Fixed-Point Masked Generative Modeling 事件

Fixed-Point Masked Generative Modeling arXiv:2605.31215v1 Announce Type: cross Abstract: Masked Generative Models (MGMs) enable parallel decoding and achieve strong performance across modalities, but require full-sequence bidirectional transformers at every step, making training costly and degrading quality under low sampling budgets. Existing work improves efficiency via better samplers or cheaper fixed-depth denoisers, but they still allocate a fixed amount of denoiser computation to each ref