Qicheng Zhao

Seeing the End at Step Zero: Accelerating Diffusion MLLMs via MLP Sparsity-Aware Truncation

Seer is proposed, a training-free framework that detects their valid semantic boundary using a Signal-to-Noise Ratio (SNR)-based criterion and performs one-shot truncation of the redundant suffix for all subsequent computations, offering a highly efficient, plug-and-play solution for DMLLM acceleration.