Retrieval-Augmented Generation (RAG) systems often struggle with imperfect retrieval, as traditional retrievers focus on lexical or semantic similarity rather than logical relevance. To address this, we propose \textbf{HopRAG}, a novel RAG framework that augments retrieval with logical reasoning through graph-structured knowledge exploration. During indexing, HopRAG constructs a passage graph, with text chunks as vertices and logical connections established via LLM-generated pseudo-queries as edges. During retrieval, it employs a \textit{retrieve-reason-prune} mechanism: starting with lexically or semantically similar passages, the system explores multi-hop neighbors guided by pseudo-queries and LLM reasoning to identify truly relevant ones. Experiments on multiple multi-hop benchmarks demonstrate that HopRAG's \textit{retrieve-reason-prune} mechanism can expand the retrieval scope based on logical connections and improve final answer quality.
@article{arxiv.2502.12442,
title = {HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation},
author = {Hao Liu and Zhengren Wang and Xi Chen and Zhiyu Li and Feiyu Xiong and Qinhan Yu and Wentao Zhang},
journal= {arXiv preprint arXiv:2502.12442},
year = {2025}
}