arXiv:2605.19409v1 Announce Type: cross Abstract: Continual Model Merging (CMM) enables rapid customization of foundation models across sequentially arriving tasks, offering a scalable alternative to repeated retraining. However, existing merging rules lack explicit controllability over the allocation of learning capacity between previously learned capabilities and newly merged models. Consequently, as tasks are merged sequentially, this deficiency accumulates into severe forgetting, particularly in scenarios with heterogeneous task importance, where performance allocation becomes highly incon
Source: arXiv cs.AI — read the full report at the original publisher.