arXiv:2605.08772v2 Announce Type: replace Abstract: Wireless digital twins (WDTs) enable site-specific learning, management, and evaluation, but constructing and maintaining uniformly high-fidelity WDTs for large-scale urban environments is costly. This paper studies task-oriented nonuniform WDT refinement (TONR) by addressing the following question: given a limited sensing and reconstruction budget, which building geometries in the initial WDT should be refined to best preserve wireless fidelity? A resource-constrained building selection problem is formulated to minimize the expected discrepancy between the wireless response of the refined WDT and the physical environment. Through local first-order approximation, the refinement value of each building is shown to depend jointly on its correctable geometry uncertainty and the sensitivity of the task response to that uncertainty. This value is then estimated solely from the initial low-fidelity WDT using physically interpretable geometry perturbations and central finite differences. To reduce the computational cost, a propagation-relevance ellipsoid is proposed to filter out buildings unlikely to contribute significantly to the wireless propagation. The resulting knapsack problem is solved under both equal and heterogeneous refinement costs. Simulations across multiple urban scenarios show that the proposed algorithm can substantially improve wireless fidelity by refining only a small subset of buildings.