Query-Aware Knowledge Retrieval via Hyperbolic Structuring
TL;DR
HyperRAG is introduced, a novel framework in the Hyper-bolic space that captures both explicit entity-based links and implicit query-aware connections and consistently outperforms existing baselines.
Abstract
Retrieval-Augmented Generation (RAG) has demonstrated significant potential in enhancing large language models (LLMs) by supplementing external knowledge. However, existing approaches focus primarily on retrieving isolated factual knowledge entities while neglecting the critical reasoning relationships. To address this limitation, Graph-Augmented Generation (GraphRAG) has emerged as an effective so-lution, which explicitly integrates structured knowledge graphs to support complex reasoning tasks. Although diverse graph construction methods have been explored, they typically rely on static, query-agnostic graphs constructed via fixed heuristics. We are thereby motivated to propose a query-centric retrieval framework that adaptively constructs a graph tailored to each query. However, it is challenging to accurately identify these latent relationships from queries to the corpus. Moreover, unifying multiple local-perspective connections into a globally coherent structured corpus introduces additional complexity. To this end, we introduce HyperRAG, a novel framework in the Hyper-bolic space that captures both explicit entity-based links and implicit query-aware connections. Extensive experiments on three benchmark datasets demonstrate that our framework consistently outperforms existing baselines.