RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons

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. Perturbation experiments demonstrate that RACE achieves superior domain specificity compared to gradient-based point estimates. Meanwhile, token-distribution-level results verify the association between the selected neurons and the target domain. Furthermore, its computational overhead is two orders of magnitude lower than that of gradient-based methods.

Artificial Intelligence (cs.AI)

arXiv:2608.24758 [cs.AI]

(or arXiv:2608.24758v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.24758 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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