# NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning Source: [NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning]() Collection index: [llms.txt]() Data updated: 2026-07-18T05:00:05.624Z Authors: Karthik Charan Raghunathan, Christian Metzner, Laura Kriener, Melika Payvand Published: 2026-04-29T15:04:25.000Z ## Abstract 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. Regularization-based methods preserve past knowledge within fixed-capacity architectures and therefore implicitly rely on an oracle architecture sized for this unknown future. When tasks are only weakly related, fixed architectures progressively run out of plastic resources; when tasks are few or strongly overlapping, models are often over-provisioned. Inspired by neurogenesis in biology, we propose NORACL to address the stability-plasticity dilemma by tackling the oracle architecture problem through neuronal growth. Starting from a compact network, NORACL grows only when needed by monitoring two complementary signals for representational and plasticity saturation. We evaluate NORACL against oracle-sized static baselines across varying task counts and geometries. Across all settings, NORACL achieves final average accuracies that are better than or on par with oracle-provisioned static baselines while using fewer parameters. Additionally, NORACL yields architectures with interpretable growth, i.e. dissimilar tasks predominantly expand feature-extraction layers, whereas tasks which rely on common features shift growth toward later feature-combination layers. Our analysis further explains why fixed-capacity networks lose plasticity as tasks accumulate, whereas NORACL creates fresh capacity for new tasks through growth. Together, these results show that adaptive neurogenesis pushes the stability-plasticity Pareto frontier of continual learning. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on Permuted MNIST](): Image Classification; Permuted MNIST; Accuracy: 79.9. - [Image Classification on MNIST Binary Split](): Image Classification; MNIST Binary Split; Accuracy: 0.739. - [Image Classification on MNIST Rotated](): Image Classification; MNIST Rotated; Accuracy (%): 74.9. ## Original sources and implementation [Original paper]() [PDF]()