NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning

ArXi:2604.27031v1 Announce Type: cross In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities. We argue that this dilemma has an architectural root. A finite network has limited representational and plastic resources, yet the required capacity depends on properties of the future task stream that are unknown: how many tasks will be encountered, and how much they overlap in feature space.