Neural Network Quantization

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A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work
Apr 20, 2026Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson +3Residual VQ Vector Quantization
UCL and Broadcom · VMware Research by Broadcom · North Carolina State University +2
LoRaQ: Optimized Low Rank Approximation for 4-bit Quantization
Apr 20, 2026Yann Bouquet, Alireza Khodamoradi, Sophie Yáng Shen +2Mixed-Precision Quantization Diffusion Transformer
Research and Advancement Development Team, Advanced Micro Devices, Inc, USA · Computer Vision Laboratory, Ecole Polytechnique F´ed´erale de Lausanne, Lausanne, Switzerland.
DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization
Apr 20, 2026Haokun Lin, Xinle Jia, Haobo Xu +7LLM Quantization 4-Bit Quantization
CASIA · CityU · NJU +3
Evolutionary fine tuning of quantized convolution-based deep learning models
Apr 19, 2026Marcin PietrońFine-Tuning Convolutional Neural Networks
AGH University Kraków, Poland
When Flat Minima Fail: Characterizing INT4 Quantization Collapse After FP32 Convergence
Apr 16, 2026Marcus ArmstrongLLM Quantization 4-Bit Quantization
Department of Computer Science University of Houston Houston, TX 77004
A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits
Apr 16, 2026Nekane Fernandez, Ivan Valdes, Steven Van Vaerenbergh +2Efficient Inference Edge Inference
Nekane Fernandez, Ivan Valdes, Idoia de la Iglesia and Julen Arratibel are with the Dept. of Connected and Distributed Intelligence, Ikerlan Technology Research Centre, Arrasate, Spain · Nekane Fernandez and Steven Van Vaerenbergh are with the Dept. of Mathematics, Statistics and Computation, Universidad de Cantabria, Santander, Spain
Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate
Apr 15, 2026Jaemin Kim, Sungkyun Kim, Junyeol Lee +1LLM Quantization Language Model Calibration
Seoul National University · Hanyang University
From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips
Mar 24, 2026Alan T. L. Bacellar, Sathvik Chemudupati, Shashank Nag +4Neural Network Robustness Neural Network Quantization
The University of Texas at Austin · Federal University of Rio de Janeiro · Instituto de Telecomunicações, Porto, Portugal
Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling
Mar 18, 2026Sooyoung Ryu, Mathieu Salzmann, Saqib JavedDiffusion Model Quantization Diffusion Model Sampling
Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea · School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland · Meta Reality Labs, Redmond, USA
DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models
Mar 9, 2026Zihao Zheng, Hangyu Cao, Chibang Tao +9VLM Quantization Efficient VLA Model Inference
School of Computer Science, Peking University, Beijing, China. · School of Software Engineering, South China University of Technology, Guangzhou, China. · School of Artificial Intelligence, Beijing Normal University, Beijing, China. +1
Dissecting Quantization Error: A Concentration-Alignment Perspective
Mar 4, 2026Marco Federici, Boris van Breugel, Paul Whatmough +1LLM Quantization 4-Bit Quantization
Qualcomm AI Research
Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Accelerators
Feb 26, 2026Yuhao Liu, Salim Ullah, Akash KumarMixed-Precision Quantization FPGA-Based Neural Network Acceleration
Dresden University of Technology, Germany · Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden/Leipzig), Germany · Ruhr University Bochum, Germany
DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce
Feb 9, 2026Wenchen Han, Shay Vargaftik, Michael Mitzenmacher +1Communication-Efficient Distributed Training High-Performance Computing
University College London · VMware Research by Broadcom · Harvard University +1
Investigating Social Bias Changes in Quantized Language Models
Feb 5, 2026Stanley Z. Hua, Sanae Lotfi, Irene Y. ChenLLM Quantization Social Bias in Language Models
UC Berkeley & UCSF · Meta Superintelligence Labs
MatGPTQ: Efficient and Accurate Inference over Nested Quantized Models
Feb 3, 2026Maximilian Kleinegger, Elvir Crnčević, Dan AlistarhLLM Quantization LLM Inference Acceleration
1Vienna University of Technology, Vienna, Austria · Institute of Science & Technology Austria (ISTA), Vienna, Austria · 3Red Hat AI, Boston, USA
Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection
Jan 17, 2026Muhammad Alif Al Hakim, Alfan Farizki Wicaksono, Fajri KotoLLM Quantization Multilingual Language Model Evaluation
Faculty of Computer Science, Universitas Indonesia · Mohamed bin Zayed University of Artificial Intelligence
ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs
Jan 12, 2026Haoqian Meng, Yilun Luo, Yafei Zhao +3LLM Quantization 4-Bit Quantization
School of Computer Science and Technology, Tianjin University
QuantKAN: A Unified Quantization Framework for Kolmogorov Arnold Networks
Nov 24, 2025Kazi Ahmed Asif Fuad, Lizhong ChenQuantization-Aware Training Kolmogorov-Arnold Networks
Department of EECS · Oregon State University · Corvallis, OR 97331
You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations
Nov 9, 2025Amit LeVi, Raz Lapid, Rom Himelstein +3Mixed-Precision Quantization LLM Quantization
1Technion—Israel Institute of Technology · 2Intuit · 3Ben-Gurion University of the Negev +1
Activation Quantization of Vision Encoders Needs Prefixing Registers
Oct 6, 2025Seunghyeon Kim, Taesun Yeom, Jinho Kim +3Activation Outliers Activation Quantization
POSTECH · NYU Langone Health2 · Google3
QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception
Sep 3, 2025Seth Z. Zhao, Huizhi Zhang, Zhaowei Li +11V2X Communication Cooperative Perception
UCLA · UW-Madison · NCSU +3
SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
Aug 5, 2025Yufei Xue, Yushi Huang, Lunjie Zhu +2Vision-Language Models VLM Quantization
Institute of Artificial Intelligence (TeleAI), China Telecom · The Hong Kong University of Science and Technology
Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method
Jul 24, 2025Qingcheng Zhu, Yangyang Ren, Linlin Yang +6Mixed-Precision Quantization LLM Quantization
Beihang University · Communication University of China · Beijing Jiaotong University
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
Jun 13, 2025Guang Liang, Xinyao Liu, Jianxin WuQuantization-Aware Training 4-Bit Quantization
State Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · University of Science and Technology of China, Hefei, China
NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache
May 23, 2025Donghyun Son, Euntae Choi, Sungjoo YooKV Caching Vector Quantization
Seoul National University
Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
Date pendingKexin Chu, Dawei Xiang, Zixu Shen +3Mixed-Precision Quantization GPU Acceleration
University of Connecticut · University of California, Santa Cruz · Independent Researcher
Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models
Date pendingArthur Negrão, Pedro Silva, Vander L. S. Freitas +2LLM Quantization 4-Bit Quantization
Postgraduate Program in Computer Science Federal University of Ouro Preto · Computing Department Federal University of Ouro Preto
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
Date pendingHaoyu Wang, Xingyu Yu, Haiyan Zhao +2LLM Quantization Quantization-Aware Training
Department of Computer Science and Technology, Tsinghua University, Beijing, China · School of Software and Microelectronics, Peking University, Beijing, China · College of Computer Science and Technology, National University of Defense Technology, Hunan, China.
RDQ: Residual Distribution Quantization for Large Language Models
Date pendingPrateek SinghLLM Quantization Post-Training Quantization
REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent
Date pendingQian Zhang, Yaoming Li, Zhewen Tan +9LLM Quantization Post-Training Quantization
Peking University · Northeastern University · ZTE Corporation

Apr 20, 2026, cs.LGJ/K move · Enter open · S save

A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work

Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson, Michael Mitzenmacher+2

UCL and Broadcom · VMware Research by Broadcom · North Carolina State University+2

Residual VQ Vector Quantization Low-Bit Quantization Neural Network Quantization

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This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-bit quantizer that EDEN extended to any

b>0

bits per coordinate; we refer to them collectively as EDEN. First, TurboQuant

_{\text{mse}}

is a special case of EDEN obtained by fixing EDEN's scalar scale parameter to

S=1

. EDEN supports both biased and unbiased quantization, each optimized by a different

S

(chosen via methods described in the EDEN works). The fixed choice

S=1

used by TurboQuant is generally suboptimal, although the optimal

S

for biased EDEN converges to

1

as the dimension grows; accordingly TurboQuant

_{\text{mse}}

approaches EDEN's behavior for large

d

. Second, TurboQuant

_{\text{prod}}

combines a biased

(b-1)

-bit EDEN step with an unbiased 1-bit QJL quantization of the residual. It is suboptimal in three ways: (1) its

(b-1)

-bit step uses the suboptimal

S=1

; (2) its 1-bit unbiased residual quantization has worse MSE than (unbiased) 1-bit EDEN; (3) chaining a biased

(b-1)

-bit step with a 1-bit unbiased residual step is inferior to unbiasedly quantizing the input directly with

b

-bit EDEN. Third, some of the analysis in the TurboQuant work mirrors that of the EDEN works: both exploit the connection between random rotations and the shifted Beta distribution, use the Lloyd-Max algorithm, and note that Randomized Hadamard Transforms can replace uniform random rotations. Experiments support these claims: biased EDEN (with optimized

S

) is more accurate than TurboQuant

_{\text{mse}}

, and unbiased EDEN is markedly more accurate than TurboQuant

_{\text{prod}}

, often by more than a bit (e.g., 2-bit EDEN beats 3-bit TurboQuant

_{\text{prod}}

). We also repeat all accuracy experiments from the TurboQuant paper, showing that EDEN outperforms it in every setup we have tried.

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