arXiv:2606. 04405v1 Announce Type: cross Abstract: Modern Transformer architectures frequently employ normalization mechanisms such as RMSNorm and Query-Key Normalization, making parts of the model approximately scale-invariant with respect to weight magnitudes.
By Mingyu Li
arXiv:2606. 04028v1 Announce Type: new Abstract: The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, with a focus on facilitating machine learning.
By Andrew Fitzgibbon, Christoph M. Wintersteiger, Jeffrey Sarnoff
arXiv:2610. 01889v1 Announce Type: new Abstract: Should low-precision transformer inference use stochastic rounding (SR) or round-to-nearest (RN)?
By Yohan Chatelain (Krembil Centre for Neuroinformatics, CAMH, Toronto, Canada), Pablo de Oliveira Castro (Universite Paris-Saclay, UVSQ, LI-PaRAD, Versailles, France)
The paper presents a second‑order theory of quantization noise for matrix multiplication, characterizing quantization formats by the variance they assign to each element. It derives a closed‑form signal‑to‑noise‑ratio law for floating‑point rounding, introduces an upper bound κ* that cannot be exceeded by any function‑preserving linear transform, and proposes KBBQ—a method that parameterizes how closely a transform approaches this bound. Experiments on W4A4 across four base models and two FP4 formats show that KBBQ outperforms the previous state of the art without extra deployment‑time computation.
By Lexington Whalen, Yuki Ito, Ryo Sakamoto
arXiv:2606. 19411v3 Announce Type: replace Abstract: Selecting a fixed-size subset that maximizes the determinant of a positive semidefinite kernel is the MAP problem for a size-constrained determinantal point process and the classical maximum-entropy sampling problem.
By Richard Yi Da Xu
arXiv:2505. 22988v3 Announce Type: replace-cross Abstract: The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible.
By Albert Tseng, Zhaofeng Sun, Christopher De Sa