Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains

ArXi:2604.17089v1 Announce Type: new Continual learning aims to update models under distribution shift without forgetting, yet many high-stakes deployments, such as healthcare, also require interpretability. In practice, models that adapt well (e.g., deep networks) are often opaque, while models that are interpretable (e.g., decision trees) are brittle under shift, making it difficult to achieve both properties simultaneously.