HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation

TL;DR

HyperRAG introduces a novel framework for reasoning over n-ary hypergraphs in retrieval-augmented generation, improving multi-hop question answering by capturing richer relational information and enabling more efficient, interpretable reasoning paths.

Contribution

The paper presents HyperRAG, a new RAG framework that leverages n-ary hypergraphs with two retrieval methods, enhancing reasoning and accuracy in QA tasks beyond traditional binary knowledge graphs.

Findings

HyperRetriever outperforms baselines with 2.95% higher MRR.

HyperMemory guides dynamic, query-aware path expansion.

HyperRAG improves reasoning interpretability and efficiency.

Abstract

Graph-based retrieval-augmented generation (RAG) methods, typically built on knowledge graphs (KGs) with binary relational facts, have shown promise in multi-hop open-domain QA. However, their rigid retrieval schemes and dense similarity search often introduce irrelevant context, increase computational overhead, and limit relational expressiveness. In contrast, n-ary hypergraphs encode higher-order relational facts that capture richer inter-entity dependencies and enable shallower, more efficient reasoning paths. To address this limitation, we propose HyperRAG, a RAG framework tailored for n-ary hypergraphs with two complementary retrieval variants: (i) HyperRetriever learns structural-semantic reasoning over n-ary facts to construct query-conditioned relational chains. It enables accurate factual tracking, adaptive high-order traversal, and interpretable multi-hop reasoning under context constraints. (ii) HyperMemory leverages the LLM's parametric memory to guide beam search, dynamically scoring n-ary facts and entities for query-aware path expansion. Extensive evaluations on WikiTopics (11 closed-domain datasets) and three open-domain QA benchmarks (HotpotQA, MuSiQue, and 2WikiMultiHopQA) validate HyperRAG's effectiveness. HyperRetriever achieves the highest answer accuracy overall, with average gains of 2.95% in MRR and 1.23% in Hits@10 over the strongest baseline. Qualitative analysis further shows that HyperRetriever bridges reasoning gaps through adaptive and interpretable n-ary chain construction, benefiting both open and closed-domain QA.