According to @akshatgupta57, a major revision to their paper on Lifelong Knowledge Editing highlights that better regularization techniques are essential for maintaining consistent downstream performance in AI models. The research, conducted with collaborators from Berkeley AI, demonstrates that addressing regularization challenges directly improves the ability of models to edit and update knowledge without degrading previously learned information, which is critical for scalable, real-world AI deployments and continual learning systems (source: @akshatgupta57 on Twitter, May 23, 2025).