@inproceedings{liu-etal-2026-hypersu,
title = "{H}yper{SU}: Corpus-Driven Semantic-Unit Hypergraph for Retrieval-Augmented Generation",
author = "Liu, Jiate and
Chen, Liuyi and
Yang, Zhengyi and
He, Chuan and
Ju, Mingchen and
Han, Bocheng and
Liu, Ruyi and
Zhou, Xu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1058/",
pages = "22399--22424",
ISBN = "979-8-89176-462-0",
abstract = "Recent Hypergraph-based retrieval-augmented generation (HyperRAG) methods use hyperedges to connect multiple entities simultaneously, enabling more efficient multi-entity evidence organization than pairwise graph structures. However, existing HyperRAG methods often rely on LLM-generated summaries to construct hyperedges, which can introduce hallucinations while also incurring high indexing costs. In addition, during retrieval, existing methods typically rely on either one-hop neighbor expansion or PageRank diffusion. The former may miss useful multi-hop evidence, while the latter can suffer from uncontrolled propagation over excessive hub nodes, leading to semantic drift and noisy reasoning chains. To address these challenges, we propose HyperSU, a novel hypergraph-based RAG framework featuring semantic-unit hyperedges and clue-guided bidirectional retrieval. During construction, HyperSU formulates hyperedge construction as an entity-aware minimum-description-length (MDL) optimization problem, inducing source-grounded semantic-unit hyperedges that balance sentence-level semantic coherence and entity compactness. It then constructs a hypergraph by modeling each semantic unit as a hyperedge over its co-mentioned entities. During retrieval, HyperSU performs clue-guided bidirectional expansion over the semantic-unit hypergraph, enabling both multi-hop evidence discovery and evidence-anchored noise reduction. Experiments show that HyperSU consistently improves answer accuracy over standard, graph-based, and hypergraph-based RAG baselines, raising average accuracy on GraphRAG-Bench by 5.5 points over the strongest baseline (68.7 vs. 63.2), with larger gains on reasoning-intensive tasks, where the relative improvement reaches 14.1{\%}. Our code is available at https://github.com/RAIDSLab/HyperSU."
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<abstract>Recent Hypergraph-based retrieval-augmented generation (HyperRAG) methods use hyperedges to connect multiple entities simultaneously, enabling more efficient multi-entity evidence organization than pairwise graph structures. However, existing HyperRAG methods often rely on LLM-generated summaries to construct hyperedges, which can introduce hallucinations while also incurring high indexing costs. In addition, during retrieval, existing methods typically rely on either one-hop neighbor expansion or PageRank diffusion. The former may miss useful multi-hop evidence, while the latter can suffer from uncontrolled propagation over excessive hub nodes, leading to semantic drift and noisy reasoning chains. To address these challenges, we propose HyperSU, a novel hypergraph-based RAG framework featuring semantic-unit hyperedges and clue-guided bidirectional retrieval. During construction, HyperSU formulates hyperedge construction as an entity-aware minimum-description-length (MDL) optimization problem, inducing source-grounded semantic-unit hyperedges that balance sentence-level semantic coherence and entity compactness. It then constructs a hypergraph by modeling each semantic unit as a hyperedge over its co-mentioned entities. During retrieval, HyperSU performs clue-guided bidirectional expansion over the semantic-unit hypergraph, enabling both multi-hop evidence discovery and evidence-anchored noise reduction. Experiments show that HyperSU consistently improves answer accuracy over standard, graph-based, and hypergraph-based RAG baselines, raising average accuracy on GraphRAG-Bench by 5.5 points over the strongest baseline (68.7 vs. 63.2), with larger gains on reasoning-intensive tasks, where the relative improvement reaches 14.1%. Our code is available at https://github.com/RAIDSLab/HyperSU.</abstract>
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%0 Conference Proceedings
%T HyperSU: Corpus-Driven Semantic-Unit Hypergraph for Retrieval-Augmented Generation
%A Liu, Jiate
%A Chen, Liuyi
%A Yang, Zhengyi
%A He, Chuan
%A Ju, Mingchen
%A Han, Bocheng
%A Liu, Ruyi
%A Zhou, Xu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-hypersu
%X Recent Hypergraph-based retrieval-augmented generation (HyperRAG) methods use hyperedges to connect multiple entities simultaneously, enabling more efficient multi-entity evidence organization than pairwise graph structures. However, existing HyperRAG methods often rely on LLM-generated summaries to construct hyperedges, which can introduce hallucinations while also incurring high indexing costs. In addition, during retrieval, existing methods typically rely on either one-hop neighbor expansion or PageRank diffusion. The former may miss useful multi-hop evidence, while the latter can suffer from uncontrolled propagation over excessive hub nodes, leading to semantic drift and noisy reasoning chains. To address these challenges, we propose HyperSU, a novel hypergraph-based RAG framework featuring semantic-unit hyperedges and clue-guided bidirectional retrieval. During construction, HyperSU formulates hyperedge construction as an entity-aware minimum-description-length (MDL) optimization problem, inducing source-grounded semantic-unit hyperedges that balance sentence-level semantic coherence and entity compactness. It then constructs a hypergraph by modeling each semantic unit as a hyperedge over its co-mentioned entities. During retrieval, HyperSU performs clue-guided bidirectional expansion over the semantic-unit hypergraph, enabling both multi-hop evidence discovery and evidence-anchored noise reduction. Experiments show that HyperSU consistently improves answer accuracy over standard, graph-based, and hypergraph-based RAG baselines, raising average accuracy on GraphRAG-Bench by 5.5 points over the strongest baseline (68.7 vs. 63.2), with larger gains on reasoning-intensive tasks, where the relative improvement reaches 14.1%. Our code is available at https://github.com/RAIDSLab/HyperSU.
%U https://aclanthology.org/2026.findings-emnlp.1058/
%P 22399-22424
Markdown (Informal)
[HyperSU: Corpus-Driven Semantic-Unit Hypergraph for Retrieval-Augmented Generation](https://aclanthology.org/2026.findings-emnlp.1058/) (Liu et al., Findings 2026)
ACL
- Jiate Liu, Liuyi Chen, Zhengyi Yang, Chuan He, Mingchen Ju, Bocheng Han, Ruyi Liu, and Xu Zhou. 2026. HyperSU: Corpus-Driven Semantic-Unit Hypergraph for Retrieval-Augmented Generation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22399–22424, Budapest, Hungary. Association for Computational Linguistics.