Diffusion Multimodal Large Models (DMLLM) perform well on multimodal reasoning, but their fixed-length generation mechanism has long dragged down inference efficiency — the model must pad its output to a preset maximum length, and a large number of useless [EOS] positions are repeatedly denoised, causing serious redundancy. The Seer framework proposed by Zhao, Sun and Yan discovers a key phenomenon: in the first denoising step of a DMLLM, the sparsity of the MLP activations changes dramatically, and this is the position signal of the effective semantic boundary. Based on this, Seer uses a Signal-to-Noise Ratio (SNR) criterion to cut off all redundant suffixes at Step-0 with one cut, then skips this part of the computation at every subsequent denoising step, achieving a "zero retraining" speedup. In batch service, Seer pairs a mixed-execution strategy with dynamic sequence length, holding performance across 9 benchmarks and even slightly improving on some tasks (e.g., DocVQA 63.52→63.66) by suppressing noise leakage from [EOS] positions, with up to ~31× throughput speedup. As work accepted by ACM Multimedia 2026, it provides a drop-in DMLLM inference-acceleration solution.