HKVM-RAG: Hypergraph Key-Value Organization Improves Multi-Hop Retrieval-Augmented Generation
Researchers introduced HKVM-RAG, a new evidence-organization method for multi-hop retrieval-augmented generation (RAG) that uses hypergraph structures to organize retrieved passages into answer chains. The approach separates retrieval keys from answer values, improving performance on benchmark datasets like 2WikiMultiHopQA and MuSiQue by 3-4 F1 points over prior graph-based methods. The work addresses a fundamental data-engineering challenge in RAG systems: organizing retrieved text to expose multi-step reasoning paths within fixed retrieval budgets.
HKVM-RAG proposes a key-value-separated evidence-organization layer for multi-hop retrieval-augmented generation systems. Rather than relying on dense retrievers that score passages independently or graph-based memories with entity-centered keys, the method assembles answer-path hyperedges from cached passage-level LLM evidence tuples, using these hyperedges as retrieval keys while retaining passage text as answer values. Testing on three benchmarks—2WikiMultiHopQA, MuSiQue, and HotpotQA—showed weighted hypergraph key-value retrieval outperformed knowledge-graph-based baselines by 3-4 F1 points. When combined with a dense-aware controller that integrates frozen ColBERTv2 features, the system achieved substantial improvements: +11.084 F1 on 2WikiMultiHopQA, +6.763 on MuSiQue, and +5.966 on HotpotQA. Ablation studies confirmed that matched non-hypergraph structured signals did not replicate these gains, suggesting the hypergraph organization itself provides a reusable evidence-control mechanism.
What's missing
The paper does not discuss computational complexity or runtime overhead of the hypergraph construction and retrieval process compared to baseline methods. Additionally, the generalization of the approach to other RAG tasks beyond multi-hop question answering (e.g., summarization, open-domain QA) remains unexplored.
What different sources said
- arXiv cs.CLCenter
ConRAG: Consensus-Driven Multi-View Retrieval for Multi-Hop Question Answering
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