@inproceedings{zou-etal-2026-uni,
    title = "Uni-{MMMU}: A Massive Multi-discipline Multimodal Unified Benchmark",
    author = "Zou, Kai  and
      Huang, Ziqi  and
      Dong, Yuhao  and
      Tian, Shulin  and
      Zheng, Dian  and
      Liu, Hongbo  and
      He, Jingwen  and
      Liu, Bin  and
      Qiao, Yu  and
      Liu, Ziwei",
    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.40/",
    pages = "908--924",
    ISBN = "979-8-89176-390-6",
    abstract = "Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. Existing evaluations either treat the two abilities in isolation or overlook tasks that inherently couple them. To address this gap, we present \textbf{Uni-MMMU}, a comprehensive and discipline-aware benchmark that systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains, including science, coding, mathematics, and puzzles. Each task is \textit{bidirectionally coupled}, demanding models to (i) leverage conceptual understanding to guide precise visual synthesis, or (ii) utilize generation as a cognitive scaffold for analytical reasoning. Uni-MMMU incorporates verifiable intermediate reasoning steps, unique ground truths, and a reproducible scoring protocol for both textual and visual outputs. Through extensive evaluation of state-of-the-art unified, generation-only, and understanding-only models, we reveal substantial performance disparities and cross-modal dependencies, offering new insights into \textit{when and how} these abilities reinforce one another, and establishing a reliable foundation for advancing unified models."
}