Multimodal Graph RAG for Long-range Visually Rich Document Understanding

• URL: http://arxiv.org/abs/2606.28780v1
• Date: Sat, 27 Jun 2026 07:14:31 GMT
• Title: Multimodal Graph RAG for Long-range Visually Rich Document Understanding
• Abstract summary: Multimodal large language models (MLLMs) are widely applied to visual document understanding.<n>MMRAG can address this challenge by retrieving relevant pages.<n>It still struggles with the visual question answering (VQA) requiring holistic comprehension of a document.
• Score: 20.85691320079392
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal large language models (MLLMs) are widely applied to visual document understanding. However, comprehending long documents remains an issue by the limited context window. Though recent multimodal retrieval-augmented generation (MMRAG) can address this challenge by retrieving relevant pages. It still struggles with the visual question answering (VQA) requiring holistic comprehension of a document. To cope with this, knowledge graph (KG) that summarizes global knowledge of a document can provide an effective solution. However, most existing LLM-based KG construction methods handle only the language modality, leaving the automatic creation of multimodal KGs (MMKGs) for visually rich documents largely unexplored. In this paper, we introduce a multimodal graph-based RAG approach to tackle this problem. Existing LLM-based KG methods evaluate the QA performance relying on indirect evidence such as comprehensiveness, diversity, empowerment, and so on. The lack of annotated datasets for comprehensive document-level VQA poses a significant challenge to effective model evaluation. To overcome this limitation, we also introduce a new benchmark, DLVQA (document-level VQA), which provides reference summaries and corresponding supporting facts for global document-level questions. Experimental results show that our approach outperforms existing MMRAG or KG-based approaches on multi-hop QA/VQA benchmarks and DLVQA.

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