Uncertainty-Aware Continual Learning for Open-World Intent Discovery Under an evolving Label Space

Real-world intelligent systems increasingly operate under open-world conditions, where user intents are not fixed or exhaustively known a priori and may evolve as new interaction patterns emerge. This paper proposes a unified uncertainty-aware probabilistic framework for continual new intent discove