@inproceedings{li-etal-2026-multi,
title = "Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning",
author = "Li, Xudong and
Tian, Yuhang and
Song, Dandan and
Wu, Zhijing and
Zhang, Shuhao and
Yang, Jun and
Huo, Yongyu and
Zhou, Changzhi and
Zhang, Xinyu and
Li, Chenhao and
Ma, Huipeng and
Zhang, Luan and
Xu, Yan and
Liu, Qian",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.853/",
pages = "17259--17276",
ISBN = "979-8-89176-395-1",
abstract = "Knowledge within large language models (LLMs) inevitably lags behind an evolving world, motivating knowledge editing methods that update facts without expensive retraining. In multi-hop knowledge editing, models must not only recall updated facts but also correctly propagate them through multi-step reasoning chains. However, most existing approaches rely on unidirectional, feed-forward pipelines, decomposing questions and retrieving edited facts in a rigid hop-wise sequence. This design is brittle: a minor retrieval error or logical mismatch at an early hop can become a silent failure that cascades to the final answer without an explicit recovery mechanism. To address this limitation, we propose Critic-Guided Multi-Agent Reasoning for Knowledge Editing (CARE), a framework for closed-loop post-edit reasoning. A Critic agent performs chain-level verification by checking both global coherence and step-wise correctness, and triggers bounded backtracking for iterative self-correction, while a Selector agent supplies high-fidelity, low-noise candidate pools from the edit store to enable effective revision. Experiments on MQuAKE-2002 and MQuAKE-hard demonstrate that CARE effectively mitigates error propagation, achieving a new state-of-the-art."
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<abstract>Knowledge within large language models (LLMs) inevitably lags behind an evolving world, motivating knowledge editing methods that update facts without expensive retraining. In multi-hop knowledge editing, models must not only recall updated facts but also correctly propagate them through multi-step reasoning chains. However, most existing approaches rely on unidirectional, feed-forward pipelines, decomposing questions and retrieving edited facts in a rigid hop-wise sequence. This design is brittle: a minor retrieval error or logical mismatch at an early hop can become a silent failure that cascades to the final answer without an explicit recovery mechanism. To address this limitation, we propose Critic-Guided Multi-Agent Reasoning for Knowledge Editing (CARE), a framework for closed-loop post-edit reasoning. A Critic agent performs chain-level verification by checking both global coherence and step-wise correctness, and triggers bounded backtracking for iterative self-correction, while a Selector agent supplies high-fidelity, low-noise candidate pools from the edit store to enable effective revision. Experiments on MQuAKE-2002 and MQuAKE-hard demonstrate that CARE effectively mitigates error propagation, achieving a new state-of-the-art.</abstract>
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%0 Conference Proceedings
%T Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning
%A Li, Xudong
%A Tian, Yuhang
%A Song, Dandan
%A Wu, Zhijing
%A Zhang, Shuhao
%A Yang, Jun
%A Huo, Yongyu
%A Zhou, Changzhi
%A Zhang, Xinyu
%A Li, Chenhao
%A Ma, Huipeng
%A Zhang, Luan
%A Xu, Yan
%A Liu, Qian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-multi
%X Knowledge within large language models (LLMs) inevitably lags behind an evolving world, motivating knowledge editing methods that update facts without expensive retraining. In multi-hop knowledge editing, models must not only recall updated facts but also correctly propagate them through multi-step reasoning chains. However, most existing approaches rely on unidirectional, feed-forward pipelines, decomposing questions and retrieving edited facts in a rigid hop-wise sequence. This design is brittle: a minor retrieval error or logical mismatch at an early hop can become a silent failure that cascades to the final answer without an explicit recovery mechanism. To address this limitation, we propose Critic-Guided Multi-Agent Reasoning for Knowledge Editing (CARE), a framework for closed-loop post-edit reasoning. A Critic agent performs chain-level verification by checking both global coherence and step-wise correctness, and triggers bounded backtracking for iterative self-correction, while a Selector agent supplies high-fidelity, low-noise candidate pools from the edit store to enable effective revision. Experiments on MQuAKE-2002 and MQuAKE-hard demonstrate that CARE effectively mitigates error propagation, achieving a new state-of-the-art.
%U https://aclanthology.org/2026.findings-acl.853/
%P 17259-17276
Markdown (Informal)
[Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning](https://aclanthology.org/2026.findings-acl.853/) (Li et al., Findings 2026)
ACL
- Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, and Qian Liu. 2026. Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17259–17276, San Diego, California, United States. Association for Computational Linguistics.