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<p>In recent years, Document Question Answering (DocQA) tasks have placed greater demands on long-context processing and complex reasoning. However, current mainstream multimodal Retrieval-Augmented Generation (RAG) models predominantly rely on page-level visual retrieval. When processing high-density heterogeneous documents, these models are susceptible to “feature dilution” and are constrained by the “logical fragmentation” caused by physical pagination, which limits the precision of fine-grained evidence localization. To alleviate the aforementioned issues of granularity mismatch and semantic disconnection, this paper proposes MMRDoc: a document reasoning framework based on structural awareness and multi-granularity synergistic retrieval. First, MMRDoc bridges cross-layout and cross-modal semantic fragmentation through Semantic Integrity Reconstruction (SIR). Subsequently, it introduces a Synergistic Dual-path Retrieval mechanism to retrieve the global visual information of Top-<i>K</i> macro-pages and the fine-grained semantic features of micro-elements in parallel. To address the issues of information redundancy and logical fragmentation caused by multi-granularity retrieval, the model dynamically aggregates the heterogeneous candidate sets and subjects them to a Structural-aware Reranking module for unified evaluation. This achieves a logical transition from coarse-grained recall to fine-grained filtering. Experiments demonstrate that MMRDoc attains state-of-the-art (SOTA) performance on three major benchmarks: MMLongBench-Doc, PaperTab, and FetaTab, while maintaining strong robustness on the extreme-length LongDocURL benchmark. Compared to existing visual RAG baselines and multi-agent systems, MMRDoc enhances cross-page table understanding and long-range reasoning capabilities, providing an effective structured synergistic approach for multimodal long-document understanding.</p>

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MMRDoc: A multi-granularity multimodal RAG framework for long-document VQA

  • 期刊论文

  • 发表日期：

    2026年7月15日

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  • Shengxu Xu,
  • Hongsong Wang

摘要

In recent years, Document Question Answering (DocQA) tasks have placed greater demands on long-context processing and complex reasoning. However, current mainstream multimodal Retrieval-Augmented Generation (RAG) models predominantly rely on page-level visual retrieval. When processing high-density heterogeneous documents, these models are susceptible to “feature dilution” and are constrained by the “logical fragmentation” caused by physical pagination, which limits the precision of fine-grained evidence localization. To alleviate the aforementioned issues of granularity mismatch and semantic disconnection, this paper proposes MMRDoc: a document reasoning framework based on structural awareness and multi-granularity synergistic retrieval. First, MMRDoc bridges cross-layout and cross-modal semantic fragmentation through Semantic Integrity Reconstruction (SIR). Subsequently, it introduces a Synergistic Dual-path Retrieval mechanism to retrieve the global visual information of Top-K macro-pages and the fine-grained semantic features of micro-elements in parallel. To address the issues of information redundancy and logical fragmentation caused by multi-granularity retrieval, the model dynamically aggregates the heterogeneous candidate sets and subjects them to a Structural-aware Reranking module for unified evaluation. This achieves a logical transition from coarse-grained recall to fine-grained filtering. Experiments demonstrate that MMRDoc attains state-of-the-art (SOTA) performance on three major benchmarks: MMLongBench-Doc, PaperTab, and FetaTab, while maintaining strong robustness on the extreme-length LongDocURL benchmark. Compared to existing visual RAG baselines and multi-agent systems, MMRDoc enhances cross-page table understanding and long-range reasoning capabilities, providing an effective structured synergistic approach for multimodal long-document understanding.