MEGRAG: Multi-Granular Evidence Graphs for Answer-Aware Multi-Hop RAG

MEGRAG proposes an answer-aware framework for multi-hop retrieval-augmented generation. It models reasoning as a path-structured evidence graph connecting passages, sentences, and extracted triples through a cross-granularity index. During retrieval, the system starts with compact triples and expands to sentence- or passage-level context when needed. It uses the current intermediate answer and previous reasoning to determine whether the original question is resolved. If information is still missing, it identifies the gap and generates a focused follow-up query; otherwise, it stops retrieval and answers. The paper reports consistent gains over diverse RAG baselines, but the supplied abstract does not provide benchmark names or numerical results.