@inproceedings{gao-etal-2026-scaling,
    title = "Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding",
    author = "Gao, Sensen  and
      Zhao, Shanshan  and
      Jiang, Xu  and
      Duan, Lunhao  and
      Chng, Yong Xien  and
      Chen, Qing-Guo  and
      Luo, Weihua  and
      Zhang, Kaifu  and
      Bian, Jia-Wang  and
      Gong, Mingming",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.204/",
    pages = "4458--4489",
    ISBN = "979-8-89176-390-6",
    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, and applications, and highlight open challenges in efficiency, fine-grained representation, and robustness, providing a roadmap for future progress in document AI."
}