DAG-RAG: Multi-Path Hierarchical Entity Grounding for GraphRAG

Retrieval-augmented generation (RAG) enhances large language models by retrieving external knowledge, and GraphRAG further enhances this paradigm by organizing knowledge as graphs for structured reasoning. However, existing GraphRAG methods still struggle with grounding complex queries to relevant entities or subgraphs within a large graph space, leading to inefficient retrieval and unstable downstream generation. To address these issues, we propose DAG-RAG, a hierarchical GraphRAG framework that constructs a multi-granularity directed acyclic graph from the original knowledge graph and performs coarse-to-fine grounding with a path-conditioned router. The structured design also enables incremental maintenance by updating only affected clusters without full graph reconstruction or model retraining Experiments show that DAG-RAG improves retrieval accuracy and downstream answer quality while reducing unnecessary graph traversal, demonstrating the effectiveness of hierarchical DAG grounding for scalable and adaptive GraphRAG.