Predictive Assistance and the Temporal Dynamics of Exploratory Compression

The paper introduces a geometric dynamical framework for understanding predictive assistance in cognitive problem-solving, highlighting how predictive AI systems can stabilize decision trajectories before exploratory search occurs. Key findings include the reduction of exploratory responsiveness due to sustained predictive stabilization, asymmetrical curvature dynamics leading to hysteresis, and the critical impact of timing on developmental outcomes in exploration. This framework provides empirical predictions about exploratory entropy and convergence, offering insights into how predictive systems can alter cognitive exploration processes, which is relevant for AI practitioners designing adaptive learning systems.