@inproceedings{shen-etal-2026-improving,
    title = "Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration",
    author = "Shen, Jucheng  and
      Sarkar, Gaurav  and
      Ro, Yeonju  and
      Sridhar, Sharath Nittur  and
      Wang, Zhangyang  and
      Akella, Aditya  and
      Kundu, Souvik",
    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.478/",
    pages = "9826--9837",
    ISBN = "979-8-89176-395-1",
    abstract = "We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate on the dynamic nature of the token unmasking confidence across blocks and steps. Based on this observation, we then present a lightweight adaptive approach that can control the generation block size, step size, and threshold based on the average confidence score of the unmasked tokens. We further reduce the softmaxing overhead of token probability generation by dynamically leveraging a subset of vocabulary size to regulate sampling breadth. CadLLM is a plug-and-play model-agnostic with KV caching based dLLMs. Extensive experiments on four popular tasks demonstrate the efficacy of CadLLM to yield throughput improvement of up to 1.1-2.28x over the state-of-the-art baseline with competitive accuracy."
}