# Label-free Coverage Score

Interpretability tools are usually built inside one model from labeled data and rebuilt for the next. This work carries a refusal direction, a correctness direction and a sentiment probe between models through a ridge-regression map fit only on unlabeled prompts, across 110 ordered pairs from five families and 1B to 72B parameters. A map fit on ordinary questions recovers 54% of refusal steering; adding unlabeled prompts from the tool's operating region raises refusal to 102% and correctness gains to 88% to 98%, while random and orthogonal controls recover at most 1%.

Interpretability tools are usually built inside one model from labeled data and rebuilt for the next. This work carries a refusal direction, a correctness direction and a sentiment probe between models through a ridge-regression map fit only on unlabeled prompts, across 110 ordered pairs from five families and 1B to 72B parameters. A map fit on ordinary questions recovers 54% of refusal steering; adding unlabeled prompts from the tool's operating region raises refusal to 102% and correctness gains to 88% to 98%, while random and orthogonal controls recover at most 1%.