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Author

Mads Toftrup

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#artificial intelligence Preprint Oct 2026

A Unified Hessian-Guided Framework for Post-Training Quantization

Low-bit quantization enables efficient deployment of large language models by reducing memory footprint and computational cost. Achieving high accuracy at low precision requires selecting quantization ranges, linear transformations, and weight rounding decisions. Existing approaches typically optimize these components...

M. Toftrup, Marco Federici, Boris van Breugel et al. · 0 citations
#artificial intelligence Preprint Aug 2026

SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning

Method is introduced, which augments SOAP-style preconditioning with a scalar secant-energy correction adapted to Kronecker geometry and an adaptive basis update followed by variance-state downscaling, and is positioned as a scalable option for stiff, high-accuracy physics-informed training, rather than a uniform repla...

Guang-Yuan Wang, Mads Toftrup, Sebastian Loeschcke et al. · 0 citations

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