@inproceedings{zhu-etal-2026-kgflow,
title = "{KGF}low: Enhancing Large Language Model Reasoning with Multi-Graph Alignment for Knowledge-Intensive Medical Question Answering",
author = "Zhu, Tiantian and
Kong, Xiangdong and
Wang, Xizhao and
Ng, Wing W. Y. and
Jia, Yuheng and
Zhang, Jun and
Zhu, Zexuan",
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.1494/",
pages = "31799--31819",
ISBN = "979-8-89176-462-0",
abstract = "Medical question answering (QA) is a knowledge-intensive task demanding evidence-based reasoning across diverse sources. Despite the demonstrated efficacy of augmenting large language models (LLMs) with domain-specific knowledge graphs (KGs) for QA tasks, the prevailing single-KG paradigm neglects the benefits of multi-KG complementarity. This paper introduces KGFlow, a novel multi-KG reasoning paradigm that mitigates the incomplete coverage and fragmented evidence of single-KG approaches through three LLM agents. Given a query, Knowledge Explorer extracts relevant knowledge triples in parallel from heterogeneous sources. Graph Aligner then semantically harmonizes these triples into global reasoning paths, which are filtered by Contextual Pruner to preserve the most valuable paths for inference. Extensive experiments show that KGFlow achieves promising performance improvements compared to state-of-the-art techniques on five medical QA datasets. We further observe by cases that the plug-and-play nature of our framework enables its application to diverse KG combinations and multilingual scenarios, resulting in broader knowledge coverage than single-KG approaches."
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<abstract>Medical question answering (QA) is a knowledge-intensive task demanding evidence-based reasoning across diverse sources. Despite the demonstrated efficacy of augmenting large language models (LLMs) with domain-specific knowledge graphs (KGs) for QA tasks, the prevailing single-KG paradigm neglects the benefits of multi-KG complementarity. This paper introduces KGFlow, a novel multi-KG reasoning paradigm that mitigates the incomplete coverage and fragmented evidence of single-KG approaches through three LLM agents. Given a query, Knowledge Explorer extracts relevant knowledge triples in parallel from heterogeneous sources. Graph Aligner then semantically harmonizes these triples into global reasoning paths, which are filtered by Contextual Pruner to preserve the most valuable paths for inference. Extensive experiments show that KGFlow achieves promising performance improvements compared to state-of-the-art techniques on five medical QA datasets. We further observe by cases that the plug-and-play nature of our framework enables its application to diverse KG combinations and multilingual scenarios, resulting in broader knowledge coverage than single-KG approaches.</abstract>
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%0 Conference Proceedings
%T KGFlow: Enhancing Large Language Model Reasoning with Multi-Graph Alignment for Knowledge-Intensive Medical Question Answering
%A Zhu, Tiantian
%A Kong, Xiangdong
%A Wang, Xizhao
%A Ng, Wing W. Y.
%A Jia, Yuheng
%A Zhang, Jun
%A Zhu, Zexuan
%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-kgflow
%X Medical question answering (QA) is a knowledge-intensive task demanding evidence-based reasoning across diverse sources. Despite the demonstrated efficacy of augmenting large language models (LLMs) with domain-specific knowledge graphs (KGs) for QA tasks, the prevailing single-KG paradigm neglects the benefits of multi-KG complementarity. This paper introduces KGFlow, a novel multi-KG reasoning paradigm that mitigates the incomplete coverage and fragmented evidence of single-KG approaches through three LLM agents. Given a query, Knowledge Explorer extracts relevant knowledge triples in parallel from heterogeneous sources. Graph Aligner then semantically harmonizes these triples into global reasoning paths, which are filtered by Contextual Pruner to preserve the most valuable paths for inference. Extensive experiments show that KGFlow achieves promising performance improvements compared to state-of-the-art techniques on five medical QA datasets. We further observe by cases that the plug-and-play nature of our framework enables its application to diverse KG combinations and multilingual scenarios, resulting in broader knowledge coverage than single-KG approaches.
%U https://aclanthology.org/2026.findings-emnlp.1494/
%P 31799-31819
Markdown (Informal)
[KGFlow: Enhancing Large Language Model Reasoning with Multi-Graph Alignment for Knowledge-Intensive Medical Question Answering](https://aclanthology.org/2026.findings-emnlp.1494/) (Zhu et al., Findings 2026)
ACL