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arXiv · 2608.24758

RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons

Runyu Wang·Bo Liu·Xiaxin Zhang·Yu Han·Jiawei Cao·Xiaoye Zhang·Zhe Zhang·Yifan Yang·Peng Ping
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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.

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Runyu Wang, Bo Liu, Xiaxin Zhang, Yu Han, Jiawei Cao, Xiaoye Zhang, Zhe Zhang, Yifan Yang, Peng Ping. 2026-09-01. RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons. https://arxiv.org/abs/2608.24758

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