The work establishes margin-verified certificates for physical alignment of residual Jacobian chains by bounding truncation errors and decomposing the Physical Alignment Matrix orthogonally under fitted effective-rank windows.
Proceedings of the IEEE International Conference on Computer Vision (ICCV) , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
VLA-GSE uses spectral decomposition of the VLA backbone to create generalized and specialized experts, enabling effective robot task adaptation while updating only 2.51% of parameters and achieving 81.2% zero-shot success on LIBERO-Plus.
citing papers explorer
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Geometric and Spectral Alignment for Deep Neural Network II
The work establishes margin-verified certificates for physical alignment of residual Jacobian chains by bounding truncation errors and decomposing the Physical Alignment Matrix orthogonally under fitted effective-rank windows.
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VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
VLA-GSE uses spectral decomposition of the VLA backbone to create generalized and specialized experts, enabling effective robot task adaptation while updating only 2.51% of parameters and achieving 81.2% zero-shot success on LIBERO-Plus.