Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding
Abstract
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.
Cite
@article{arxiv.2510.15253,
title = {Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding},
author = {Sensen Gao and Shanshan Zhao and Xu Jiang and Lunhao Duan and Yong Xien Chng and Qing-Guo Chen and Weihua Luo and Kaifu Zhang and Jia-Wang Bian and Mingming Gong},
journal= {arXiv preprint arXiv:2510.15253},
year = {2026}
}
Comments
Accepted by ACL2026 Main Conference; Project is available at https://github.com/SensenGao/Multimodal-RAG-Survey-For-Document