ArXiv

DualG-MRAG: Decoupling Macro-Reasoning and Micro-Matching for Multimodal Retrieval-Augmented Generation

2026-07-30 · 17:40 UTC ·Jiacheng Tao, Qingyun Sun, Haonan Yuan... ·1 min read
Authors
Jiacheng Tao, Qingyun Sun, Haonan Yuan...
Categories
cs.AI
arXiv
https://arxiv.org/abs/2607.28580v1
PDF
https://arxiv.org/pdf/2607.28580v1

Brief

DualG-MRAG targets multimodal RAG multi-hop reasoning failures by decoupling macro structural routing and micro evidence matching through separate Macro and Micro graphs. It uses a GNN Retriever with query-driven message passing and a dynamic-programming decoder that emits explicit reasoning paths for generation. Experiments report improved evidence recall and complex QA accuracy over baselines.

Why it matters

DualG-MRAG introduces a dual-tier architecture with a Macro Graph for global topological routing and a Micro Graph for fine-grained local verification to decouple structural reasoning from evidence matching and reduce retrieval noise.

Key details

  • Retrieval is formulated as query-driven message passing via a GNN Retriever, and a dynamic-programming decoding mechanism extracts explicit reasoning paths from the GNN forward pass to guide the generator instead of isolated document chunks.
  • Extensive experiments (paper on arXiv 2026-07-30; accepted to ACM MM 2026) show DualG-MRAG outperforms baselines on evidence recall and complex multimodal QA accuracy.
Source evidence

Abstract

While Multimodal Retrieval-Augmented Generation (MM-RAG) has shown promising results, it still struggles with complex multi-hop reasoning tasks. Existing methods primarily focus on independent instance-level matching, which often fails to capture explicit relationships across modalities and documents. Although Graph-enhanced methods introduce structural modeling, they face a fundamental challenge in multimodal scenarios: incorporating fine-grained visual features leads to rapid graph expansion and retrieval noise, whereas coarse-grained representations cause the discarding of critical local evidence. To address this dilemma, we propose DualG-MRAG, a Dual-tier framework that introduces a decoupled architecture comprising Macro-reasoning and Micro-matching Graphs for Multimodal RAG. Specifically, to suppress retrieval noise by isolating global structural reasoning from fine-grained evidence matching, we construct a Macro Graph for global topological routing and a Micro Graph for precise local verification. Subsequently, to enable dynamic relevance propagation across heterogeneous evidence sources, we formulate retrieval as a query-driven message passing process via a GNN Retriever. Furthermore, to provide the generative model with coherent structural guidance, we introduce a dynamic programming decoding mechanism that extracts explicit reasoning paths directly from the GNN's forward pass, replacing the standard input of isolated document chunks. Extensive experiments demonstrate that DualG-MRAG outperforms baselines in both evidence recall and complex QA accuracy.

Comment: Accepted to the 34th ACM International Conference on Multimedia (ACM MM 2026). 12 pages