Separating global structural reasoning from fine-grained evidence matching into two graph tiers significantly improves retrieval accuracy and reasoning quality in multimodal QA systems.
DualG-MRAG improves multimodal retrieval-augmented generation by using two separate graph layers: a macro-graph for global reasoning and a micro-graph for precise evidence matching. This decoupled approach reduces retrieval noise while preserving fine-grained details, and uses graph neural networks to propagate relevance across text and images for better complex question answering.