HKVM-RAG: Key-Value-Separated Hypergraph Evidence Organization for Multi-Hop RAG

• URL: http://arxiv.org/abs/2606.07218v2
• Date: Thu, 11 Jun 2026 06:04:57 GMT
• Title: HKVM-RAG: Key-Value-Separated Hypergraph Evidence Organization for Multi-Hop RAG
• Abstract summary: Multi-hop RAG poses a problem beyond passage matching: under fixed retrieval budgets, a system must organize retrieved text into evidence units that expose answer chains.<n>We present HKVM-RAG, a key-value-separated evidence-organization layer.
• Score: 5.749820809225301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-hop RAG poses a data-engineering problem beyond passage matching: under fixed retrieval budgets, a system must organize retrieved text into evidence units that expose answer chains. Dense retrievers score passages independently, while graph-based memories make associations explicit but often rely on pairwise or entity-centered keys that fragment multi-hop evidence. We present HKVM-RAG, a key-value-separated evidence-organization layer. It assembles answer-path hyperedges from cached passage-level LLM evidence tuples and uses them as retrieval keys, while retaining passage text as answer values. To isolate key-space design, our fixed-substrate protocol holds the tuple cache, candidate passages, reader, and evaluation budget constant across pairwise graph and hypergraph variants. Weighted hypergraph key-value retrieval improves over KG-PPR by +3.426 F1 on 2WikiMultiHopQA and +3.592 F1 on MuSiQue; HotpotQA shows that higher structured support coverage need not yield standalone answer-F1 gains. We therefore study WHG-KV as an evidence-control signal rather than a dense-retrieval replacement. Oracle and train-to-dev analyses identify support selection as repairable, and a dense-aware controller combines frozen ColBERTv2 and HKVM rank/score features using out-of-fold HKVM predictions. It reaches 88.846, 65.073, and 85.810 F1 on the three benchmarks, improving over ColBERTv2 by +11.084, +6.763, and +5.966 F1. Source-level ablations show that matched non-WHG structured signals do not match the WHG-KV gains. These results provide bounded evidence that key-value-separated hypergraph organization can serve as a reusable evidence-control mechanism for multi-hop RAG.

Related papers

• Constrained Dominant Sets for Multimodal Document Question Answering [3.7607300100158345]
  Long multimodal document question answering is limited by which evidence reaches the reader, rather than by the quantity retrieved.<n>This work introduces a retriever that selects evidence as a Constrained Dominant Set on a query-augmented affinity graph.
  arXiv  Detail & Related papers  (2026-06-05T13:24:46Z)
• AdaGATE: Adaptive Gap-Aware Token-Efficient Evidence Assembly for Multi-Hop Retrieval-Augmented Generation [6.314380942821944]
  We propose AdaGATE, a training-free evidence controller for multi-hop RAG.<n>AdaGATE frames evidence selection as a token-constrained repair problem.<n>We evaluate AdaGATE on HotpotQA under clean, redundancy, and noise injected retrieval conditions.
  arXiv  Detail & Related papers  (2026-05-04T14:45:49Z)
• CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search [51.911048955965136]
  CoSearch is a framework that jointly trains a multi-step reasoning agent and a generative document ranking model.<n>Our results show that joint training of the reasoning agent and retrieval system is both feasible and strongly performant.
  arXiv  Detail & Related papers  (2026-04-19T17:48:17Z)
• PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering [57.89576196160413]
  Large language models (LLMs) remain brittle on multi-hop question answering (MHQA)<n>We propose textbfPlanned Active Retrieval and Reasoning RAG (PAR$2$-RAG), a two-stage framework that separates emphcoverage from emphcommitment.
  arXiv  Detail & Related papers  (2026-03-30T23:52:54Z)
• Decomposing Query-Key Feature Interactions Using Contrastive Covariances [75.38737409771085]
  We study the query-key space -- the bilinear joint embedding space between queries and keys.<n>It is when features in keys and queries align in these low-rank subspaces that high attention scores are produced.
  arXiv  Detail & Related papers  (2026-02-04T16:50:02Z)
• N2N-GQA: Noise-to-Narrative for Graph-Based Table-Text Question Answering Using LLMs [0.0]
  Multi-hop question answering over hybrid table-text data requires retrieving and reasoning across multiple evidence pieces from large corpora.<n>Standard Retrieval-Augmented Generation (RAG) pipelines process documents as flat ranked lists, causing retrieval noise to obscure reasoning chains.<n>N2N-GQA is the first zeroshot framework for open-domain hybrid table-text QA that constructs dynamic evidence graphs from noisy retrieval outputs.
  arXiv  Detail & Related papers  (2026-01-10T15:55:15Z)
• TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework [62.66056331998838]
  TeaRAG is a token-efficient agentic RAG framework capable of compressing both retrieval content and reasoning steps.<n>Our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps.
  arXiv  Detail & Related papers  (2025-11-07T16:08:34Z)
• Hierarchical Sequence Iteration for Heterogeneous Question Answering [27.22775290181187]
  This paper introduces HSEQ, a unified framework that linearizes documents, tables, and knowledge graphs into a reversible hierarchical sequence.<n> Experiments on HotpotQA (text), HybridQA/TAT-QA (table+text), and MetaQA (KG) show consistent EM/F1 gains over strong single-pass, multi-hop, and agentic RAG baselines with high efficiency.
  arXiv  Detail & Related papers  (2025-10-23T12:48:18Z)
• TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation [30.485127201645437]
  We propose TRACE to enhance the multi-hop reasoning ability of RAG models. TRACE constructs knowledge-grounded reasoning chains, which are a series of logically connected knowledge triples. TRACE achieves an average performance improvement of up to 14.03% compared to using all the retrieved documents.
  arXiv  Detail & Related papers  (2024-06-17T12:23:32Z)
• Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning [61.73411954056032]
  Key Point Analysis (KPA) continues to be a significant and unresolved issue within the field of argument mining. We propose a novel approach for KPA with pairwise generation and graph partitioning.
  arXiv  Detail & Related papers  (2024-04-17T13:44:29Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.