@inproceedings{zheng-etal-2026-disentangling,
title = "Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts",
author = "Zheng, Xianda and
Huang, Zijian and
Chiang, Meng-Fen and
Liu, Jiamou and
Fang, Yuan and
Witbrock, Michael J. and
Zhao, Kaiqi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1451/",
pages = "31465--31478",
ISBN = "979-8-89176-390-6",
abstract = "Explicit knowledge conflicts, where retrieved contexts contain contradictory information, have become increasingly prevalent as Large Language Models (LLMs) integrate diverse data sources. The core challenge lies in the complexity of entangled narratives and the heterogeneity of conflict cases, which impose excessive demands on the reasoning capabilities of standard models. To address this, we propose \textbf{K}nowledge \textbf{C}onflict \textbf{R}easoning (\textbf{KCR}), a framework that adjudicates conflicts by structuring the underlying logic. KCR first disentangles conflicting contexts into distinct sets of reasoning traces, utilizing both textual and graph-based representations, to simplify comprehension. It then employs a Reinforcement Learning with Verifiable Rewards (RLVR) paradigm, guiding the model to internalize a reasoning process that maximizes logical consistency while actively suppressing spurious reasoning paths derived from contradictory contexts. Extensive experiments demonstrate that KCR yields substantial improvements: a KCR-enhanced 7B model surpasses the performance of baselines equipped with top-tier closed-source models such as GPT-4o and GPT-5.1."
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<abstract>Explicit knowledge conflicts, where retrieved contexts contain contradictory information, have become increasingly prevalent as Large Language Models (LLMs) integrate diverse data sources. The core challenge lies in the complexity of entangled narratives and the heterogeneity of conflict cases, which impose excessive demands on the reasoning capabilities of standard models. To address this, we propose Knowledge Conflict Reasoning (KCR), a framework that adjudicates conflicts by structuring the underlying logic. KCR first disentangles conflicting contexts into distinct sets of reasoning traces, utilizing both textual and graph-based representations, to simplify comprehension. It then employs a Reinforcement Learning with Verifiable Rewards (RLVR) paradigm, guiding the model to internalize a reasoning process that maximizes logical consistency while actively suppressing spurious reasoning paths derived from contradictory contexts. Extensive experiments demonstrate that KCR yields substantial improvements: a KCR-enhanced 7B model surpasses the performance of baselines equipped with top-tier closed-source models such as GPT-4o and GPT-5.1.</abstract>
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%0 Conference Proceedings
%T Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts
%A Zheng, Xianda
%A Huang, Zijian
%A Chiang, Meng-Fen
%A Liu, Jiamou
%A Fang, Yuan
%A Witbrock, Michael J.
%A Zhao, Kaiqi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zheng-etal-2026-disentangling
%X Explicit knowledge conflicts, where retrieved contexts contain contradictory information, have become increasingly prevalent as Large Language Models (LLMs) integrate diverse data sources. The core challenge lies in the complexity of entangled narratives and the heterogeneity of conflict cases, which impose excessive demands on the reasoning capabilities of standard models. To address this, we propose Knowledge Conflict Reasoning (KCR), a framework that adjudicates conflicts by structuring the underlying logic. KCR first disentangles conflicting contexts into distinct sets of reasoning traces, utilizing both textual and graph-based representations, to simplify comprehension. It then employs a Reinforcement Learning with Verifiable Rewards (RLVR) paradigm, guiding the model to internalize a reasoning process that maximizes logical consistency while actively suppressing spurious reasoning paths derived from contradictory contexts. Extensive experiments demonstrate that KCR yields substantial improvements: a KCR-enhanced 7B model surpasses the performance of baselines equipped with top-tier closed-source models such as GPT-4o and GPT-5.1.
%U https://aclanthology.org/2026.acl-long.1451/
%P 31465-31478
Markdown (Informal)
[Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts](https://aclanthology.org/2026.acl-long.1451/) (Zheng et al., ACL 2026)
ACL
- Xianda Zheng, Zijian Huang, Meng-Fen Chiang, Jiamou Liu, Yuan Fang, Michael J. Witbrock, and Kaiqi Zhao. 2026. Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31465–31478, San Diego, California, United States. Association for Computational Linguistics.