizing textual content together with figures, tables, charts, etc. into a human-aligned hierarchical schema that captures both semantic and layout dependencies. Building upon this representation, a Reflective Reasoning Agent performs structure-aware and evidence-driven reasoning, dynamically assessing the sufficiency of retrieved context and iteratively refining answers through targeted interactions with DMAP. Extensive experiments on MMDocQA benchmarks demonstrate that DMAP yields document-specific structural representations aligned with human interpretive patterns, substantially enhancing retrieval precision, reasoning consistency, and multimodal comprehension over conventional RAG-based approaches. Code is available at https://github.com/Forlorin/DMAP

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