Activation Quantization

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Activation quantization studies how to represent the intermediate values passed between neural-network layers with fewer bits, reducing memory traffic and enabling faster, smaller deployments. Common approaches include calibration-based clipping and scale selection, smoothing or rotating activations to reduce outliers, mixed-precision allocation, and quantization-aware training; methods target formats such as INT8, INT4, and FP4. Work spans language and vision models, diffusion systems, speech models, and interactive policies, with evaluations using measures such as perplexity, ImageNet accuracy, generation quality, and task success. A central tension is preserving quality across changing activation distributions while delivering real speed and memory gains on the hardware and kernels used in deployment.

Latest papers

120 papers24 papers in the last four weeks, up 71% on the four weeks before. 0.1% of all new papers.

A Unified Hessian-Guided Framework for Post-Training Quantization
Oct 29, 2026Mads Toftrup, Marco Federici, Boris van Breugel +2Quantization LLM Quantization
Aarhus University · Qualcomm AI Research
Quantize by Doing: Task-Guided Mixed-Precision Quantization of Vision-Language-Action Models
Oct 28, 2026Jiuyi Xu, Xiao Hu, Meida Chen +3Mixed-Precision Quantization VLM Quantization
Colorado School of Mines · Northeastern University · USC Institute for Creative Technologies +1
ResCom: Activation Residual Compensation for Large Language Model Quantization
Oct 25, 2026Ning Yang, Bohan Cui, Zhuowen Liu +1LLM Quantization Quantization Error Compensation
Institute of Automation, Chinese Academy of Sciences · Fudan University · The Chinese University of Hong Kong +1
LIRQuant: An Efficient Locally Isolated Rotation Quantization Method for Large Language Models
Oct 19, 2026Desheng Xu, Hanglin Li, Tianchen Song +2LLM Quantization Post-Training Quantization
Li Auto Inc.
PACT: NVFP4 Data-Flow LoRA Fine-Tuning on Consumer GPUs
Oct 18, 2026Lei Gao, Hossein Entezari Zarch, Chaoyi Jiang +1Memory-Efficient Fine-Tuning Parameter-Efficient Fine-Tuning
University of Southern California
SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks
Oct 16, 2026Kai Sun, Peibo Duan, Zhanglu Yan +3Spiking Neural Networks Post-Training Quantization
Monash University · National University of Singapore · Northeastern University
Compiling Multi-Threshold Operators for Quantized Non-Monotonic and Fine-Granular Activations in Custom-Tailored DNN Accelerators
Oct 15, 2026Christoph Berganski, Felix Jentzsch, Marco PlatznerActivation Quantization Neural Network Activation Functions
Computer Engineering Group, Paderborn University, Paderborn, Germany
ShiftQAT: Quantization-Aware Training for the Numerical Contract of a Fixed-Function Edge CNN Accelerator
Oct 15, 2026Oguzhan Buyuksolak, Osman Erman Okman, Mehmet Gorkem UlkarHardware-Aware Quantization Neural Network Quantization
Analog Devices, Inc.
Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution
Oct 12, 2026Jinwoo Chung, Jangho KimActivation Quantization Post-Training Quantization
Kookmin University Seoul, Republic of Korea
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models
Oct 11, 2026Xinmeng Liu, Xianglong Yan, Kaicheng Yang +2Post-Training Quantization Neural Network Quantization
Shanghai Jiao Tong University · Xi’an University of Electronic Science and Technology
DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs
Oct 9, 2026Mahsa Salmani, Sayeh Sharify, Hesham MostafaLLM Quantization Post-Training Quantization
d-Matrix
Deflating the Hessian: Rank-4 W4A4 Quantization for Multimodal Diffusion Transformers
Oct 8, 2026Shiwen Wang, Pengxiang Zhao, Xiaoming YuanDiffusion Model Quantization Post-Training Quantization
The University of Hong Kong · Huawei Technologies Co., Ltd.
TR-PTQ: High-Accuracy Integer-Only Transformer Post Training Quantization via Taylor Region Reformulation
Oct 7, 2026Eliyahu Levy, Adam Teman, Yoni PugachovPost-Training Quantization Neural Network Quantization
Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
Few Bits, One Law: Toward W2A4KV2
Oct 6, 2026Kai Yi, Tarek Elgamal, Sruthikesh Surineni +3Neural Network Quantization LLM Quantization
Meta AI
Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration
Oct 6, 2026Hyeongheon Cha, Young D. Kwon, Sung-Ju LeeTest-Time Adaptation Vision Transformer
KAIST · Samsung AI Center-Cambridge · University of Cambridge
CentriQ: Calibration-Free Quantization of Diffusion Transformers via Exact Mean Centering
Oct 5, 2026Nataša Jovanović, Mathieu Salzmann, Saqib JavedDiffusion Transformer Diffusion Model Quantization
Tenstorrent · EPFL
VisionMX: Unlocking Microscaling Post-Training Quantization for Vision Models
Oct 2, 2026Elad Dror Cohen, Ofir Gordon, Lior Dikstein +2Neural Network Quantization Post-Training Quantization
Arm AI Research (AAIR), Arm Ltd
CHASE-VLA: Post-Training Quantization Framework for Vision-Language-Action Models with Chunk-Aware Scale Estimation
Oct 2, 2026Jin Hyun, Jung Gyu Min, Gyuhyun Jung +1Post-Training Quantization VLM Quantization
KAIST, Daejeon, South Korea · POSTECH, Pohang, South Korea
Joint Branch-Space Transform Coding for Diffusion Activation Quantization with Classifier-Free Guidance
Oct 1, 2026Mingrun Jiang, Yuejia Liu, Zishan Shao +11Classifier-Free Guidance Diffusion Model Quantization
Duke University · Carnegie Mellon University · University of Florida +2
Backward-State Policy Is Part of the Learning Algorithm
Sep 30, 2026Shuxiao Xie, Shuyang Xie, Dezhi Ran +2Mixed-Precision Quantization Backpropagation
Beijing Tongming Lake Information Technology Application Innovation Center (TLAIC), China
SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models
Sep 30, 2026Yunhan Wang, Haodong Wang, Zhiming Liu +74-Bit Quantization Efficient Inference for World Action Models
Tongji · HKUST · HIT +1
Behavioral Capacity Certificates for Quantized Language Models
Sep 29, 2026Arian Eamaz, Mojtaba SoltanalianLLM Quantization KV-Cache Quantization
Department of Electrical and Computer Engineering University of Illinois Chicago
Bits Under ZK-LLM: Evaluating Zero-Knowledge-Friendly Quantization for Verifiable Private LLM Inference
Sep 29, 2026Taeung Yoon, Yupeng Zhang, Xiaojing LiaoLLM Quantization LLM Inference
Siebel School of Computing and Data Science University of Illinois Urbana-Champaign
ThinQuant: Scalable Rotation Learning for Weight and Activation Quantization of LLMs
Sep 28, 2026Mehdi Makni, Ryan Lucas, Rahul MazumderLLM Quantization Rotation-Based Quantization
Operations Research Center Massachusetts Institute of Technology
TORQUE: Optimizing What (not) to Quantize Before and After Rotation
Sep 28, 2026Ran Ben Basat, Michael Mitzenmacher, Shay VargaftikRotation-Based Quantization Vector Quantization
University College London · Harvard University · VMware Research by Broadcom
Beyond Reconstruction Loss in Post-Training Quantization: Balanced Fitting for Large Vision-Language Models
Sep 28, 2026Minchan Kang, Kyeonghye Park, Seungyeon Sa +3VLM Quantization Large Vision-Language Models
Korea Advanced Institute of Science and Technology (KAIST) · Hanbat National University
JustQuant: You Don't Need Smoothing, SVD, or Rotation for 4-Bit Activation Quantization
Sep 27, 2026Kaicheng Yang, Kaisen Yang, Chunyu Liu +8Neural Network Quantization Diffusion Model Quantization
Shanghai Jiao Tong University · Tsinghua University · The Hong Kong Polytechnic University
Q-WAM: 4-Bit Quantization of World Action Models with Action-Subspace Protection
Sep 27, 2026Arash Akbari, Arman Akbari, Jingwu Luo +7Mixed-Precision Quantization 4-Bit Quantization
Northeastern University · EmbodyX · University of Minnesota - Twin Cities +1
PrismQuant: Optimal Null-Space Rotations for Grouped Quantizers
Sep 26, 2026Yanlong Chen, Yining Chen, Song Zhang +2LLM Quantization Rotation-Based Quantization
Nanyang Technological University, Singapore · Nanjing University of Posts and Telecommunications, Nanjing, China · Nanjing University, Nanjing, China +1
Same Bit Width, Different Outcomes: Post-Training Quantization of Text-to-Speech Across Architectures
Sep 24, 2026Se Un Park, Yutae Kim, Junyoung ParkTTS Evaluation Weight-Only Quantization
UX Factory, Inc.

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A Unified Hessian-Guided Framework for Post-Training Quantization

Mads Toftrup, Marco Federici, Boris van Breugel, Paul Whatmough+1

Aarhus University · Qualcomm AI Research

Quantization LLM Quantization Post-Training Quantization Low-Bit Quantization Activation Quantization

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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 independently using local reconstruction objectives, which fail to account for the varying impact of quantization errors on downstream predictions. Direct optimization of task-level metrics would provide a more faithful objective, but is challenging due to the discontinuous nature of quantization operations and the high computational cost of evaluating their downstream effects. In this work, we derive a Hessian-guided approximation of the output change induced by joint weight and activation quantization. Based on a tractable Fisher approximation, we formulate a unified post-training quantization framework that enables the joint optimization of quantization ranges, linear transformations, and weight rounding under a common output-sensitivity objective. The resulting method remains practical for modern large language models and consistently outperforms state-of-the-art transformation and rounding methods across both channel-wise and block-wise quantization settings.