@inproceedings{sourati-etal-2026-lad,
    title = "{LAD}-{RAG}: Layout-aware Dynamic {RAG} for Visually-Rich Document Understanding",
    author = "Sourati, Zhivar  and
      Wang, Zheng  and
      Liu, Marianne Menglin  and
      Hu, Yazhe  and
      Guo, Mengqing  and
      Bharadwaj, Sujeeth  and
      Han, Kyu J.  and
      Sheng, Tao  and
      Ravi, Sujith  and
      Dehghani, Morteza  and
      Roth, Dan",
    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.724/",
    pages = "15945--15968",
    ISBN = "979-8-89176-390-6",
    abstract = "Question answering over visually rich documents (VRDs) requires reasoning not only over isolated content but also over documents' structural organization and cross-page dependencies. However, conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retrieve a fixed number of pages at inference, regardless of the specific demands of the question or context. This often results in incomplete evidence retrieval and degraded answer quality for multi-page reasoning tasks. To address these limitations, we propose LAD-RAG, a novel Layout-Aware Dynamic RAG framework. During ingestion, LAD-RAG constructs a symbolic document graph that captures layout structure and cross-page dependencies, adding it alongside standard neural embeddings to yield a more holistic representation of the document. During inference, an LLM agent dynamically interacts with the neural and symbolic indices to adaptively retrieve the necessary evidence based on the query. Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DocVQA demonstrate that LAD-RAG improves retrieval, achieving over 90{\%} perfect recall on average without any top-$k$ tuning, and outperforming baseline retrievers by up to 20{\%} in recall at comparable noise levels, yielding higher QA accuracy with minimal latency."
}