@inproceedings{imani-etal-2026-sympybench,
title = "{S}ym{P}y{B}ench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code",
author = "Imani, Shima and
Moon, Seungwhan and
Ahmadyan, Adel and
Zhang, Lu and
Kirmani, Ahmed and
Damavandi, Babak",
editor = {Matusevych, Yevgen and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en and
Aletras, Nikolaos},
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-industry.8/",
pages = "105--118",
ISBN = "979-8-89176-384-5",
abstract = "We introduce SymPyBench, a large-scale synthetic benchmark of 15K 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. In addition to standard accuracy, we introduce three new metrics: 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."
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%0 Conference Proceedings
%T SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code
%A Imani, Shima
%A Moon, Seungwhan
%A Ahmadyan, Adel
%A Zhang, Lu
%A Kirmani, Ahmed
%A Damavandi, Babak
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F imani-etal-2026-sympybench
%X We introduce SymPyBench, a large-scale synthetic benchmark of 15K 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. In addition to standard accuracy, we introduce three new metrics: 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.
%U https://aclanthology.org/2026.eacl-industry.8/
%P 105-118
Markdown (Informal)
[SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code](https://aclanthology.org/2026.eacl-industry.8/) (Imani et al., EACL 2026)
ACL