arXiv:2602.03846v2 Announce Type: replace-cross Abstract: We develop a continual learning method for pretrained models that \emph{requires no access to old-task data}, addressing a practical barrier in foundation model adaptation where pretraining distributions are often unavailable. Our key observation is that pretrained networks exhibit substantial \emph{geometric redundancy}, and that this redundancy can be exploited in two complementary ways. First, redundant neurons provide a proxy for dominant pretraining-era feature directions, enabling the construction of approximately protected update
This research addresses a critical limitation in foundation model adaptation, which is becoming increasingly relevant as large pre-trained models proliferate across industries.
It enables continuous learning in large AI models without requiring access to sensitive or legacy data, significantly reducing data management burdens and improving privacy.
Foundation models can be more efficiently and continually updated, making them more adaptable to new tasks and changes in data distributions without catastrophic forgetting.
- · AI model developers
- · Enterprises deploying AI
- · Privacy-sensitive sectors (e.g., healthcare, finance)
- · Companies reliant on frequent full model retraining
- · Legacy AI adaptation methods
More robust and adaptable AI systems that continually learn and improve in situ.
Accelerated deployment and adoption of foundation models in new and evolving domains due to easier adaptation.
Enhanced AI 'agility' reducing the lifecycle cost and accelerating the pace of innovation for AI-powered products and services.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI