Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate
Fuente:
arXiv
Saved in:
| Main Authors: | Kim, Jaemin, Kim, Sungkyun, Lee, Junyeol, Seo, Jiwon |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FlexiQ: Adaptive Mixed-Precision Quantization for Latency/Accuracy Trade-Offs in Deep Neural Networks
by: Kim, Jaemin, et al.
Published: (2025)
by: Kim, Jaemin, et al.
Published: (2025)
ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference
by: Oh, Hyungjun, et al.
Published: (2024)
by: Oh, Hyungjun, et al.
Published: (2024)
LittleBit: Ultra Low-Bit Quantization via Latent Factorization
by: Lee, Banseok, et al.
Published: (2025)
by: Lee, Banseok, et al.
Published: (2025)
Speculative Verification: Exploiting Information Gain to Refine Speculative Decoding
by: Kim, Sungkyun, et al.
Published: (2025)
by: Kim, Sungkyun, et al.
Published: (2025)
TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction
by: Li, Yuhang, et al.
Published: (2024)
by: Li, Yuhang, et al.
Published: (2024)
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
by: Wang, Haoyu, et al.
Published: (2024)
by: Wang, Haoyu, et al.
Published: (2024)
MUXQ: Mixed-to-Uniform Precision MatriX Quantization via Low-Rank Outlier Decomposition
by: Lee, Seoungsub, et al.
Published: (2026)
by: Lee, Seoungsub, et al.
Published: (2026)
DecDEC: A Systems Approach to Advancing Low-Bit LLM Quantization
by: Park, Yeonhong, et al.
Published: (2024)
by: Park, Yeonhong, et al.
Published: (2024)
Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
by: Zhang, Chenxiang, et al.
Published: (2025)
by: Zhang, Chenxiang, et al.
Published: (2025)
StableQAT: Stable Quantization-Aware Training at Ultra-Low Bitwidths
by: Chen, Tianyi, et al.
Published: (2026)
by: Chen, Tianyi, et al.
Published: (2026)
Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning
by: Kim, Jinsu, et al.
Published: (2024)
by: Kim, Jinsu, et al.
Published: (2024)
MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization
by: Su, Le, et al.
Published: (2026)
by: Su, Le, et al.
Published: (2026)
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
by: Kim, Junhan, et al.
Published: (2024)
by: Kim, Junhan, et al.
Published: (2024)
PINT: Physics-Informed Neural Time Series Models with Applications to Long-term Inference on WeatherBench 2m-Temperature Data
by: Park, Keonvin, et al.
Published: (2025)
by: Park, Keonvin, et al.
Published: (2025)
Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
by: Zhang, Tianao, et al.
Published: (2025)
by: Zhang, Tianao, et al.
Published: (2025)
LoRAQuant: Mixed-Precision Quantization of LoRA to Ultra-Low Bits
by: Mirzaei, Amir Reza, et al.
Published: (2025)
by: Mirzaei, Amir Reza, et al.
Published: (2025)
Training Dynamics Impact Post-Training Quantization Robustness
by: Catalan-Tatjer, Albert, et al.
Published: (2025)
by: Catalan-Tatjer, Albert, et al.
Published: (2025)
HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models
by: Yan, Xin, et al.
Published: (2026)
by: Yan, Xin, et al.
Published: (2026)
CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training
by: Tabesh, Soroush, et al.
Published: (2025)
by: Tabesh, Soroush, et al.
Published: (2025)
PTQ4VM: Post-Training Quantization for Visual Mamba
by: Cho, Younghyun, et al.
Published: (2024)
by: Cho, Younghyun, et al.
Published: (2024)
NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models
by: Chong, Hyochan, et al.
Published: (2026)
by: Chong, Hyochan, et al.
Published: (2026)
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
by: Lee, Jung Hyun, et al.
Published: (2023)
by: Lee, Jung Hyun, et al.
Published: (2023)
Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models
by: Park, Jungwoo, et al.
Published: (2025)
by: Park, Jungwoo, et al.
Published: (2025)
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
by: Lee, Jung Hyun, et al.
Published: (2024)
by: Lee, Jung Hyun, et al.
Published: (2024)
SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision
by: Choi, Yuseon, et al.
Published: (2025)
by: Choi, Yuseon, et al.
Published: (2025)
TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision
by: Kim, Jinhee, et al.
Published: (2025)
by: Kim, Jinhee, et al.
Published: (2025)
HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning
by: Zhang, Jinhao Zhang Yunquan, et al.
Published: (2026)
by: Zhang, Jinhao Zhang Yunquan, et al.
Published: (2026)
DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization
by: Lee, Dongyeun, et al.
Published: (2025)
by: Lee, Dongyeun, et al.
Published: (2025)
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
by: Zhao, Jiaqi, et al.
Published: (2025)
by: Zhao, Jiaqi, et al.
Published: (2025)
SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization
by: Zhao, Zhixiong, et al.
Published: (2025)
by: Zhao, Zhixiong, et al.
Published: (2025)
Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
by: Ouyang, Xu, et al.
Published: (2024)
by: Ouyang, Xu, et al.
Published: (2024)
BitSnap: Checkpoint Sparsification and Quantization in LLM Training
by: Peng, Yanxin, et al.
Published: (2025)
by: Peng, Yanxin, et al.
Published: (2025)
MSQ: Memory-Efficient Bit Sparsification Quantization
by: Han, Seokho, et al.
Published: (2025)
by: Han, Seokho, et al.
Published: (2025)
LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment
by: Lee, Banseok, et al.
Published: (2026)
by: Lee, Banseok, et al.
Published: (2026)
Understanding the Difficulty of Low-Precision Post-Training Quantization for LLMs
by: Xu, Zifei, et al.
Published: (2024)
by: Xu, Zifei, et al.
Published: (2024)
Efficient Multi-bit Quantization Network Training via Weight Bias Correction and Bit-wise Coreset Sampling
by: Kim, Jinhee, et al.
Published: (2025)
by: Kim, Jinhee, et al.
Published: (2025)
DMD-augmented Unpaired Neural Schrödinger Bridge for Ultra-Low Field MRI Enhancement
by: Kim, Youngmin, et al.
Published: (2026)
by: Kim, Youngmin, et al.
Published: (2026)
Measuring the Depth of LLM Unlearning via Activation Patching
by: Lee, Jaeung, et al.
Published: (2026)
by: Lee, Jaeung, et al.
Published: (2026)
Diagonal-Tiled Mixed-Precision Attention for Efficient Low-Bit MXFP Inference
by: Ding, Yifu, et al.
Published: (2026)
by: Ding, Yifu, et al.
Published: (2026)
pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training
by: Zhang, Wenzheng, et al.
Published: (2026)
by: Zhang, Wenzheng, et al.
Published: (2026)
Similar Items
-
FlexiQ: Adaptive Mixed-Precision Quantization for Latency/Accuracy Trade-Offs in Deep Neural Networks
by: Kim, Jaemin, et al.
Published: (2025) -
ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference
by: Oh, Hyungjun, et al.
Published: (2024) -
LittleBit: Ultra Low-Bit Quantization via Latent Factorization
by: Lee, Banseok, et al.
Published: (2025) -
Speculative Verification: Exploiting Information Gain to Refine Speculative Decoding
by: Kim, Sungkyun, et al.
Published: (2025) -
TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction
by: Li, Yuhang, et al.
Published: (2024)