Evaluating the Semantic Specificity of Representation Steering in Language Models

Abstract:Localized Representation Steering (LRS) is widely used to correct reasoning pathologies in large language models. However, standard benchmark evaluations can easily be fooled by superficial label overrides, creating a false impression of reasoning circuit repairs. In this work, we propose Cross-Rule Transfer (CRT), a diagnostic framework that audits representational interventions by evaluating them on rule families where the model is natively competent. Evaluating late-layer LRS for a widespread logical failure, contradiction blindness, reveals that the intervention merely injects a global label bias: applying the steering vector to rules the model already handles correctly (99.6% baseline) degrades performance to 40.4% by forcing false contradiction predictions. We support this diagnosis with four complementary controls (direct logit bias equivalence, control vector label-flipping, cross-model grafting, and early-layer steering checks), providing a rigorous methodology to distinguish genuine reasoning repairs from superficial label overrides.

Computation and Language (cs.CL)

arXiv:2608.29431 [cs.CL]

(or arXiv:2608.29431v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.29431 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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