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arXiv Computer Vision By Ruxi Gu, Zilei Wang, Wei Wang

Beyond Uniform Subspaces: Spectrum-Aware and Depth-Adaptive Fusion for Multi-Task Model Merging

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The paper introduces SADA-Merging, a new data‑free model merging framework that addresses limitations of existing subspace‑based methods. It allocates subspace capacity based on each task’s spectral complexity, adapts spectral preservation to task‑specific plasticity, and applies depth‑dependent anchoring to mitigate projection distortion. Experiments show that SADA‑Merging consistently outperforms current data‑free merging techniques across various task scales and adaptation settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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