---
title: "RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.24758v1"
permalink: "https://feed.astralor.com/items/itm_75738d1c7bb3e475afdca1b2"
published: "2026-08-25T15:57:43.000Z"
score: 72
channel: "tech"
tags: ["RACE","Transformer","Mechanistic Interpretability","Statistical Estimation"]
---

# RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons

## AI commentary

> What stands out here is replacing expensive gradient computation with forward-pass statistics, achieving two orders of magnitude efficiency gain—very practical for researchers doing large-scale neuron analysis. If you work on interpretability or alignment, this framework is worth a look.

## At a glance

Mechanistic interpretability faces the challenge of identifying stable neuron behavior across entire domains. Existing methods often rely on instance-level point estimates or computationally expensive procedures, which obscure population-level variability or limit scalable analysis. RACE proposes a forward-pass statistical framework using residual alignment to estimate functional consistency of Transformer neurons across domains. Perturbation experiments show RACE achieves superior domain specificity compared to gradient-based point estimates, and token-distribution results verify the association between selected neurons and target domains. Computational overhead is two orders of magnitude lower than gradient-based methods.

## Key points

- RACE uses forward passes and residual alignment to evaluate neuron functional consistency without gradient computation.
- It outperforms gradient-based point estimates in domain specificity and validates neuron-domain associations at the token distribution level.
- Computational overhead is two orders of magnitude lower than gradient-based methods, enabling scalable domain-wide analysis.

## Full text

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.

---

Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_75738d1c7bb3e475afdca1b2