DocTrace uses a document structural tree index, on-demand hypergraph working memory, and graph-structured experience memory to outperform ComoRAG by up to 8.85% F1 and 4.40% EM on three of four long-document QA datasets while cutting compute by 53.32%.
Hyper-RAG: Combating LLM hallucina- tions using hypergraph-driven retrieval-augmented generation,
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HKVM-RAG uses key-value-separated hypergraphs to organize LLM evidence tuples into answer-path hyperedges, yielding F1 gains over KG-PPR on two multi-hop QA benchmarks and further gains when combined with dense retrievers.
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HKVM-RAG: Key-Value-Separated Hypergraph Evidence Organization for Multi-Hop RAG
HKVM-RAG uses key-value-separated hypergraphs to organize LLM evidence tuples into answer-path hyperedges, yielding F1 gains over KG-PPR on two multi-hop QA benchmarks and further gains when combined with dense retrievers.