@inproceedings{liu-sharma-2026-claimclaire,
title = "{C}laim{CLAIRE}: A Trust-Aware Multi-Component Fact-Checking Agent for Open-World Claims",
author = "Liu, Xinman and
Sharma, Mayank",
editor = "Chang, Kai-Wei and
Mehrabi, Ninareh and
Krishna, Satyapriya and
Das, Anubrata and
Dhamala, Jwala and
Cao, Yang Trista and
Kumarage, Tharindu and
Ramakrishna, Anil and
Christodoulopoulos, Christos and
Wan, Yixin and
Galystan, Aram and
Kumar, Anoop and
Gupta, Rahul",
booktitle = "Proceedings of the 6th Workshop on Trustworthy {NLP} ({T}rust{NLP} 2026)",
month = jul,
year = "2026",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.trustnlp-main.6/",
pages = "73--91",
ISBN = "979-8-89176-418-7",
abstract = "Verifying complex real-world claims against diverse and potentially unreliable open-web sources requires balancing evidence comprehensiveness with rigorous source reliability. Current automated fact-checking approaches often fail to address this holistically, losing contextual dependencies and applying trust signals monolithically at the document level.We introduce ClaimCLAIRE, a multi-component fact-checking agent that integrates four key innovations: (1) iterative component-aware decomposition with exhaustiveness validation, (2) holistic evidence gathering using a ReAct agent that maintains cross-component semantic awareness, (3) trust-modulated retrieval that weights evidence by source credibility to mitigate the influence of misinformation, and (4) adaptive gap-filling to address recall bottlenecks in under-supported sub-claims.Evaluated on the AVeriTeC benchmark, ClaimCLAIRE achieves 84.27{\%} accuracy and a macro-F1 of 0.806. Our systematic ablations demonstrate that while decomposition alone can degrade performance, its integration with trust-aware retrieval and adaptive gap-filling yields a pipeline where component-level verdicts, source trust ratings, and deterministic AND-logic synthesis together support transparent, accountable fact verification."
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<abstract>Verifying complex real-world claims against diverse and potentially unreliable open-web sources requires balancing evidence comprehensiveness with rigorous source reliability. Current automated fact-checking approaches often fail to address this holistically, losing contextual dependencies and applying trust signals monolithically at the document level.We introduce ClaimCLAIRE, a multi-component fact-checking agent that integrates four key innovations: (1) iterative component-aware decomposition with exhaustiveness validation, (2) holistic evidence gathering using a ReAct agent that maintains cross-component semantic awareness, (3) trust-modulated retrieval that weights evidence by source credibility to mitigate the influence of misinformation, and (4) adaptive gap-filling to address recall bottlenecks in under-supported sub-claims.Evaluated on the AVeriTeC benchmark, ClaimCLAIRE achieves 84.27% accuracy and a macro-F1 of 0.806. Our systematic ablations demonstrate that while decomposition alone can degrade performance, its integration with trust-aware retrieval and adaptive gap-filling yields a pipeline where component-level verdicts, source trust ratings, and deterministic AND-logic synthesis together support transparent, accountable fact verification.</abstract>
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%0 Conference Proceedings
%T ClaimCLAIRE: A Trust-Aware Multi-Component Fact-Checking Agent for Open-World Claims
%A Liu, Xinman
%A Sharma, Mayank
%Y Chang, Kai-Wei
%Y Mehrabi, Ninareh
%Y Krishna, Satyapriya
%Y Das, Anubrata
%Y Dhamala, Jwala
%Y Cao, Yang Trista
%Y Kumarage, Tharindu
%Y Ramakrishna, Anil
%Y Christodoulopoulos, Christos
%Y Wan, Yixin
%Y Galystan, Aram
%Y Kumar, Anoop
%Y Gupta, Rahul
%S Proceedings of the 6th Workshop on Trustworthy NLP (TrustNLP 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-418-7
%F liu-sharma-2026-claimclaire
%X Verifying complex real-world claims against diverse and potentially unreliable open-web sources requires balancing evidence comprehensiveness with rigorous source reliability. Current automated fact-checking approaches often fail to address this holistically, losing contextual dependencies and applying trust signals monolithically at the document level.We introduce ClaimCLAIRE, a multi-component fact-checking agent that integrates four key innovations: (1) iterative component-aware decomposition with exhaustiveness validation, (2) holistic evidence gathering using a ReAct agent that maintains cross-component semantic awareness, (3) trust-modulated retrieval that weights evidence by source credibility to mitigate the influence of misinformation, and (4) adaptive gap-filling to address recall bottlenecks in under-supported sub-claims.Evaluated on the AVeriTeC benchmark, ClaimCLAIRE achieves 84.27% accuracy and a macro-F1 of 0.806. Our systematic ablations demonstrate that while decomposition alone can degrade performance, its integration with trust-aware retrieval and adaptive gap-filling yields a pipeline where component-level verdicts, source trust ratings, and deterministic AND-logic synthesis together support transparent, accountable fact verification.
%U https://aclanthology.org/2026.trustnlp-main.6/
%P 73-91
Markdown (Informal)
[ClaimCLAIRE: A Trust-Aware Multi-Component Fact-Checking Agent for Open-World Claims](https://aclanthology.org/2026.trustnlp-main.6/) (Liu & Sharma, TrustNLP 2026)
ACL