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When Models Refuse: Political Steerability and Feature Richness as Measures of Ideological Depth

Large language models (LLMs) sometimes refuse to follow benign instructions, such as declining to argue a political position or adopt a stated persona, and such refusals are commonly read as safety guardrails at work. We ask whether they can instead signal a capability deficit: a shortage of the internal representations a model needs to reason from the instructed perspective. To investigate, we introduce ideological depth, a property with two components: (i) a model's ability to follow political instructions without *failure* (steerability), and (ii) the feature richness of its internal political representations, measured with sparse autoencoders (SAEs). Using two widely used openweight LLMs as candidates, we compare interventions based on prompts and activation-steering, and probe political features with publicly available SAEs. We find large, systematic differences: a model that is more steerable in both ideological directions activates ~7.3x more distinct political features, while the other model instead responds with increased refusals. Causally ablating a small, targeted set of political features from the former model reproduces the same feature-poor behavior and drives up refusals. Together, these results indicate that refusals on benign prompts can arise from capability deficits rather than fixed safety rules, and that ideological depth is a measurable property of LLMs that helps predict when a model will refuse.

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