Twintinuum: Advancing Self-Calibrating Physical-Digital Continuum

Network Digital Twins (NDTs) are reshaping the network research landscape by enabling real-time predictive modeling and analysis of complex networked systems. Despite their potential, practical deployment of NDTs is hindered by growing divergence between the physical network and its digital replica, driven by insufficient or delayed synchronization, and the high communication overhead from continuous calibration, often worsened by inefficient data representation. To address these issues, we propose Twintinuum, a novel uncertainty-guided self-calibration mechanism that enables scalable twinning with low communication overhead. Our approach dynamically adapts the synchronization based on estimated model uncertainty, enabling both short-term responsiveness and long-term consistency. Moreover, the proposed framework supports plug-and-play calibration, allowing the digital twins to autonomously align with physical dynamics without extensive manual tuning or retraining. Extensive experiments on wireless signal datasets and diverse base models demonstrate that our method achieves superior signal quality estimation with significantly less data exchange.