Narrow-domain finetuning makes vision-language models (VLMs) sound like domain experts while quietly making them less attentive to the image. We attribute this gap to a persistent domain prior: an input-agnostic shift in hidden state, installed by narrow finetuning, that biases the model toward domain-style outputs regardless of visual evidence. To test the claim we introduce ActDiff, a white-box diagnostic that isolates the finetuning trace by differencing base and finetuned hidden states under progressively degraded images. The trace is compact and persists under blank and Gaussian-noise inputs; the finetuned model is correspondingly less, not more, disrupted when the image is destroyed (gap 0.323 ± 0.036 nats on BIOMED, generic eval). Subtracting the trace at inference recovers 54% of the MMStar accuracy collapse on BIOMED, with a peak-and-collapse dose response that random and wrong-domain controls do not produce. A single first-moment regulariser added at training time shrinks the trace 34x and improves generic NLL. The prior thus has a clean diagnostic, a causal handle at inference, and a one-line mitigation at training.