Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models

ArXi:2605.27975v1 Announce Type: new Generative models, including diffusion models, are increasingly used as foundation models and adapted through sequential fine-tuning, making continual learning an essential problem setting. However, continual learning in such generative models remains poorly understood: after a task change, what aspects of the learned distribution are most easily lost, and what replay samples should be prioritized? We address these questions through the modern Hopfield energy.