SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915657155608576 |
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| author | Imani, Shima Moon, Seungwhan Ahmadyan, Adel Zhang, Lu Ahmed, Kirmani Damavandi, Babak |
| author_facet | Imani, Shima Moon, Seungwhan Ahmadyan, Adel Zhang, Lu Ahmed, Kirmani Damavandi, Babak |
| contents | We introduce, a large-scale synthetic benchmark of 15,045 university-level physics problems (90/10% train/test split). Each problem is fully parameterized, supporting an effectively infinite range of input configurations, and is accompanied by structured, step-by-step reasoning and executable Python code that produces the ground-truth solution for any parameter set. The benchmark contains three question types: MC-Symbolic (multiple-choice with symbolic options), MC-Numerical (multiple-choice with numerical options), and free-form (open-ended responses). These diverse formats test complementary reasoning skills. By leveraging the dynamic, code-driven nature of the benchmark, we introduce three novel evaluation metrics in addition to standard accuracy: Consistency Score, Failure Rate, and Confusion Rate, that quantify variability and uncertainty across problem variants. Experiments with state-of-the-art instruction-tuned language models reveal both strengths and limitations in scientific reasoning, positioning SymPyBench as a foundation for developing more robust and interpretable reasoning systems |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05954 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code Imani, Shima Moon, Seungwhan Ahmadyan, Adel Zhang, Lu Ahmed, Kirmani Damavandi, Babak Artificial Intelligence We introduce, a large-scale synthetic benchmark of 15,045 university-level physics problems (90/10% train/test split). Each problem is fully parameterized, supporting an effectively infinite range of input configurations, and is accompanied by structured, step-by-step reasoning and executable Python code that produces the ground-truth solution for any parameter set. The benchmark contains three question types: MC-Symbolic (multiple-choice with symbolic options), MC-Numerical (multiple-choice with numerical options), and free-form (open-ended responses). These diverse formats test complementary reasoning skills. By leveraging the dynamic, code-driven nature of the benchmark, we introduce three novel evaluation metrics in addition to standard accuracy: Consistency Score, Failure Rate, and Confusion Rate, that quantify variability and uncertainty across problem variants. Experiments with state-of-the-art instruction-tuned language models reveal both strengths and limitations in scientific reasoning, positioning SymPyBench as a foundation for developing more robust and interpretable reasoning systems |
| title | SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.05954 |