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

Jul 14, 2026

Researchers introduce MG²-RAG, a lightweight framework that constructs a hierarchical multimodal knowledge graph by combining textual parsing with entity-driven visual grounding, enabling unified multimodal nodes. The framework features a multi-granularity graph retrieval mechanism that supports structured multi-hop reasoning and aggregates dense similarities across the graph. MG²-RAG achieves state-of-the-art performance across four multimodal tasks, while reducing graph construction overhead with significant speedup and cost reduction compared to prior graph-based methods.

Why it matters: MG²-RAG advances multimodal retrieval-augmented generation by enabling efficient, fine-grained cross-modal reasoning without relying on costly translation-to-text pipelines.

Full story at: arXiv Information Retrieval ↗