On the factory floor, where milliseconds determine whether a production line halts or flows, researchers have long trusted digital twins to guide the invisible choreography of computation — deciding what a sensor processes locally and what it sends elsewhere. Yet those twins were built on a quiet fiction: that the virtual perfectly mirrors the real. A new framework published in Nature dismantles that fiction, treating the gap between model and machine not as noise to be dismissed but as the very thing worth measuring, and in doing so achieves a 20.5 percent reduction in task completion latency