@inproceedings{zhu-etal-2026-hyperpathsrag,
title = "{H}yper{P}aths{RAG}: Multi-Target Path Approximation over Hypergraph Knowledge Graphs for Multi-Hop {QA}",
author = "Zhu, Guanhua and
He, Zirui and
Du, Mengnan and
Yuan, Chenxi",
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.1530/",
doi = "10.18653/2026.findings-emnlp.1530",
pages = "32558--32583",
ISBN = "979-8-89176-462-0",
abstract = "Multi-hop question answering needs evidence that is scattered across many sentences and ordered along a reasoning chain. Existing RAG systems struggle with this setting because flat chunk retrieval can miss bridge facts, binary graph structures lose n-ary information, and unordered top-K contexts leave evidence composition to the answer model. We introduce HyperPathsRAG, a hypergraph-based RAG framework that aligns query decomposition with the corpus index. It decomposes questions into n-ary sub-questions aligned with hypergraph facts, jointly matches all sub-questions to retrieve reasoning paths, scores paths by chain completeness, and presents the retrieved evidence in reasoning order. We evaluate in the candidate-passage setting on the 1k-question hard subsets of HotpotQA, 2Wiki, and MuSiQue, with three answering LLMs. HyperPathsRAG attains the best average Exact Match on every backbone (up to 42.2 EM / 56.2 F1 with Llama-3.3-70B-Instruct), and the best average F1 on two of three backbones; ablations attribute the bulk of the gain to the hypergraph index and the multi-target retriever. Our code is available at https://anonymous.4open.science/r/HyperPathsRAG-AE05/."
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<abstract>Multi-hop question answering needs evidence that is scattered across many sentences and ordered along a reasoning chain. Existing RAG systems struggle with this setting because flat chunk retrieval can miss bridge facts, binary graph structures lose n-ary information, and unordered top-K contexts leave evidence composition to the answer model. We introduce HyperPathsRAG, a hypergraph-based RAG framework that aligns query decomposition with the corpus index. It decomposes questions into n-ary sub-questions aligned with hypergraph facts, jointly matches all sub-questions to retrieve reasoning paths, scores paths by chain completeness, and presents the retrieved evidence in reasoning order. We evaluate in the candidate-passage setting on the 1k-question hard subsets of HotpotQA, 2Wiki, and MuSiQue, with three answering LLMs. HyperPathsRAG attains the best average Exact Match on every backbone (up to 42.2 EM / 56.2 F1 with Llama-3.3-70B-Instruct), and the best average F1 on two of three backbones; ablations attribute the bulk of the gain to the hypergraph index and the multi-target retriever. Our code is available at https://anonymous.4open.science/r/HyperPathsRAG-AE05/.</abstract>
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%0 Conference Proceedings
%T HyperPathsRAG: Multi-Target Path Approximation over Hypergraph Knowledge Graphs for Multi-Hop QA
%A Zhu, Guanhua
%A He, Zirui
%A Du, Mengnan
%A Yuan, Chenxi
%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 zhu-etal-2026-hyperpathsrag
%X Multi-hop question answering needs evidence that is scattered across many sentences and ordered along a reasoning chain. Existing RAG systems struggle with this setting because flat chunk retrieval can miss bridge facts, binary graph structures lose n-ary information, and unordered top-K contexts leave evidence composition to the answer model. We introduce HyperPathsRAG, a hypergraph-based RAG framework that aligns query decomposition with the corpus index. It decomposes questions into n-ary sub-questions aligned with hypergraph facts, jointly matches all sub-questions to retrieve reasoning paths, scores paths by chain completeness, and presents the retrieved evidence in reasoning order. We evaluate in the candidate-passage setting on the 1k-question hard subsets of HotpotQA, 2Wiki, and MuSiQue, with three answering LLMs. HyperPathsRAG attains the best average Exact Match on every backbone (up to 42.2 EM / 56.2 F1 with Llama-3.3-70B-Instruct), and the best average F1 on two of three backbones; ablations attribute the bulk of the gain to the hypergraph index and the multi-target retriever. Our code is available at https://anonymous.4open.science/r/HyperPathsRAG-AE05/.
%R 10.18653/2026.findings-emnlp.1530
%U https://aclanthology.org/2026.findings-emnlp.1530/
%U https://doi.org/10.18653/2026.findings-emnlp.1530
%P 32558-32583
Markdown (Informal)
[HyperPathsRAG: Multi-Target Path Approximation over Hypergraph Knowledge Graphs for Multi-Hop QA](https://aclanthology.org/2026.findings-emnlp.1530/) (Zhu et al., Findings 2026)
ACL