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

Retrieval-Augmented Generation (RAG) mitigates hallucinations in Multimodal Large Language Models (MLLMs), yet existing systems struggle with complex cross-modal reasoning. Flat vector retrieval often ignores structural dependencies, while current graph-based methods rely on costly ``translation-to-text'' pipelines that discard fine-grained visual information. To address these limitations, we propose MG2-RAG, a lightweight Multi-Granularity Graph RAG framework that jointly improves graph construction, modality fusion, and cross-modal retrieval. MG2-RAG constructs a hierarchical multimodal knowledge graph by combining lightweight textual parsing with entity-driven visual grounding, enabling textual entities and visual regions to be fused into unified multimodal nodes that preserve atomic evidence. Building on this representation, we introduce a multi-granularity graph retrieval mechanism that aggregates dense similarities and propagates relevance across the graph to support structured multi-hop reasoning. Extensive experiments across four representative multimodal tasks (i.e., retrieval, knowledge-based VQA, reasoning, and classification) demonstrate that MG2-RAG consistently achieves state-of-the-art performance while reducing graph construction overhead with an average 43.3× speedup and 23.9× cost reduction compared with advanced graph-based frameworks.