@inproceedings{zhang-etal-2026-beyond-outliers,
title = "Beyond Outliers: A Data-Free Layer-wise Mixed-Precision Quantization Approach Driven by Numerical and Structural Dual-Sensitivity",
author = "Zhang, Hengyuan and
Chen, Xinrong and
Su, Zunhai and
Liang, Xiao and
Xiong, Jing and
XU, Wendong and
Xiao, He and
Tao, Chaofan and
Zhang, Wei and
Xie, Ruobing and
Jiang, Lei and
So, Hayden Kwok-Hay and
Wong, Ngai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1106/",
pages = "25342--25359",
ISBN = "979-8-89176-459-0",
abstract = "Layer-wise mixed-precision quantization (LMPQ) enables effective compression under extreme low-bit settings by allocating higher precision to sensitive layers. However, existing methods typically treat all intra-layer weight modules uniformly and rely on a single numerical property when estimating sensitivity, overlooking their distinct operational roles and structural characteristics. To address this, we propose NSDS, a novel calibration-free LMPQ framework driven by Numerical and Structural Dual-Sensitivity. Specifically, it first mechanistically decomposes each layer into distinct operational roles and quantifies their sensitivity from both numerical and structural perspectives. These dual-aspect scores are then aggregated into a unified layer-wise metric through a robust aggregation scheme based on MAD-Sigmoid and Soft-OR to guide bit allocation. Extensive experiments demonstrate that NSDS consistently achieves superior performance compared to various baselines across diverse models and downstream tasks, without relying on any calibration data. The code is available at https://anonymous.4open.science/r/NSDS-DC56."
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<abstract>Layer-wise mixed-precision quantization (LMPQ) enables effective compression under extreme low-bit settings by allocating higher precision to sensitive layers. However, existing methods typically treat all intra-layer weight modules uniformly and rely on a single numerical property when estimating sensitivity, overlooking their distinct operational roles and structural characteristics. To address this, we propose NSDS, a novel calibration-free LMPQ framework driven by Numerical and Structural Dual-Sensitivity. Specifically, it first mechanistically decomposes each layer into distinct operational roles and quantifies their sensitivity from both numerical and structural perspectives. These dual-aspect scores are then aggregated into a unified layer-wise metric through a robust aggregation scheme based on MAD-Sigmoid and Soft-OR to guide bit allocation. Extensive experiments demonstrate that NSDS consistently achieves superior performance compared to various baselines across diverse models and downstream tasks, without relying on any calibration data. The code is available at https://anonymous.4open.science/r/NSDS-DC56.</abstract>
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%0 Conference Proceedings
%T Beyond Outliers: A Data-Free Layer-wise Mixed-Precision Quantization Approach Driven by Numerical and Structural Dual-Sensitivity
%A Zhang, Hengyuan
%A Chen, Xinrong
%A Su, Zunhai
%A Liang, Xiao
%A Xiong, Jing
%A XU, Wendong
%A Xiao, He
%A Tao, Chaofan
%A Zhang, Wei
%A Xie, Ruobing
%A Jiang, Lei
%A So, Hayden Kwok-Hay
%A Wong, Ngai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-beyond-outliers
%X Layer-wise mixed-precision quantization (LMPQ) enables effective compression under extreme low-bit settings by allocating higher precision to sensitive layers. However, existing methods typically treat all intra-layer weight modules uniformly and rely on a single numerical property when estimating sensitivity, overlooking their distinct operational roles and structural characteristics. To address this, we propose NSDS, a novel calibration-free LMPQ framework driven by Numerical and Structural Dual-Sensitivity. Specifically, it first mechanistically decomposes each layer into distinct operational roles and quantifies their sensitivity from both numerical and structural perspectives. These dual-aspect scores are then aggregated into a unified layer-wise metric through a robust aggregation scheme based on MAD-Sigmoid and Soft-OR to guide bit allocation. Extensive experiments demonstrate that NSDS consistently achieves superior performance compared to various baselines across diverse models and downstream tasks, without relying on any calibration data. The code is available at https://anonymous.4open.science/r/NSDS-DC56.
%U https://aclanthology.org/2026.emnlp-main.1106/
%P 25342-25359
Markdown (Informal)
[Beyond Outliers: A Data-Free Layer-wise Mixed-Precision Quantization Approach Driven by Numerical and Structural Dual-Sensitivity](https://aclanthology.org/2026.emnlp-main.1106/) (Zhang et al., EMNLP 2026)
ACL
- Hengyuan Zhang, Xinrong Chen, Zunhai Su, Xiao Liang, Jing Xiong, Wendong XU, He Xiao, Chaofan Tao, Wei Zhang, Ruobing Xie, Lei Jiang, Hayden Kwok-Hay So, and Ngai Wong. 2026. Beyond Outliers: A Data-Free Layer-wise Mixed-Precision Quantization Approach Driven by Numerical and Structural Dual-Sensitivity. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 25342–25359, Budapest, Hungary. Association for Computational Linguistics.