InstantWAM: Instant World Action Model via Asymmetric One-Step Decoding

World Action Models (WAMs) regenerate every action plan from noise, even though much of the next plan was already predicted at the previous replan. This symmetry wastes computation and ignores the temporal structure of closed-loop control. We introduce InstantWAM, built on a simple asymmetry: generate the first plan, then update the future already in motion. InstantWAM carries the unexecuted suffix into the next planning window as a persistent generative state, perturbs it back onto the pretrained flow, and corrects it with a single observation-conditioned evaluation. The WAM's own action attention determines how long the inherited future remains useful. Repeated multi-step generation thereby becomes one-step decoding without training or changing the frozen model. InstantWAM reduces average network function evaluations from 10 to 1.62, delivering $3.19\times$ faster planner calls and $2.47\times$ faster episode-level planning. On RoboTwin, it achieves $91.27\%$ success versus $91.83\%$ for 10-step FastWAM; on LIBERO, it reaches $95.75\%$. The future should not be discarded at every replan, but carried forward and updated.