Seventeen models complete 200 dialogues each, 100 with Nemotron Personas USA and 100 with PersonaMem-v2. The target separates label prediction and generation; Gemini 3.1 Pro Thinking judges subjective metrics and FICR, embedding drift is computed, and a subset is repeated with DeepSeek-V4-Pro.
3,400 target dialogues and more than 30,000 turns. Cross-judge checking uses 46 dialogues and 11 cells; the human pilot covers 59 turn-cells with five annotators. Latent-intent accuracy on PersonaMem ranged from .09 to .80. Enabling reasoning in Gemma-4 increased that accuracy by .466 and .497 by condition. FICR saturated on Nemotron and ranged from .53 to .88 on PersonaMem. The warm-up effect appeared in 16 of 17 models, with GPT-5.5 reversing it.
A simulator and the main judge share the Gemini 3.1 family. The reasoning claim depends on one paired family. Full human validation is still pending. The cross-judge comparison is small. Only English and mainly United States profiles are evaluated. Human-centered does not mean human-validated. It does not demonstrate fidelity to specific real people. It does not establish that small subjective differences are significant.