@inproceedings{zhou-etal-2026-diffusion,
    title = "Diffusion with Truncated Blocks: Fast and High-Quality Text Generation using Truncated Block Generation",
    author = "Zhou, Yuyan  and
      Chen, Weiyu  and
      Kwok, James",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.212/",
    pages = "4335--4348",
    ISBN = "979-8-89176-395-1",
    abstract = "Diffusion-based Large Language Models (dLLMs) are emerging as a powerful alternative to traditional autoregressive models. These models learn to generate text by iteratively denoising masked sequences. In this work, we identify a critical problem in dLLMs: the model{'}s attention is wastefully expended on uninformative mask tokens, diluting its focus on meaningful context. We term this phenomenon ``attention dilution''. We further show that this artifact is amplified by token-level noising, whereas models employing sequence-level noise exhibit a reduced effect. To resolve this problem, we introduce Truncated Block Generation, a novel sampling algorithm that not only mitigates attention dilution but also enables faster inference and flexible-length sequence generation. Extensive experiments validate our analysis and demonstrate the marked effectiveness of our proposed method in enhancing both the performance and efficiency of dLLMs."
}