@inproceedings{li-etal-2026-confidence,
    title = "The Confidence Paradox: Unveiling the Latent Discriminative Power of Diffusion Large Language Models in Mathematical Reasoning",
    author = "Li, Yansi  and
      Liu, Gongshen  and
      Zhang, Zhuosheng",
    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.2142/",
    pages = "43179--43196",
    ISBN = "979-8-89176-395-1",
    abstract = "Diffusion large language models (DLLMs) have emerged as a promising alternative to autoregressive (AR) generation, uniquely offering token-level probabilities under bidirectional context. However, the semantics of their native uncertainty estimates remain underexplored. In this work, we uncover a calibration paradox inherent to the bidirectional generation mechanism of state-of-the-art DLLMs. Concretely, we demonstrate that diffusion confidence is structurally distinct from AR likelihood. Notably, LLaDA-8B is highly miscalibrated (31.2{\%} ECE) on mathematical reasoning benchmarks, yet possesses superior discriminative power (0.826 AUROC), significantly outperforming comparable AR baselines in single-pass settings (0.611 AUROC). We diagnose that this paradox arises because diffusion confidence functions less like a probability of correctness and more like a proxy for structural consistency enabled by the model{'}s bidirectional access to the entire solution path. We further show that lightweight post-hoc calibration can reconcile this gap, reducing ECE by over 60{\%} while preserving the strong ranking signal. Our findings suggest that DLLMs offer a unique, cost-efficient uncertainty signal for reasoning tasks that complements expensive AR approaches."
}