Neural Network Quantization

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57 papers in the last four weeks, up 54% on the four weeks before. 0.4% of all new papers.

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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

Nov 24, 2025, cs.LGJ/K move · Enter open · S save

QuantKAN: A Unified Quantization Framework for Kolmogorov Arnold Networks

Kazi Ahmed Asif Fuad, Lizhong Chen

Department of EECS · Oregon State University · Corvallis, OR 97331

Quantization-Aware Training Kolmogorov-Arnold Networks Post-Training Quantization Low-Bit Quantization Neural Network Quantization

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Kolmogorov--Arnold Networks (KANs) replace linear weights with spline-based functions, offering strong expressivity but posing challenges for low-precision deployment due to heterogeneous parameter distributions. We introduce QuantKAN, the first unified framework for quantization-aware training (QAT) and post-training quantization (PTQ) of KANs. The framework employs branch-aware quantizers for base and spline parameters and extends modern QAT and PTQ methods to spline-based layers across EfficientKAN, FastKAN, PyKAN, and KAGN. Experiments on MNIST, CIFAR-10/100, TinyImageNet, and ImageNet provide the first unified QAT/PTQ KAN benchmarks and show that DSQ is the most robust QAT method at aggressive low-bit settings, while GPTQ is the strongest PTQ method at moderate precision. Sensitivity analyses reveal architecture-specific failure modes: spline/basis parameters dominate in FastKAN, while base or scaling parameters dominate in EfficientKAN, GRAM, and PyKAN. Vivado HLS estimates on a Xilinx UltraScale+ device further suggest up to 3.32

\times

throughput and 7.7

\times

lower estimated dynamic energy per inference under W4A4, exposing a residual \emph{basis-evaluation tax} that motivates basis-aware microarchitecture. QuantKAN is available at https://github.com/OSU-STARLAB/QuantKAN/.

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