SCI-Verifier: Scientific Verifier with Thinking
Authors: name: Shenghe Zheng, name: Chenyu Huang, name: Fangchen Yu, name: Junchi Yao, name: Jingqi Ye, name: Tao Chen, name: Yun Luo, name: Ning Ding, name: LEI BAI, name: Ganqu Cui, name: Peng Ye

As large language models (LLMs) are increasingly applied to scientific
reasoning, the complexity of answer formats and the diversity of equivalent
expressions make answer verification a critical yet challenging task. Existing
verification studies in scientific domains suffer from two major limitations:
(a) the absence of systematic evaluation standards and insufficient
disciplinary coverage, which hinders their comprehensive assessment; and (b)
heavy reliance on cumbersome rule design or prompt engineering, which reduces
their effectiveness in complex reasoning scenarios or limits their
cross-disciplinary generalization. To address these challenges, we propose
solutions at both the data and model levels. On the data side, we construct
SCI-VerifyBench, a cross-disciplinary benchmark covering mathematics, physics,
biology, chemistry, and general scientific QA. The benchmark is built from real
LLM responses and enhanced with domain-specific equivalence transformations
that generate challenging and realistic data. Model-based and expert
annotations ensure both quality and diversity, enabling rigorous evaluation of
verification ability. On the model side, we emphasize the importance of
reasoning for verification and introduce SCI-Verifier, a unified
reasoning-augmented verifier for scientific domains. Through post-training,
SCI-Verifier demonstrates strong logical reasoning and equivalence judgment
capabilities while maintaining concise and stable outputs. Together,
SCI-VerifyBench and SCI-Verifier provide a principled framework for scientific
verification, offering both systematic evaluation and practical pathways to
enhance the reliability and applicability of LLMs in scientific domains.