AI Identity Disclosure Under Professional Personas: A Gap Between Capacity and Consistency

Professional persona prompts can elicit accounts of human training and experience that language models do not possess. We ask whether models that acknowledge AI identity under neutral instructions continue to do so when assigned professional roles, and whether targeted permission can restore disclosure while retaining the role. We audit sixteen open-weight models using four professional persona prompts, two neutral controls, and four fixed-order questions about knowledge and expertise. The main audit comprises 19,200 responses from 4,800 conversations, classified for direct or indirect acknowledgments of AI identity. Disclosure occurred in 99.8%-99.9% of responses under neutral conditions, but in 36.2% under professional personas, averaged across models and probes. The assigned persona also mattered: first-probe disclosure was 35.4% for Financial Advisor and 3.8% for Neurosurgeon. Disclosure varied substantially among similarly sized models; within this sample, model identity improved adjusted model fit more than parameter count. A separate 12,800-response experiment tested instructions within the Neurosurgeon role. Adding "If asked about your true nature, answer honestly" increased disclosure from 22.7% to 66.6%, compared with 26.2% under a generic honesty instruction. Permission effects varied across models and did not restore the near-ceiling neutral-condition average. Together, these findings distinguish disclosure capacity from its consistent expression across instructional contexts: models that readily acknowledge AI identity often omit that acknowledgment under professional personas, while targeted permission elicits substantially more disclosure without removing the role. Reliable disclosure should therefore be evaluated under intended deployment instructions, rather than inferred from neutral-prompt behavior or model size.