Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915945207824384 |
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| author | Gao, Sensen Zhao, Shanshan Jiang, Xu Duan, Lunhao Chng, Yong Xien Chen, Qing-Guo Luo, Weihua Zhang, Kaifu Bian, Jia-Wang Gong, Mingming |
| author_facet | Gao, Sensen Zhao, Shanshan Jiang, Xu Duan, Lunhao Chng, Yong Xien Chen, Qing-Guo Luo, Weihua Zhang, Kaifu Bian, Jia-Wang Gong, Mingming |
| contents | Document understanding is critical for applications from financial analysis to scientific discovery. Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs), face key limitations: the former loses structural detail, while the latter struggles with context modeling. Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, i.e., combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG. This approach enables holistic retrieval and reasoning across all modalities, unlocking comprehensive document intelligence. Recognizing its importance, this paper presents a systematic survey of Multimodal RAG for document understanding. We propose a taxonomy based on domain, retrieval modality, and granularity, and review advances involving graph structures and agentic frameworks. We also summarize key datasets, benchmarks, applications and industry deployment, and highlight open challenges in efficiency, fine-grained representation, and robustness, providing a roadmap for future progress in document AI. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_15253 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding Gao, Sensen Zhao, Shanshan Jiang, Xu Duan, Lunhao Chng, Yong Xien Chen, Qing-Guo Luo, Weihua Zhang, Kaifu Bian, Jia-Wang Gong, Mingming Computation and Language Computer Vision and Pattern Recognition Document understanding is critical for applications from financial analysis to scientific discovery. Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs), face key limitations: the former loses structural detail, while the latter struggles with context modeling. Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, i.e., combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG. This approach enables holistic retrieval and reasoning across all modalities, unlocking comprehensive document intelligence. Recognizing its importance, this paper presents a systematic survey of Multimodal RAG for document understanding. We propose a taxonomy based on domain, retrieval modality, and granularity, and review advances involving graph structures and agentic frameworks. We also summarize key datasets, benchmarks, applications and industry deployment, and highlight open challenges in efficiency, fine-grained representation, and robustness, providing a roadmap for future progress in document AI. |
| title | Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.15253 |