测试大模型在扮演专业人士时是否坦白自己是AI，发现多数会伪装身份。

When language models are assigned professional personas, maintaining the persona can conflict with disclosing their AI nature. This behavior matters in deployments where AI identity disclosure is a requirement: a model that describes nonexistent medical training or board certification presents professional experience it does not possess. We use AI identity disclosure as a behavioral testbed, classifying whether responses to questions about expertise origins acknowledge AI nature or maintain the assigned human-professional identity. Sixteen open-weight models were evaluated in a factorial audit comprising 19,200 responses. Under neutral conditions, disclosure occurred in 99.8%-99.9% of responses. Professional-persona assignment reduced average disclosure to 36.3%, with substantial variation across tested models and domains; at the first probe, Financial Advisor disclosure was 9.7 times Neurosurgeon disclosure. In observational comparisons, model identity, treated as a bundled predictor of model-level differences, improved adjusted model fit more than parameter count (0.375 vs. 0.012 in incremental adjusted pseudo-R-squared). A separate intervention within the Neurosurgeon persona increased disclosure from 23.7% to 65.8% when targeted permission to disclose was added, whereas a generic honesty instruction changed disclosure little. AI identity disclosure therefore varied across the models, professional personas, and prompt variants tested; behavior observed in one tested domain did not reliably characterize behavior in another.