MG2-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) mitigates halluci-nations in Multimodal Large Language Models (MLLMs), yet existingsystems struggle with complex cross-modal reasoning. Flat vector retrievaloften ignores structural dependencies, while current graph-based methodsrely on costly “translation-to-text” pipelines that discard fine-grainedvisual information. To address these limitations, we propose MG2 -RAG,a lightweight Multi-Granularity Graph RAG framework that jointlyimproves graph construction, modality fusion, and cross-modal retrieval.MG2 -RAG constructs a hierarchical multimodal knowledge graph bycombining lightweight textual parsing with entity-driven visual ground-ing, enabling textual entities and visual regions to be fused into unifiedmultimodal nodes that preserve atomic evidence. Building on this repre-sentation, we introduce a multi-granularity graph retrieval mechanismthat aggregates dense similarities and propagates relevance across thegraph to support structured multi-hop reasoning. Extensive experimentsacross four representative multimodal tasks (i.e., retrieval, knowledge-based VQA, reasoning, and classification) demonstrate that MG2 -RAGconsistently achieves state-of-the-art performance while reducing graphconstruction overhead with an average 43.3× speedup and 23.9× costreduction compared with advanced graph-based frameworks. The sourcecode is publicly available at https://github.com/Daboolu/MG2-RAG.