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It is found that while models often track the correct update direction, their quantitative shifts remain heavily under-responsive and both verbalized and logits-based confidence estimates are poorly calibrated compared to the human reference standard.
Cross-Rule Transfer is proposed, a diagnostic framework that audits representational interventions by evaluating them on rule families where the model is natively competent, providing a rigorous methodology to distinguish genuine reasoning repairs from superficial label overrides.
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