PICon subjects persona agents to 10 demographic questions, 40 logically chained follow-ups, web verification of entities, and repetition of the initial questions. It evaluates eight groups of 10 agents and 63 humans. No synthetic group exceeds the human descriptive area combining internal, external and retest consistency, although Character.ai exceeds humans on the external axis and Twin 2K 500 and Li et al. on retest. The central result must be decomposed: internal consistency mixes non-contradiction with cooperativeness, so OpenCharacter and Consistent LLM score poorly mainly because of evasive or irrelevant responses. External consistency rewards producing searchable names and explicitly excludes claims about the speaker's own biography; it does not validate whether the agent represents the person. The paper also says NEI labels are excluded, while the public code counts them in the denominator as non-refuted, and an appendix redefines EC as a Wilson bound. Statistical support is weak: n=10 per group, B=3 purported bootstrap resamples, and Welch tests on overlapping cumulative human waves. The public package makes the framework reusable but releases no paper outputs, web evidence or table pipeline. PICon is a useful factual-coherence stress test, not validation of personality, identity, human similarity or fitness to replace participants.
Research question
How to evaluate in black box whether a persona agent maintains factual claims without internal contradictions, without refutation by web evidence, and stable when repeating questions during and between conversations.