Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models
Diffusion-based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel. This turns each decoding step into a position-selection problem: the model must choose not only which predictions are reliable in isolation, but also which positions should be committed together as context for later decoding steps. Existing confidence-based decoding ranks masked positions independently and commits the top-K positions, largely ignoring whether the committed tokens provide complementary visual grounding. We identify a step-level limitation of this strategy in multimodal settings: high-confidence tokens selected in the same step can rely...
| Authors | Yulin Yuan, Hongshuo Zhao, Xiangming Meng |
|---|---|
| Primary category | cs.LG |
| Published | 25 May 2026 |
| Last updated at source | 10 June 2026 |
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