@inproceedings{dai-etal-2026-musical,
    title = "Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores",
    author = "Dai, Congren  and
      Yang, Yue  and
      Li, Krinos  and
      Zhou, Huichi  and
      Liang, Shijie  and
      Bo, Zhang  and
      Liu, Enyang  and
      Jin, Ge  and
      An, Hongran  and
      Zhang, Haosen  and
      Jing, Peiyuan  and
      Lee, KinHei  and
      Zhang, Zhenxuan  and
      Li, Xiaobing  and
      Sun, Maosong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.493/",
    pages = "10777--10799",
    ISBN = "979-8-89176-390-6",
    abstract = "Understanding complete musical scores entails integrated reasoning over pitch, rhythm, harmony, and large-scale structure, yet the ability of Large Language Models and Vision{--}Language Models to interpret full musical notation remains insufficiently examined.We introduce Musical Score Understanding Benchmark (MSU-Bench), a human-curated benchmark for score-level musical understanding across textual (ABC notation) and visual (PDF) modalities. MSU-Bench contains 1,800 generative question{--}answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others, organised into four levels of increasing difficulty, ranging from onset information to texture and form. Evaluations of more than fifteen state-of-the-art models, in both zero-shot and fine-tuned settings, reveal pronounced modality gaps, unstable level-wise performance, and challenges in maintaining multilevel correctness. Fine-tuning substantially improves results across modalities while preserving general knowledge, positioning MSU-Bench as a robust foundation for future research in multimodal reasoning. The benchmark and code are available at https://github.com/Congren-Dai/MSU-Bench."
}