@inproceedings{du-etal-2026-super,
title = "Super Paths, Better Reasoning: Multi-Hop Knowledge Graphs Reasoning with {LLM} via Super-Path Ranking and Constraint Verification",
author = "Du, Kunpeng and
Zheng, Yuhang and
Li, Xiaopeng and
Gao, Chen and
Wang, Jishu and
Zhu, Rui and
Zhang, Xuan",
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.408/",
pages = "8617--8637",
ISBN = "979-8-89176-462-0",
abstract = "Knowledge graph reasoning (KGR) composes multi-hop facts over KGs to support complex knowledge-intensive tasks, providing reliable external factual support for large language models (LLMs) and helping mitigate hallucinations. However, building compact, faithful, and high-quality contexts from large-scale KGs remains challenging. We observe a non-monotonic relationship between retrieval scale and reasoning quality: insufficient retrieval misses critical paths, while excessive retrieval introduces redundant and noisy paths that burden LLMs reasoning. Motivated by this observation, we propose super-path ranking and constraint verification (SPaRC), an efficient framework that constructs, ranks, and verifies super-paths before LLMs reasoning. SPaRC compresses redundant paths into super-paths, ranks them with contrastive learning, verifies top-ranked paths under query semantic constraints, and provides the verified context to the LLMs for answer reasoning. Experiments on WebQSP and CWQ show that SPaRC achieves state-of-the-art performance, improving Hit@1 by up to 12.0 points and F1 by up to 5.9 points on CWQ. On challenging settings covering deeper hop reasoning, multiple topic entities, and multi-answer questions, SPaRC further improves F1 on two benchmarks by up to 17.6{\%} and 42.1{\%}, 26.7{\%} and 5.9{\%}, and 10.4{\%} and 79.4{\%}, respectively, while reducing reasoning tokens by 29.7{\%} and retrieval time by 25{\%}."
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<abstract>Knowledge graph reasoning (KGR) composes multi-hop facts over KGs to support complex knowledge-intensive tasks, providing reliable external factual support for large language models (LLMs) and helping mitigate hallucinations. However, building compact, faithful, and high-quality contexts from large-scale KGs remains challenging. We observe a non-monotonic relationship between retrieval scale and reasoning quality: insufficient retrieval misses critical paths, while excessive retrieval introduces redundant and noisy paths that burden LLMs reasoning. Motivated by this observation, we propose super-path ranking and constraint verification (SPaRC), an efficient framework that constructs, ranks, and verifies super-paths before LLMs reasoning. SPaRC compresses redundant paths into super-paths, ranks them with contrastive learning, verifies top-ranked paths under query semantic constraints, and provides the verified context to the LLMs for answer reasoning. Experiments on WebQSP and CWQ show that SPaRC achieves state-of-the-art performance, improving Hit@1 by up to 12.0 points and F1 by up to 5.9 points on CWQ. On challenging settings covering deeper hop reasoning, multiple topic entities, and multi-answer questions, SPaRC further improves F1 on two benchmarks by up to 17.6% and 42.1%, 26.7% and 5.9%, and 10.4% and 79.4%, respectively, while reducing reasoning tokens by 29.7% and retrieval time by 25%.</abstract>
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%0 Conference Proceedings
%T Super Paths, Better Reasoning: Multi-Hop Knowledge Graphs Reasoning with LLM via Super-Path Ranking and Constraint Verification
%A Du, Kunpeng
%A Zheng, Yuhang
%A Li, Xiaopeng
%A Gao, Chen
%A Wang, Jishu
%A Zhu, Rui
%A Zhang, Xuan
%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 du-etal-2026-super
%X Knowledge graph reasoning (KGR) composes multi-hop facts over KGs to support complex knowledge-intensive tasks, providing reliable external factual support for large language models (LLMs) and helping mitigate hallucinations. However, building compact, faithful, and high-quality contexts from large-scale KGs remains challenging. We observe a non-monotonic relationship between retrieval scale and reasoning quality: insufficient retrieval misses critical paths, while excessive retrieval introduces redundant and noisy paths that burden LLMs reasoning. Motivated by this observation, we propose super-path ranking and constraint verification (SPaRC), an efficient framework that constructs, ranks, and verifies super-paths before LLMs reasoning. SPaRC compresses redundant paths into super-paths, ranks them with contrastive learning, verifies top-ranked paths under query semantic constraints, and provides the verified context to the LLMs for answer reasoning. Experiments on WebQSP and CWQ show that SPaRC achieves state-of-the-art performance, improving Hit@1 by up to 12.0 points and F1 by up to 5.9 points on CWQ. On challenging settings covering deeper hop reasoning, multiple topic entities, and multi-answer questions, SPaRC further improves F1 on two benchmarks by up to 17.6% and 42.1%, 26.7% and 5.9%, and 10.4% and 79.4%, respectively, while reducing reasoning tokens by 29.7% and retrieval time by 25%.
%U https://aclanthology.org/2026.findings-emnlp.408/
%P 8617-8637
Markdown (Informal)
[Super Paths, Better Reasoning: Multi-Hop Knowledge Graphs Reasoning with LLM via Super-Path Ranking and Constraint Verification](https://aclanthology.org/2026.findings-emnlp.408/) (Du et al., Findings 2026)
ACL
- Kunpeng Du, Yuhang Zheng, Xiaopeng Li, Chen Gao, Jishu Wang, Rui Zhu, and Xuan Zhang. 2026. Super Paths, Better Reasoning: Multi-Hop Knowledge Graphs Reasoning with LLM via Super-Path Ranking and Constraint Verification. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 8617–8637, Budapest, Hungary. Association for Computational Linguistics.