Low-Bit Quantization

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21 papers in the last four weeks, up 62% on the four weeks before. 0.2% of all new papers.

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LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection
Jun 2, 2026Liulu He, XuanAng Liu, Juntao Liu +8LLM Quantization LLM Compression
Nanjing University, China · China Mobile Research Institute, China · Xiaomi Corporation, China +2
Qift: Shift-Friendly No-Zero W2 Post-Training Quantization for Rotated W2A4/KV4 LLM Inference
Jun 1, 2026Chi-Wei Huang, Chia-Chi TsaiLLM Quantization Rotation-Based Quantization
National Cheng Kung University
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
Jun 1, 2026Ekaterina Alimaskina, Darya Rudas, Denis Shveykin +3LLM Quantization Efficient Language Model Reasoning
BRAIn Lab, Moscow, Russia
GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation
May 31, 2026Shihao Zhang, Rayan SaabLLM Quantization Low-Rank Adaptation
Department of Mathematics, University of California San Diego
Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
May 30, 2026Jinyang Du, Shenghao Jin, Ziqian Xu +5Video Diffusion Models Diffusion Model Quantization
SenseTime
ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression
May 30, 2026Wenya Yu, Chao Zhang, Li Wang +2LLM Quantization Post-Training Quantization
School of Mathematics and Statistics, Central South University, Changsha, China · Department of Computer Science, Central South University, Changsha, China · CRAN, Université de Lorraine, CNRS, Nancy, France +1
Batched Stochastic Linear Bandits with 1-Bit Communication Constraints
May 29, 2026Ivan Lau, Daniel McMorrow, Kevin Jamieson +1Multi-Armed Bandits Minimax Regret
National University of Singapore · University of Washington
HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization
May 28, 2026Artur Zagitov, Gleb Molodtsov, Aleksandr BeznosikovLLM Quantization Rotation-Based Quantization
1MIRAI · 2BRAIn Lab · 3Innopolis University
BitTP: The Lightweight Trajectory Prediction Model with BitLLM for Edge-Devices
May 28, 2026Mincheol Kang, Hyunjin Lim, Bomin Kang +1Multi-Agent Trajectory Prediction Weight-Only Quantization
1KAIST, Republic of Korea · 2DGIST, Republic of Korea
Quantized Keys Steal Attention: Bias Correction for KV-Cache Compression in Video Diffusion
May 25, 2026Tuna Tuncer, Felix Becker, Thomas PfeilVideo Diffusion Models Self-Attention
1Technical University of Munich · 2Tensordyne
GoQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization
May 25, 2026Maoyang Xiang, Tao Luo, Bo WangLLM Quantization Efficient Neural Network Inference
Information Systems Technology and Design Singapore University of Technology and Design Singapore · Institute of High Performance Computing (IHPC) Agency for Science, Technology and Research (A*STAR) Singapore
Mapping the Schedule x Bit-Width Boundary in Sub-100M Quantisation-Aware Training
May 25, 2026Christian Brandt ThomassenLLM Quantization Learning Rate Scheduling
Dwarf A/S, Copenhagen, Denmark
The Quantization Benefits of Residual-Free Transformers
May 25, 2026Yiping Ji, Mahalakshmi Sabanayagam, Peyman Moghadam +2Transformer Activation Outliers
Australian Institute for Machine Learning, Adelaide University · DATA61, CSIRO
JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates
May 25, 2026Kai Yi, Vignesh Vivekraja, Harshit Khaitan +1LLM Quantization Quantization-Aware Training
Meta AI
InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization
May 25, 2026Ke Li, Dong An, Xiaoling Zang +6LLM Quantization Activation Quantization
School of Software Technology, Zhejiang University · Ant Group · College of Computer Science and Technology, Zhejiang University of Technology +3
Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization
May 24, 2026Gorgi PavlovLLM Quantization Rotation-Based Quantization
Lehigh University
BitsMoE: Cost-Aware Bit Allocation in Spectral Space for MoE LLM Quantization
May 22, 2026Jiayu Zhao, Zihan Teng, Minhao Fan +4Mixed-Precision Quantization LLM Quantization
School of Microelectronics, University of Science and Technology of China · College of Computing and Data Science, Nanyang Technological University · School of Electrical and Electronic Engineering, Nanyang Technological University
Signs Beat Floats: Low-Rank Double-Binary Adaptation for On-Device Fine-Tuning
May 22, 2026Yoshihiko Fujisawa, Yuma Ichikawa, Yudai Fujimoto +2LLM Quantization Memory-Efficient Fine-Tuning
Fujitsu Limited · Institute of Science Tokyo · RIKEN Center for AIP +1
ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention
May 21, 2026Joe SharrattMixed-Precision Quantization Self-Attention
QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
May 21, 2026Haotong Qin, Xudong Ma, Xianglong Liu +4Quantization-Aware Training Low-Bit Quantization
ETH Zurich, Switzerland · Beihang University, China · Shanghai Jiao Tong University, China
ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
May 19, 2026Arash Akbari, Arman Akbari, Masih Eskandar +11Mixed-Precision Quantization VLM Quantization
1Northeastern University · University of Georgia · 3Cisco Systems
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models
May 19, 2026Yi Zhong, Haotong Qin, Xindong Zhang +2Vision-Language Models VLM Quantization
VCIP, College of Computer Science, Nankai University · D-ITET, ETH Zürich · OPPO Research Institute +1
OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond
May 19, 2026Zunhai Su, Rui Yang, Chao Zhang +11KV Caching Rotation-Based Quantization
Tsinghua University · Meituan LongCat Team · The University of Hong Kong +3
K-Quantization and its Impact on Output Performance
May 19, 2026Robin Baki Davidsson, Pierre NuguesLLM Quantization LLM Evaluation
Lund University · Lund, Sweden
MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization
May 18, 2026Le Su, Xing Luo, Zhi JinLLM Quantization Post-Training Quantization
School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen, Guangdong 518107, China · Department of Frontier Research Peng Cheng Laboratory Shenzhen, Guangdong 518055, China
OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization
May 18, 2026Zhongzhu Zhou, Donglin Zhuang, Jisen Li +4Rotation-Based Quantization LLM Inference Acceleration
Together AI · University of Sydney · University of Illinois Urbana-Champaign
Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings
May 17, 2026Wenxuan XiaoNeural Representation Geometry Embedding Compression
Changsha University
WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
May 17, 2026Dongyue Li, Zechun Liu, Kai Yi +6LLM Quantization Quantization-Aware Training
1Northeastern University, MA · 2Meta AI, CA.
When Bits Break Recourse: Counterfactual-Faithful Quantization
May 16, 2026Chaymae Yahyati, Ismail Lamaakal, Khalid El Makkaoui +1Mixed-Precision Quantization Counterfactual Evaluation
Mohammed First University
A Hardware-Aware, Per-Layer Methodology for Post-Training Quantization of Large Language Models
May 14, 2026Earl KillianMixed-Precision Quantization LLM Quantization

Jun 2, 2026, cs.LGJ/K move · Enter open · S save

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection

Liulu He, XuanAng Liu, Juntao Liu, Taolue Feng+7

Nanjing University, China · China Mobile Research Institute, China · Xiaomi Corporation, China+2

LLM Quantization LLM Compression Weight-Only Quantization Low-Bit Quantization Neural Network Quantization

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Existing quantization methods are fundamentally limited by rigid, integer-based bit-widths (e.g., 2, 3-bit), resulting in a

deployment gap" where Large Language Models cannot be optimally fitted to specific memory budgets. To bridge this gap, we introduce LiftQuant, a novel framework that enables continuous bit-width control for true Pareto-optimal deployment. The core innovation is a

lift-then-project" mechanism which approximates low-dimensional weight vectors by projecting a simple 1-bit lattice from a higher-dimensional ``lifted" space. Crucially, the effective bit-width is determined simply by the ratio of the lifted dimension to the original dimension, which allows the bit-width to be tuned quasi-continuous as the dimension is a flexible structural parameter. This projection generates a structured yet non-uniform codebook, capturing the expressive power of Vector Quantization (VQ). While beneficial over VQ, LiftQuant's decoding path relies solely on linear transformations and 1-bit uniform quantizers, retaining hardware-friendly nature. This flexibility is transformative: LiftQuant enables a 70B LLM to be compressed to 2.4 bits to precisely fit a 24GB GPU, where its performance significantly surpasses state-of-the-art 2-bit models fitted on the same device. Our code and ckpt is available at https://github.com/Heliulu/LiftQuant.

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Latest papers 155