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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.21130 |
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| _version_ | 1866908470234578944 |
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| author | Tang, Bintao Yang, Xin Wang, Yuhao Qiu, Zixuan Ji, Zimo Jiang, Wenyuan |
| author_facet | Tang, Bintao Yang, Xin Wang, Yuhao Qiu, Zixuan Ji, Zimo Jiang, Wenyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21130 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | INTEGRALBENCH: Benchmarking LLMs with Definite Integral Problems Tang, Bintao Yang, Xin Wang, Yuhao Qiu, Zixuan Ji, Zimo Jiang, Wenyuan Artificial Intelligence 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. |
| title | INTEGRALBENCH: Benchmarking LLMs with Definite Integral Problems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.21130 |