Preprint
Aug 2026
Escaping Low-Dimensional Overlap: Multi-Task Model Merging via High-Dimensional Sparse Disentanglement
A sparse-representation-based merging framework that uses Sparse Autoencoders (SAEs) to project task vectors into a high-dimensional sparse feature space, enabling feature-level disentanglement before fusion is proposed.
Yi-Hang Zhang, Shengen Sun, Junbo Wen et al.
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