INTEGRALBENCH: Benchmarking LLMs with Definite Integral Problems

Bintao Tang; Xin Yang; Yuhao Wang; Zixuan Qiu; Zimo Ji; Wenyuan Jiang

INTEGRALBENCH: Benchmarking LLMs with Definite Integral Problems

Artificial Intelligence 2025-07-30 v1

Authors: Bintao Tang , Xin Yang , Yuhao Wang , Zixuan Qiu , Zimo Ji , Wenyuan Jiang

Abstract

We present INTEGRALBENCH, a focused benchmark designed to evaluate Large Language Model (LLM) performance on definite integral problems. INTEGRALBENCH provides both symbolic and numerical ground truth solutions with manual difficulty annotations. Our evaluation of nine state-of-the-art LLMs reveals significant performance gaps and strong correlations between problem difficulty and model accuracy, establishing baseline metrics for this challenging domain. INTEGRALBENCH aims to advance automated mathematical reasoning by providing a rigorous evaluation framework specifically tailored for definite integral computation.

Keywords

large language model evaluation large language model benchmark evaluation

Cite

@article{arxiv.2507.21130,
  title  = {INTEGRALBENCH: Benchmarking LLMs with Definite Integral Problems},
  author = {Bintao Tang and Xin Yang and Yuhao Wang and Zixuan Qiu and Zimo Ji and Wenyuan Jiang},
  journal= {arXiv preprint arXiv:2507.21130},
  year   = {2025}
}

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19 pages, 5 figures

INTEGRALBENCH: Benchmarking LLMs with Definite Integral Problems

Abstract

Keywords

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