Modular Delta Merging with Orthogonal Constraints: A Scalable Framework for Continual and Reversible Model Composition
arXiv:2507.20997v4 Announce Type: replace
Abstract: In real-world machine learning deployments, models must be continually updated, composed, and when required, selectively undone. However, existing approaches to model merging and continual learning o…