Model Fusion via Retrofitting 事件

Model Fusion via Retrofitting arXiv:2507.00037v2 Announce Type: replace-cross Abstract: Model fusion seeks to combine independently trained neural networks into a single model without retraining, but is complicated by representational divergence arising from permutation invariance, random initialization, and heterogeneous training data. Existing methods struggle particularly in zero-shot settings under non-IID data distributions, and are often limited to specific architectures or pairwise fusio