VerifiAgent: a Unified Verification Agent in Language Model Reasoning

Jiuzhou Han, Wray Buntine, Ehsan Shareghi


Abstract
Large language models demonstrate remarkable reasoning capabilities but often produce unreliable or incorrect responses. Existing verification methods are typically model-specific or domain-restricted, requiring significant computational resources and lacking scalability across diverse reasoning tasks. To address these limitations, we propose VerifiAgent, a unified verification agent that integrates two levels of verification: meta-verification, which assesses completeness and consistency in model responses, and tool-based adaptive verification, where VerifiAgent autonomously selects appropriate verification tools based on the reasoning type, including mathematical, logical, or commonsense reasoning. This adaptive approach ensures both efficiency and robustness across different verification scenarios. Experimental results show that VerifiAgent outperforms baseline verification methods (e.g., deductive verifier, backward verifier) among all reasoning tasks. Additionally, it can further enhance reasoning accuracy by leveraging feedback from verification results. VerifiAgent can also be effectively applied to inference scaling, achieving better results with fewer generated samples and costs compared to existing process reward models in the mathematical reasoning domain.
Anthology ID:
2025.findings-emnlp.891
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2025
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
16410–16431
Language:
URL:
https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.891/
DOI:
10.18653/v1/2025.findings-emnlp.891
Bibkey:
Cite (ACL):
Jiuzhou Han, Wray Buntine, and Ehsan Shareghi. 2025. VerifiAgent: a Unified Verification Agent in Language Model Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 16410–16431, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
VerifiAgent: a Unified Verification Agent in Language Model Reasoning (Han et al., Findings 2025)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.891.pdf
Checklist:
 2025.findings-emnlp.891.checklist.pdf