@inproceedings{wang-2026-mdocrag,
    title = "{MD}oc{RAG}-{RL}: Empowering Multi-Modal Document {RAG} via Complex Visual Reasoning with Reinforcement Learning",
    author = "Wang, Zhongyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.420/",
    pages = "8641--8651",
    ISBN = "979-8-89176-395-1",
    abstract = "While Retrieval-Augmented Generation(RAG) enhances multi-modal large language models(MLLMs) by introducing external knowledge, existing RAG systems still face significant limitations when dealing with complex visual reasoning. On one hand, MLLMs, being generative models, produce suboptimal embeddings for retrieval tasks. On the other hand, existing methods naively insert images into context without adequate visual perception, thereby limiting reasoning capabilities. To address these challenges, we propose MDocRAG-RL, a novel RAG framework for complex visual reasoning. We design specialized pre-training and fine-tuning tasks to enable MLLMs to compress visual document representations and align textual and visual embeddings for improved retrieval efficiency. Additionally, we design a visual perception action space for the generator that allows progressive coarse-to-fine information acquisition from visually-rich documents. Furthermore, we develop a reinforcement learning framework to enhance the complex visual reasoning capability of the RAG system. Extensive experiments on multiple challenging benchmarks demonstrate the significant effectiveness of our approach, achieving state-of-the-art performance across various benchmarks."
}