Quantization Robustness

Latest papers

22 papers7 papers in the last four weeks, up 133% on the four weeks before. 0.0% of all new papers.

When Lower Reconstruction Loss Hurts: Distributionally Robust Refinement for Low-Bit LLM Quantization
Oct 8, 2026Yanlong Zhao, Xiaoyuan Cheng, Huihang Liu +6LLM Quantization Post-Training Quantization
University of Science and Technology of China · University College London · Shanghai University of Finance and Economics +5
Activation Denoising: A Robustness View on Parallel vs Sequential LLM Quantization
Oct 5, 2026Yan Scholten, Rachel Lawrence, James Hensman +3LLM Quantization Quantization Robustness
Dept. of Computer Science & Munich Data Science Institute, Technical University of Munich · Microsoft Research Cambridge, UK
Beyond In-Distribution Preservation: Recovering Generalization in Quantized VLAs via Vulnerability-Oriented Tuning
Oct 5, 2026Shen Ruan, Wenchang Gao, Jin Wang +4Vision-Language-Action Models OOD Generalization
Soochow University · Independent Researcher · Shanghai Jiao Tong University +2
Understanding the Weight Averaging Mechanism in LLM Training for Post-Training Quantization
Oct 4, 2026Hanzhang Wang, Tianqi Shen, Zonglin Liu +3LLM Quantization Quantization Robustness
City University of Hong Kong · The University of Hong Kong
PulseQuant: Propagation-Guided Subspace Correction for 4-Bit Video Diffusion Transformers
Sep 27, 2026Yutong Wang, Xingtong Ge, Enhuai Liu +4Video Diffusion Models Diffusion Transformer
The University of Sydney · The Hong Kong University of Science and Technology · The Chinese University of Hong Kong +1
Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction
Sep 23, 2026Jialu Wang, Jianing Deng, Shuqing Luo +6Machine Unlearning LLM Unlearning
University of North Carolina at Chapel Hill · University of Pittsburgh · University of Arizona +1
The Undetected Damage of Quantization on Retrieval and How to Fix It
Sep 21, 2026Luca Zhou, Alessandro Zirilli, Daniele Solombrino +2Mixed-Precision Quantization Information Retrieval
Sapienza University of Rome · Not Diamond · Paradigma
Why Does Post-Training Quantization Work?
Sep 10, 2026Yuxiang Chen, Michael Beyer, Jun Zhu +1LLM Quantization Quantization Robustness
Dept. of Comp. Sci. and Tech., Institute for AI, BNRist Center, THBI Lab, Tsinghua-Bosch Joint ML Center, Tsinghua University · College of AI, Tsinghua University · Bosch AI Research, Renningen, Germany
SandwichQuant: Which Parameters Matter Before and After Quantization?
Aug 25, 2026Peng Xia, Junbiao PangNeural Network Quantization Low-Bit Quantization
School of Information Science and Technology, Beijing University of Technology Beijing, China
Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models
Aug 21, 2026Deepanshu Pandey, Arnav Chavan, Nahush Lele +2Quantization Robustness Neural Network Quantization
Amazon
Which Decisions Low-Bit Quantization Breaks, and How to Predict Them
Aug 6, 2026Zekun Wu, Swati Dhiman, Adriano KoshiyamaPost-Training Quantization Low-Bit Quantization
Holistic AI · UCL Centre for Artificial Intelligence, University College London
Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents
Jul 29, 2026Jiwon Jang, Kisu Yang, Heuiseok Lim +1Post-Training Quantization AI Agent Reliability
VAIV Company · Seoul National University · Korea University
GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries
Jul 22, 2026Miguel P. Bento, João F. SeabraLLM Quantization Rotation-Based Quantization
Independent Researcher · Lisbon, Portugal
Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics
Jul 20, 2026Hamidreza Yaghoubi Araghi, Parastoo Pilevar, Ming C. LinNeural Network Robustness Image Corruption Robustness
University of Maryland, College Park, MD, USA
Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization
Jun 7, 2026Patrik Czakó, Gábor Kertész, Sándor SzénásiLLM Quantization 4-Bit Quantization
Doctoral School of Applied Informatics and Applied Mathematics, Obuda University Budapest, Hungary · John von Neumann Faculty of Informatics, Obuda University Budapest, Hungary
DocQT: Improving Document Forgery Localization Robustness via Diverse JPEG Quantization Tables
May 19, 2026Kylian Ronfleux-Corail, Guillaume Bernard, Mickaël Coustaty +1Image Corruption Robustness Image Forgery Detection
L3I · MAIF, Niort, France · L3i Laboratory, La Rochelle University, La Rochelle, France
Attention Sinks and Outliers in Attention Residuals
May 18, 2026Haozheng Luo, Haoran Dai, Shaoyang Zhang +10LLM Quantization Attention Residual Connections
Department of Computer Science, Northwestern University · Department of Computer Science, Illinois Institute of Technolog · Department of Computer Science and Engineering, University of Michigan +5
DurableUn: Quantization-Induced Recovery Attacks in Machine Unlearning
May 4, 2026Abdullah Ahmad Khan, Ferdous SohelQuantization-Aware Training 4-Bit Quantization
Murdoch University
On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks
Apr 22, 2026Aarav Gupta, Gururaj Deshpande, Chandreyi ChakrabortyMixed-Precision Quantization Diffusion Model Quantization
College of Computing · Georgia Institute of Technology
Zero-Shot Quantization via Weight-Space Arithmetic
Apr 3, 2026Daniele Solombrino, Antonio Andrea Gargiulo, Alessandro Zirilli +3Quantization Robustness Quantization
Sapienza University of Rome · Sapienza University of Rome / Paradigma
RESQ: A Unified Framework for REliability- and Security Enhancement of Quantized Deep Neural Networks
Mar 16, 2026Ali Soltan Mohammadi, Samira Nazari, Ali Azarpeyvand +5Adversarial Robustness Quantization Robustness
University of Zanjan, Zanjan, Iran · Brandenburg Technical University, Cottbus, Germany · Tallinn University of Technology, Tallinn, Estonia +1
Quantization Robustness of Monotone Operator Equilibrium Networks
Mar 11, 2026James Li, Philip H. W. Leong, Thomas ChaffeyQuantization Robustness Neural Network Quantization
School of Electrical and Computer Engineering, The University of Sydney, NSW, Australia

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When Lower Reconstruction Loss Hurts: Distributionally Robust Refinement for Low-Bit LLM Quantization

Yanlong Zhao, Xiaoyuan Cheng, Huihang Liu, Baihua He+5

University of Science and Technology of China · University College London · Shanghai University of Finance and Economics+5

LLM Quantization Post-Training Quantization Weight-Only Quantization Quantization Robustness

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Weight-only post-training quantization (PTQ) relies heavily on reconstruction loss minimization to preserve model quality at low precision. We show that the weights favored by minimizing this loss need not yield better model performance on new tasks. In fact, we find that lower reconstruction loss can even degrade model performance on the same calibration data. Our analysis further shows that weights with lower reconstruction loss on calibration data can have higher loss than other weights when the distribution of input activations changes. Motivated by these observations and our analysis, we propose Distributionally Robust Quantization (DRQ), a post-hoc refinement process that minimizes worst-case reconstruction loss over a constrained set of input activation distributions. DRQ refines the integer codes representing quantized weights within the existing quantization grid, keeping quantization parameters and inference operators unchanged. Extensive experiments show that DRQ improves models quantized by six representative PTQ methods, including AWQ, GPTQ, and ParoQuant, and delivers gains across both dense and mixture-of-experts large language models. These results establish DRQ as a general post-hoc refinement framework for weight-only PTQ, achieving better downstream performance without adding inference overhead.