@inproceedings{zhang-etal-2026-perceive,
title = "Perceive-Then-Adjudicate: A Multi-Agent Framework for Resolving Knowledge-Evidence Conflicts in {LLM} Fact-Checking",
author = "Zhang, Yongcheng and
Zhang, Xin and
Li, Fei and
Teng, Chong and
Yang, Jiang-Ming and
Ji, Donghong and
Li, Zhuang",
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.1445/",
pages = "30813--30833",
ISBN = "979-8-89176-462-0",
abstract = "Fact-checking has transitioned from traditional semantic matching toward active reasoning powered by Large Language Models (LLMs), manifesting as the interplay between internal parametric knowledge and external grounded evidence. However, this paradigm introduces two critical challenges: (i) excessive reliance on external evidence even when it conflicts with correct internal judgment, and (ii) uncritical acceptance of a consensus even when both sources are erroneous. To address these limitations, we propose a Multi-Agent Arbitration Framework where a meta-cognitive Adjudication Agent (AA) orchestrates a Knowledge Agent (KA) and an Evidence Agent (EA). Through hybrid reinforcement and imitation learning, the AA perceives conflict signals between KA and EA to adaptively calibrate judgmental autonomy against evidentiary support. Upon detecting a potentially erroneous consensus, the AA invokes a Reflective Rejection mechanism to scrutinize and disrupt deceptive alignment. Empirical evaluations on Snopes and PolitiFact demonstrate that our framework consistently outperforms state-of-the-art baselines. Notably, it effectively mitigates over-reliance errors while maintaining high fidelity in correct-consensus scenarios, recovering 26.2{\%} of dual-error cases where static systems typically succumb to superficial consistency. Our code is publicly available at https://github.com/Zhang-Gorden/Perceive-Then-Adjudicate."
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<abstract>Fact-checking has transitioned from traditional semantic matching toward active reasoning powered by Large Language Models (LLMs), manifesting as the interplay between internal parametric knowledge and external grounded evidence. However, this paradigm introduces two critical challenges: (i) excessive reliance on external evidence even when it conflicts with correct internal judgment, and (ii) uncritical acceptance of a consensus even when both sources are erroneous. To address these limitations, we propose a Multi-Agent Arbitration Framework where a meta-cognitive Adjudication Agent (AA) orchestrates a Knowledge Agent (KA) and an Evidence Agent (EA). Through hybrid reinforcement and imitation learning, the AA perceives conflict signals between KA and EA to adaptively calibrate judgmental autonomy against evidentiary support. Upon detecting a potentially erroneous consensus, the AA invokes a Reflective Rejection mechanism to scrutinize and disrupt deceptive alignment. Empirical evaluations on Snopes and PolitiFact demonstrate that our framework consistently outperforms state-of-the-art baselines. Notably, it effectively mitigates over-reliance errors while maintaining high fidelity in correct-consensus scenarios, recovering 26.2% of dual-error cases where static systems typically succumb to superficial consistency. Our code is publicly available at https://github.com/Zhang-Gorden/Perceive-Then-Adjudicate.</abstract>
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%0 Conference Proceedings
%T Perceive-Then-Adjudicate: A Multi-Agent Framework for Resolving Knowledge-Evidence Conflicts in LLM Fact-Checking
%A Zhang, Yongcheng
%A Zhang, Xin
%A Li, Fei
%A Teng, Chong
%A Yang, Jiang-Ming
%A Ji, Donghong
%A Li, Zhuang
%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 zhang-etal-2026-perceive
%X Fact-checking has transitioned from traditional semantic matching toward active reasoning powered by Large Language Models (LLMs), manifesting as the interplay between internal parametric knowledge and external grounded evidence. However, this paradigm introduces two critical challenges: (i) excessive reliance on external evidence even when it conflicts with correct internal judgment, and (ii) uncritical acceptance of a consensus even when both sources are erroneous. To address these limitations, we propose a Multi-Agent Arbitration Framework where a meta-cognitive Adjudication Agent (AA) orchestrates a Knowledge Agent (KA) and an Evidence Agent (EA). Through hybrid reinforcement and imitation learning, the AA perceives conflict signals between KA and EA to adaptively calibrate judgmental autonomy against evidentiary support. Upon detecting a potentially erroneous consensus, the AA invokes a Reflective Rejection mechanism to scrutinize and disrupt deceptive alignment. Empirical evaluations on Snopes and PolitiFact demonstrate that our framework consistently outperforms state-of-the-art baselines. Notably, it effectively mitigates over-reliance errors while maintaining high fidelity in correct-consensus scenarios, recovering 26.2% of dual-error cases where static systems typically succumb to superficial consistency. Our code is publicly available at https://github.com/Zhang-Gorden/Perceive-Then-Adjudicate.
%U https://aclanthology.org/2026.findings-emnlp.1445/
%P 30813-30833
Markdown (Informal)
[Perceive-Then-Adjudicate: A Multi-Agent Framework for Resolving Knowledge-Evidence Conflicts in LLM Fact-Checking](https://aclanthology.org/2026.findings-emnlp.1445/) (Zhang et al., Findings 2026)
ACL