@inproceedings{huang-etal-2026-empirical,
    title = "Empirical Analysis of Decoding Biases in Masked Diffusion Models",
    author = "Huang, Pengcheng  and
      Liu, Tianming  and
      Liu, Zhenghao  and
      Yan, Yukun  and
      Wang, Shuo  and
      Xiao, Tong  and
      Chen, Zulong  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.311/",
    pages = "6853--6876",
    ISBN = "979-8-89176-390-6",
    abstract = "Masked Diffusion Models (MDMs) have recently emerged as a promising non-autoregressive paradigm for sequence generation. However, their performance is highly sensitive to the choice of decoding strategy. In this work, we reveal that prevalent uncertainty-based decoding strategies induce two decoding biases in MDMs: rigid boundary bias and trivial token bias. These biases limit the model{'}s reasoning ability and ultimately degrade generation quality. To address these challenges, we propose UNmasking Calibration for DecOding DEbiasing (UNCODE), a decoding calibration framework that regularizes uncertainty-based decoding by incorporating two complementary priors to shape global decoding trajectories and promote content informativeness. Extensive experiments on three advanced MDMs across seven reasoning- and planning-intensive benchmarks demonstrate that UNCODE consistently outperforms existing decoding strategies by more than 7{\%}, while achieving performance comparable to autoregressive models of similar parameter scales. Our code will be made publicly available on GitHub."
}