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

2026-02-16

AI Summary

This work proposes HyperRAG, a novel retrieval-augmented generation (RAG) framework that addresses the limitations of traditional binary knowledge graph–based approaches in multi-hop question answering—namely, rigid retrieval, high computational cost, and insufficient relational expressiveness. HyperRAG is the first to incorporate n-ary hypergraphs into RAG, modeling high-order relational facts through hypergraph structures. It integrates structural-semantic joint reasoning with parametric memory from large language models, featuring a HyperRetriever module that adaptively constructs multi-hop reasoning paths and a HyperMemory mechanism that dynamically guides path expansion. Evaluated on benchmarks including WikiTopics and HotpotQA, HyperRAG significantly outperforms existing methods, achieving an average improvement of 2.95% in MRR and 1.23% in Hits@10, while also demonstrating enhanced interpretability and cross-domain generalization capabilities.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• retrieval-augmented generation
• n-ary relations
• hypergraphs
• multi-hop reasoning
• knowledge representation

Innovation

• HyperRAG
• n-ary hypergraphs
• retrieval-augmented generation
• multi-hop reasoning
• structural-semantic reasoning