MCoRe: Multi-Entry Complementary Retrieval with Reflection-Guided Iteration for Multi-Hop QA
Retrieval-augmented generation (RAG) has become a standard paradigm for knowledge-intensive question answering by grounding large language models (LLMs) in external evidence. However, open-domain multi-hop question answering (QA) remains challenging for two reasons. First, evidence dispersion across documents and non-contiguous spans means that critical bridge evidence can be weakly related to query and is easy to miss. Second, semantic-resolution mismatch complicates retrieval: coarser retrieval views offer better global coherence but may obscure the exact bridging detail, while finer-grained views highlight specific mentions but may omit the context needed to reveal the relation. In this paper, we propose MCoRe, a multi-entry complementary retrieval framework with reflection-guided iteration for multi-hop QA. To mitigate the semantic-resolution mismatch, MCoRe enables multi-entry complementary retrieval by indexing entry units at multiple semantic resolutions (entities, sentences, and summaries) with explicit links to chunk evidence, mapping all hits back to chunks, and fusing cross-resolution hits via chunk-level voting to form a compact evidence set for answer generation. To cope with evidence dispersion, MCoRe performs reflection-guided iteration: when evidence is insufficient, it identifies the missing bridge cue and issues a gap-focused follow-up query to recover it. Empirical results demonstrate the effectiveness of MCoRe, which consistently outperforms state-of-the-art baselines by 6.77 EM points and 8.79 F1 points averaged over three multi-hop QA benchmarks, with gains of up to 12.70 EM and 14.06 F1 points on 2Wiki.