What Drives Representation Steering? A Mechanistic Case Study on Steering Refusal

Applying steering vectors to large language models (LLMs) is an efficient and effective model alignment technique, but we lack an interpretable explanation for how it works-- specifically, what internal mechanisms steering vectors affect and how this results in different model outputs. To investigate the causal mechanisms underlying the effectiveness of steering vectors, we conduct a comprehensive case study on refusal. We propose a multi-token activation patching framework and discover that dif

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ethics.ai (9 April 2026), “What Drives Representation Steering? A Mechanistic Case Study on Steering Refusal,” evidence record 6105, https://ethics.ai/record/6105 (originally published by arXiv).

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