@inproceedings{feng-etal-2026-cosineverifier,
title = "{C}o{S}ine{V}erifier: Agentic Verification for Computation-Oriented Scientific Questions",
author = "Feng, Ruixiang and
An, Zhenwei and
Wen, Yuntao and
Le, Ran and
Jia, Yiming and
Yang, Chen and
Chen, Zongchao and
Shi, Ke and
Chen, Lisi and
Gao, Shen and
Shang, Shuo and
Song, Yang and
Zhang, Tao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1929/",
pages = "43903--43922",
ISBN = "979-8-89176-459-0",
abstract = "While prior work focuses on treating answer verification as a general reasoning or semantic matching problem, significant challenges remain in computation-oriented scientific domains. In this paper, we introduce CoSineVerifier, an agentic verifier that actively invokes external executors to ground its judgments in precise computations and symbolic reasoning. To train this accurate agentic verifier, we propose a novel verifier training data augmentation method to increase the correctness and efficiency of tool-invoked verifications. Extensive experiments across STEM, QA, and long-form reasoning tasks demonstrate CoSineVerifier{'}s robust generalization abilities, achieving state-of-the-art performance on VerifyBench-Hard and SCI-Bench. Furthermore, when employed as an RLVR reward model, CoSineVerifier consistently outperforms both rubric- and model-based verifiers on AIME{'}24, AIME{'}25 and GPQA-D, highlighting its potential to advance LLM reasoning and accelerate data processing"
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<abstract>While prior work focuses on treating answer verification as a general reasoning or semantic matching problem, significant challenges remain in computation-oriented scientific domains. In this paper, we introduce CoSineVerifier, an agentic verifier that actively invokes external executors to ground its judgments in precise computations and symbolic reasoning. To train this accurate agentic verifier, we propose a novel verifier training data augmentation method to increase the correctness and efficiency of tool-invoked verifications. Extensive experiments across STEM, QA, and long-form reasoning tasks demonstrate CoSineVerifier’s robust generalization abilities, achieving state-of-the-art performance on VerifyBench-Hard and SCI-Bench. Furthermore, when employed as an RLVR reward model, CoSineVerifier consistently outperforms both rubric- and model-based verifiers on AIME’24, AIME’25 and GPQA-D, highlighting its potential to advance LLM reasoning and accelerate data processing</abstract>
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%0 Conference Proceedings
%T CoSineVerifier: Agentic Verification for Computation-Oriented Scientific Questions
%A Feng, Ruixiang
%A An, Zhenwei
%A Wen, Yuntao
%A Le, Ran
%A Jia, Yiming
%A Yang, Chen
%A Chen, Zongchao
%A Shi, Ke
%A Chen, Lisi
%A Gao, Shen
%A Shang, Shuo
%A Song, Yang
%A Zhang, Tao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F feng-etal-2026-cosineverifier
%X While prior work focuses on treating answer verification as a general reasoning or semantic matching problem, significant challenges remain in computation-oriented scientific domains. In this paper, we introduce CoSineVerifier, an agentic verifier that actively invokes external executors to ground its judgments in precise computations and symbolic reasoning. To train this accurate agentic verifier, we propose a novel verifier training data augmentation method to increase the correctness and efficiency of tool-invoked verifications. Extensive experiments across STEM, QA, and long-form reasoning tasks demonstrate CoSineVerifier’s robust generalization abilities, achieving state-of-the-art performance on VerifyBench-Hard and SCI-Bench. Furthermore, when employed as an RLVR reward model, CoSineVerifier consistently outperforms both rubric- and model-based verifiers on AIME’24, AIME’25 and GPQA-D, highlighting its potential to advance LLM reasoning and accelerate data processing
%U https://aclanthology.org/2026.emnlp-main.1929/
%P 43903-43922
Markdown (Informal)
[CoSineVerifier: Agentic Verification for Computation-Oriented Scientific Questions](https://aclanthology.org/2026.emnlp-main.1929/) (Feng et al., EMNLP 2026)
ACL
- Ruixiang Feng, Zhenwei An, Yuntao Wen, Ran Le, Yiming Jia, Chen Yang, Zongchao Chen, Ke Shi, Lisi Chen, Shen Gao, Shuo Shang, Yang Song, and Tao Zhang. 2026. CoSineVerifier: Agentic Verification for Computation-Oriented Scientific Questions. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 43903–43922, Budapest, Hungary. Association for Computational Linguistics.