发表机构
Beihang University; SKLCCSE（北京航空航天大学; SKLCCSE）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多模态RAG在复杂多跳推理中存在的问题，本文提出DualG-MRAG框架，通过解耦宏观推理与微观匹配抑制检索噪声，经实验验证其在证据召回和问答准确率上优于基线。
AI 中文摘要
多模态检索增强生成（MM-RAG）虽已取得良好效果，但在复杂多跳推理任务上仍存在不足。现有方法主要聚焦于独立实例级匹配，往往无法捕捉跨模态及跨文档的显式关系。尽管图增强方法引入了结构建模，但在多模态场景中面临核心难题：融入细粒度视觉特征会导致图快速扩张并产生检索噪声，而粗粒度表示则会丢失关键局部证据。为解决该困境，本文提出DualG-MRAG，这是一个包含宏观推理图与微观匹配图的解耦架构，用于多模态RAG。具体而言，为通过分离全局结构推理与细粒度证据匹配来抑制检索噪声，本文构建用于全局拓扑路由的宏观图和用于精确局部验证的微观图；随后，为实现跨异构证据源的动态相关性传播，本文将检索建模为基于查询的消息传递过程，采用GNN Retriever完成；此外，为向生成模型提供连贯的结构指导，本文引入动态规划解码机制，直接从GNN前向传播中提取显式推理路径，替代孤立文档块的标准输入。大量实验表明，DualG-MRAG在证据召回率和复杂问答准确率上均优于基线方法。
英文摘要
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.
CommentsAccepted to the 34th ACM International Conference on Multimedia (ACM MM 2026). 12 pages
