@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://preview.aclanthology.org/ingest-eacl/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."
}