4-Bit Quantization

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8 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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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
QuantForge: Discovering Residual Decompositions for MXFP4 Post-Training Quantization
Sep 28, 2026Qiulin Shang, Zhoutong Wu, Jie Hu +1LLM Quantization Large Language Model-Guided Optimization
Peking University
JustQuant: You Don't Need Smoothing, SVD, or Rotation for 4-Bit Activation Quantization
Sep 27, 2026Kaicheng Yang, Kaisen Yang, Chunyu Liu +8Diffusion Model Quantization Quantization-Aware Training
Shanghai Jiao Tong University · Tsinghua University · The Hong Kong Polytechnic University
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
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
Global Ranks Survive, Selected Heads Shift: BOS-Sink Topology under 4-bit Weight-Only Quantization
Sep 20, 2026Kuanlin Chen, Chen-Wei Kuo, Cheng-En OuAttention Head Analysis 4-Bit Quantization
Independent Researcher Taoyuan, Taiwan · National Tsing Hua University Hsinchu, Taiwan
MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration
Sep 17, 2026Yuan Liao, Jae-sun SeoAI Accelerator Inference VLM Quantization
Cornell Tech, Cornell University New York, NY, USA
KBBQ: A Predictive Noise Law and the Limits of Spectrum Flattening in FP4 Quantization
Sep 8, 2026Lexington Whalen, Yuki Ito, Ryo Sakamoto4-Bit Quantization Neural Network Quantization
1SB Intuitions, Tokyo, Japan.
Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLM
Sep 3, 2026Sergii Kozyrev, Davyd MaiborodaLLM Quantization 4-Bit Quantization
When Local Variance Optimality Is Not Enough: RoPE-Aligned Q/K Rotations for Dynamic 4-Bit Quantisation
Aug 13, 2026Shuhan Wang, Yilin Luo, Nan Xu +1LLM Quantization 4-Bit Quantization
NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space
Aug 13, 2026Eleftherios Mylonas, Angelos Kouprizas, Michael Birbas +14-Bit Quantization Hardware-Aware NAS
Dept. of ECE University of Patras Patras, Greece
Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting
Aug 12, 2026Junyi Ye, Ivy Gateri WanjikuFinancial Forecasting 4-Bit Quantization
School of Computing, Montclair State University · Montclair, New Jersey, USA
PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks
Aug 7, 2026Hui Xie, Tong Shi, Haotong Qin +3Mixed-Precision Quantization 4-Bit Quantization
State Key Laboratory of Complex & Critical Software Environment, Beihang University · School of Artificial Intelligence, Beihang University · Center for Project-Based Learning, D-ITET, ETH Zurich +2
FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling
Aug 3, 2026Xianglong Yan, Hong Liu, Chengzhu Bao +4LLM Quantization 4-Bit Quantization
Shanghai Jiao Tong University · Tencent
Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents
Jul 29, 2026Jiwon Jang, Kisu Yang, Heuiseok Lim +1AI Agent Reliability Benchmark Design
VAIV Company · Seoul National University · Korea University
HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models
Jul 29, 2026Hei Yi Mak, Shadan Golestan, Hoang Le +10LLM Quantization 4-Bit Quantization
Huawei
Stable FP4 Training via Transposition-Invariant Block Quantization
Jul 27, 2026Mehdi Rahimifar, Amin Darabi, Mehran Taghian Jazi +6Mixed-Precision Quantization 4-Bit Quantization
MXAttention: Data-Free Optimal Scaling and Pre-Normalization Quantization for MXFP4 Attention
Jul 27, 2026Jianlin Yu, Jing Lin, Linghui Kong +13Video Diffusion Models Diffusion Model Quantization
Huawei Technologies Co., Ltd.
KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers
Jul 23, 2026Yann Bouquet, Alireza Khodamoradi, Kristof Denolf +1Diffusion Transformer Diffusion Model Quantization
EPFL, Lausanne, Switzerland. · Advanced Micro Devices, Inc., Longmont, CO, USA.
QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides
Jul 17, 2026Zhengyang Zhuge, Hao Yu, Xin Wang +4LLM Quantization RL for Language Models
Qwen Team, Alibaba Inc.
Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate
Jul 14, 2026Joshua HillMixed-Precision Quantization LLM Quantization
Baseten Labs, Inc. · David R. Cheriton School of Computer Science, University of Waterloo
Full-Stack FP4: Stable LLM Pretraining with Quantized Projections, Optimizers, and Attention
Jul 5, 2026Siyu Ding, Mingchuan Ma, Jiabo Tong +3Language Model Pretraining Mixed-Precision Quantization
Institute of Automation, Chinese Academy of Sciences · Sichuan University · Beijing Zhongguancun Academy +3
HiFA4: Training-Free 4-bit FlashAttention on Ascend HIF4 NPUs for LLM Inference
Jul 5, 2026Hui Dong, Yanzhao Li, Jie Gao +5Softmax Attention LLM Quantization
Huawei Technologies
$\text{Log}_\text{b}$ Quant: Quantizing Language Models in Logarithmic Space
Jul 1, 2026Jeremias Bohn, Tizian Dippold, Mahdi Koubaa +2LLM Quantization 4-Bit Quantization
School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
RotateAttention: RoPE-Aware Rotation and Range Rectification for INT4 Quantized Attention in Video Generation
Jul 1, 2026Yaofu Liu, Wanli Lan, Jinxi Li +2Mixed-Precision Quantization Rotary Positional Embeddings
The Hong Kong University of Science and Technology, Hong Kong SAR, China · Independent Researcher, China
SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference
Jun 25, 2026Haoqian Meng, Yilun Luo, Yafei Zhao +44-Bit Quantization LLM Inference Acceleration
School of Computer Science and Technology, Tianjin University
Rethinking Shrinkage Bias in LLM FP4 Pretraining: Geometric Origin, Systemic Impact, and UFP4 Recipe
Jun 18, 2026Qian Zhao, Kunlong Chen, Changxin Tian +9Language Model Pretraining LLM Quantization
Ling Team, Ant Group
ReQAT: Achieving Full-Precision Reasoning Accuracy with 4-bit Floating-Point Quantization-Aware Training
Jun 14, 2026Janghwan Lee, Sihwa Lee, Jinseok Kim +4LLM Quantization Quantization-Aware Training
1Hanyang University, 2Rebellions Inc., Republic of Korea.
MosaicQuant: Inlier-Outlier Disaggregation for Unified 4-Bit LLM Quantization
Jun 14, 2026Yangjia Hu, Haodong Wang, Zicong Hong +8LLM Quantization 4-Bit Quantization
HKUST · EPFL · MetaX Integrated Circuits Co., Ltd
DynamicPTQ: Mitigating Activation Quantization Collapse via Residual-Stream Dynamics
Jun 10, 2026Zimo Zhao, Maolin Wang, Bowen Yu +3Mixed-Precision Quantization LLM Quantization
City University of Hong Kong · Hong Kong, China · Zhejiang University of Technology +1

Sep 30, 2026, cs.ROJ/K move · Enter open · S save

SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models

Yunhan Wang, Haodong Wang, Zhiming Liu, Zicong Hong+6

Tongji · HKUST · HIT+1

4-Bit Quantization Efficient Inference for World Action Models Activation Quantization Neural Network Quantization

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World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous semantic streams of video, proprioceptive, and action tokens. Quantization reduces inference cost, but comparable numerical errors in different streams can have markedly different effects on final actions, making numerical accuracy alone insufficient for reliable control. We introduce SteerQuant, a 4-bit quantization framework for WAMs that steers errors toward computations with less influence on final actions. It maps how each stream's quantization errors affect final actions and uses this map to guide shared channel scaling. Activation scaling is further calibrated for each stream and denoising step to accommodate changes in activation ranges and action impact. This adapts quantization to different stream requirements without duplicating weights or increasing bit-widths for selected streams. To reduce the extra kernel launches and memory traffic introduced by scaling, we develop Rudder, a 4-bit inference engine for WAMs that fuses scaling and output compensation into low-bit kernels. Under W4A8 and W4A4, SteerQuant maintains mean LIBERO success within 0.8 percentage points of full precision, while delivering up to

2.23\times

denoising speedup over BF16 across three WAMs with reduced peak GPU memory usage. On a real dual-arm robot, W4A8 deployment achieves a

1.35\times

end-to-end inference speedup while maintaining average task success relative to BF16.

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Latest papers 65