Hierarchical and cascading cooperative rearrangement regions in dense colloidal suspensions.
Cooperative dynamics in soft materials such as colloidal suspensions, gels, and polymers stem from complex surface interactions and structural heterogeneities, driving behaviors such as yielding, failure, and avalanches. In X-ray photon correlation spectroscopy (XPCS), these dynamics manifest as localized decorrelation bursts in the two-time intensity correlation function, whose physical significance has remained difficult to quantify with traditional models that assume uniform and random motion. Here, we develop a deep-learning framework that detects and tracks such bursts as individual dynamical events. Combining theoretical validation with XPCS measurements of dense colloidal suspensions, we show that cooperative rearrangement regions (CRRs) are organized into temporally correlated hierarchies and cascades, with event durations and recurrence patterns that depart from homogeneous relaxation models. The results reveal a multiscale pathway for structural relaxation in dense colloids and demonstrate that intermittent features in XPCS encode physically meaningful cooperative dynamics. Our approach provides a route to quantifying avalanche-like and heterogeneous relaxation in soft, glassy, and disordered materials. We introduce an AI-powered framework that interprets intermittent bursts in two-time correlation functions as spatiotemporal "objects". Leveraging deep learning, it detects and tracks CRRs, extracting their occurrence times and durations to support detailed analysis. Validated through theory and experiment, this approach reveals the hierarchical and cascading nature of CRRs, offering new insights into relaxation dynamics in dense colloidal suspensions. It provides a robust tool for investigating complex behaviors in disordered systems.