@inproceedings{wang-etal-2026-race,
title = "{RACE}: Scalable Statistical Estimation of Functional Consistency in {LLM} Neurons",
author = "Wang, Runyu and
Liu, Bo and
Zhang, Xiaxin and
Han, Yu and
Cao, Jiawei and
Zhang, Xiaoye and
Zhang, Zhe and
Yang, Yifan and
Ping, Peng",
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.1458/",
pages = "33331--33353",
ISBN = "979-8-89176-459-0",
abstract = "Discovering stable neuron behavior across entire domains remains a challenge in mechanistic interpretability. Existing methods often rely on instance-level point estimates or computationally expensive procedures, which either obscure population-level variability or limit scalable domain-wide analysis. We present RACE (Residual Alignment for Consistency Estimation), a forward-pass statistical framework that evaluates the domain-wide functional consistency of Transformer neurons. Compared with gradient-based point estimates, RACE produces neuron rankings that yield more domain-specific effects under perturbation. Token-distribution shifts support the connection between the selected neurons and the target domain, while scoring requires roughly one-hundredth of the computational overhead of the gradient-based methods. Code is available at https://github.com/Nexround/RACE."
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<abstract>Discovering stable neuron behavior across entire domains remains a challenge in mechanistic interpretability. Existing methods often rely on instance-level point estimates or computationally expensive procedures, which either obscure population-level variability or limit scalable domain-wide analysis. We present RACE (Residual Alignment for Consistency Estimation), a forward-pass statistical framework that evaluates the domain-wide functional consistency of Transformer neurons. Compared with gradient-based point estimates, RACE produces neuron rankings that yield more domain-specific effects under perturbation. Token-distribution shifts support the connection between the selected neurons and the target domain, while scoring requires roughly one-hundredth of the computational overhead of the gradient-based methods. Code is available at https://github.com/Nexround/RACE.</abstract>
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%0 Conference Proceedings
%T RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons
%A Wang, Runyu
%A Liu, Bo
%A Zhang, Xiaxin
%A Han, Yu
%A Cao, Jiawei
%A Zhang, Xiaoye
%A Zhang, Zhe
%A Yang, Yifan
%A Ping, Peng
%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 wang-etal-2026-race
%X Discovering stable neuron behavior across entire domains remains a challenge in mechanistic interpretability. Existing methods often rely on instance-level point estimates or computationally expensive procedures, which either obscure population-level variability or limit scalable domain-wide analysis. We present RACE (Residual Alignment for Consistency Estimation), a forward-pass statistical framework that evaluates the domain-wide functional consistency of Transformer neurons. Compared with gradient-based point estimates, RACE produces neuron rankings that yield more domain-specific effects under perturbation. Token-distribution shifts support the connection between the selected neurons and the target domain, while scoring requires roughly one-hundredth of the computational overhead of the gradient-based methods. Code is available at https://github.com/Nexround/RACE.
%U https://aclanthology.org/2026.emnlp-main.1458/
%P 33331-33353
Markdown (Informal)
[RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons](https://aclanthology.org/2026.emnlp-main.1458/) (Wang et al., EMNLP 2026)
ACL
- Runyu Wang, Bo Liu, Xiaxin Zhang, Yu Han, Jiawei Cao, Xiaoye Zhang, Zhe Zhang, Yifan Yang, and Peng Ping. 2026. RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 33331–33353, Budapest, Hungary. Association for Computational Linguistics.