AI-H^2RAG: Asymmetric Indexing-based Hierarchical Hyper-RAG

Structured RAG has progressed from modeling pairwise relations with ordinary graphs to representing multi-way, higher-order relations with hypergraphs. However, existing approaches still model cross-granularity hierarchies only to a limited extent, constraining their ability to integrate global context with local facts. Inspired by hierarchical associative memory in the cortico-hippocampal system, we note that human memory jointly encodes multiple elements to link higher-level representations with concrete details. Partial cues can then rapidly reinstate relevant context and facts, with high-level context guiding local interpretation and low-level details grounding holistic recall. Following this principle, we propose Asymmetric Indexing-based Hierarchical Hyper-RAG (AI-H^2RAG). AI-H^2RAG constructs three-layer nested topic–chunk, chunk–sentence, and sentence–entity hypergraphs, organizing evidence so that retrieval can proceed from topic-level scope to entity-level facts and trace local facts back to their topical context. Its asymmetric index propagates final parent context downward and aggregates raw child evidence upward. Each query is then aligned with the index to progressively narrow the candidate space and expand linked evidence for answer generation. Evaluations span eight document corpora and include five generator backbones and three evidence-depth stages. AI-H^2RAG achieves the highest average answer quality across the evaluated settings, with relative gains of up to 7.6% over the strongest baseline. Its online retrieval latency is lower than that of the strongest structured-retrieval baseline, showing that hierarchical higher-order retrieval improves answer quality without sacrificing efficiency.