Unmasking-Time Visual Calibration for Hallucination Mitigation in Multimodal Discrete Diffusion Language Models

Despite the recent success of multimodal discrete diffusionlanguage models (multimodal dLLMs) in generating text through iter-ative demasking with bidirectional attention, object hallucination re-mains a critical challenge. Existing hallucination mitigation methods aredesigned for autoregressive generation and encounter fundamental mis-matches when applied to multimodal dLLMs, either doubling inferencecost through contrastive decoding or suffering from ambiguous step andposition alignment under bidirectional context. To address this, we pro-pose Unmasking-Time Visual Calibration (UVC), a lightweight, training-free framework that mitigates hallucinations natively within the demask-ing process. UVC extracts multi-granularity contrastive activation shiftvectors offline by comparing model responses to clean and visually de-graded inputs, identifies the most visually informative attention headsvia per-head AUC scoring, and injects the pre-computed calibration sig-nals exclusively at still-masked positions during inference, requiring noadditional forward passes. Extensive experiments on POPE and MMEwith two representative multimodal dLLMs demonstrate that UVC con-sistently reduces hallucinations by a substantial margin while introducingless than one percent inference overhead. Additional evaluation on open-ended captioning with the CHAIR metric further confirms that UVC sub-stantially lowers hallucination rates without affecting generation length,and generalizes effectively across benchmarks without retraining. Codeis available at https://github.com/THU-BPM/UVC.