Generalized Incremental Learning under Concept Drift across Evolving Data Streams

ArXi:2506.05736v2 Announce Type: replace-cross Real-world data streams exhibit inherent non-stationarity characterized by concept drift, posing significant challenges for adaptive learning systems. While existing methods address isolated distribution shifts, they overlook the critical co-evolution of label spaces and distributions under limited supervision and persistent uncertainty.