Author

Paul N. Whatmough

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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

#machine learning Preprint Oct 2026

SQuAT: Spherical Lattice Vector Quantization-Aware Training

Vector quantization (VQ), in particular lattice-based methods, offer strong rate-distortion trade-offs for low-bit large language models (LLMs), yet their behavior under quantization-aware training (VQ-QAT) remains poorly understood. We scale lattice VQ-QAT to billion-parameter LLMs and multi-billion-token training reg...

Sebastian Loeschcke, Ivan Koryakovskiy, T. V. D. van der Ouderaa et al. · 0 citations