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This paper presents the first systematic study of uncertainty quantification (UQ) for Large Language Diffusion Models (LLDMs), proposing lightweight zero-shot uncertainty signals derived from the iterative denoising process and showing that LLDMs can achieve both fast inference and reliable hallucination detection with up to 100x lower computational overhead compared to sampling-based baselines.