arXiv AI June 30, 2026 By Rahid Zahid Alekberli, Hikmat Karimov

Spectral Perturbation of the Empirical Fisher Information Matrix under Weight Quantization

arXiv:2606. 28432v1 Announce Type: cross Abstract: We study the spectral perturbation of the empirical Fisher Information Matrix (FIM) of a parametric statistical model under two structured perturbations: departure of the input from a reference (in-distribution) ensemble, and finite-precision (quantized) perturbation of the model's parameters.

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arXiv Machine Learning

Jul 14

Reliability Scaling Laws for Quantized Large Language Models

arXiv:2607. 10855v1 Announce Type: new Abstract: Quantization is a powerful strategy to build capable and resource-efficient large language models (LLMs) by reducing the bitwidth of the parameters.

By Sirine Ayadi, S\'andor Dar\'oczi, Stephan G\"unnemann, Bertrand Charpentier

llms efficiency benchmarks

arXiv AI

Jun 9

High-Rate Quantized Matrix Multiplication II

arXiv:2605. 13768v2 Announce Type: replace-cross Abstract: This is the second part of the work investigating quantized matrix multiplication (MatMul).

By Or Ordentlich, Yury Polyanskiy

llms efficiency

arXiv AI

Jun 8

FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models

arXiv:2606. 06547v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a "stability lag" where early decisions remain fragile even after being written.

By Haoyu Huang, Linlin Yang, Sheng Xu, Boyu Liu, Guodong Guo, Zhongqian Fu, Hang Zhou, Baochang Zhang

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jun 18

Fisher Width: A Geometric Measure of Complexity on Statistical Manifolds

arXiv:2606. 18306v1 Announce Type: new Abstract: Gaussian width is a central geometric complexity measure in high-dimensional probability, compressed sensing, convex optimization, and learning theory.

By Vu Khac Ky

arXiv AI

Jul 13

Signed Symmetric Quantization for Few-Bit Integers

arXiv:2607. 08779v1 Announce Type: cross Abstract: The signed integer alphabet contains one more negative representable value than positive.

By Ian Colbert, Eashan Dash, Pablo Monteagudo-Lago, Juan Amboage, Srinidhi N, Giuseppe Franco, Nicholas J. Fraser, Arun Ramachandran

llms efficiency

arXiv AI

Jul 7

Spectral Signatures of Large Language Models

arXiv:2607. 03377v1 Announce Type: cross Abstract: The rapidly growing repository of publicly available large language models (LLMs) presents significant challenges for systematic management and quantification at scale, such as model lineage tracing, licensing, and evaluation.

By Zhuoying Zhang, Ishan V. Prasad, Yuanzhe Hu, Zihang Liu, Hengrui Luo, Pu Ren, Yaoqing Yang

llms benchmarks

Related stories

Reliability Scaling Laws for Quantized Large Language Models

High-Rate Quantized Matrix Multiplication II

FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models

Fisher Width: A Geometric Measure of Complexity on Statistical Manifolds

Signed Symmetric Quantization for Few-Bit Integers

Spectral Signatures of Large Language Models