Model Degradation Behaviors in Continual Learning Lifecycles

Abstract

This article examines the mechanisms and manifestations of model degradation within continual
learning lifecycles, focusing on the progression of representational drift and catastrophic forgetting
during sequential model updates. A multi-layer analytical methodology was applied to observe how
internal neural representations, gradient interference patterns, and parameter importance distributions
evolve over time. The results demonstrate that degradation often begins in intermediate semantic layers
and can remain undetected at the performance level until later stages. Gradient conflict and task
dissimilarity were found to accelerate deterioration, whereas selective memory replay and dynamically
timed retraining mitigated these effects. The study concludes that continual learning stability requires
adaptive monitoring and intervention strategies to preserve performance integrity in evolving
operational environments.