ECG-RAG: Evidence Chain-Guided Retrieval for Multi-Hop Question Answering

Retrieval-Augmented Generation (RAG) is widely used to improve the factual grounding of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries, standard RAG systems struggle with multi-hop questions where the necessary evidence is scattered across multiple passages. Recent graph-based methods capture relations among entities and text units to support broader context retrieval for complex reasoning tasks. However, existing dense and graph-based retrievers often optimize local relevance or neighborhood coverage, without explicitly ensuring that the retrieved evidence forms a complete support chain from the question to the answer. In this paper, we propose Evidence Chain-Guided Retrieval-Augmented Generation (ECG-RAG), a retrieval framework that explicitly targets evidence-chain completeness for multi-hop RAG. To support this goal, ECG-RAG adopts a hierarchical hypergraph that aligns atomic entities, pairwise relations, high-level multi-entity relations, and source passages in a unified retrieval space. At inference time, ECG-RAG parses each query into an evidence-chain goal and activates anchor nodes in the pairwise-relation layer. It then uses ant-inspired exploration to dynamically prioritize relation transitions that advance this goal, thereby deriving a compact query-specific local relation graph for subsequent path search. A${}^{\ast }$-style search is then applied over this local graph to assemble evidence paths that cover the target chain. The selected paths are projected back to grounded source passages and organized into path-structured evidence context for answer generation. Extensive experiments on six QA benchmarks show that ECG-RAG achieves the best overall QA performance, reaching 60.4 EM and 66.6 F1 with average gains of 2.1 EM and 0.7 F1 over the strongest baseline. Retrieval diagnostics on 2WikiMultiHopQA show 93.6% Evidence Recall, with all annotated supporting evidence covered in the final generation context for 85.0% of the questions.