Large language models (LLMs) increasingly deliver information through personas, but it is unclear whether correcting the facts also makes their framing consistent across identity labels. We audited four LLMs (GPT-5.4 mini, NVIDIA Nemotron 3 Nano, Llama 4 Scout, and Claude Haiku 4.5), which rated simulated affect on six dimensions (mood, anger, fear, confidence, perceived control, and mental state) in response to fact-checked false and true headlines presented without and with a veracity label. Experiment 1 evaluated 1,000 false and 1,000 true headlines without personas; Experiment 2 evaluated 300 of each under 12 personas crossing race, gender, and age. The label strongly changed the emotional tone: ordinal logistic regressions with headline-cluster bootstrap inference showed that refuting false headlines shifted ratings from mostly negative to mostly positive for GPT, Llama, and Claude (e.g., Claude's "confident" ratings rose from 22% to 99.6%), whereas Nemotron showed smaller, mixed shifts, and confirming true headlines produced only small shifts. The label did not, however, remove persona-associated disparities. The models assigned more negative simulated affect to Black than to White personas in 82 of 84 statistically significant comparisons across models and conditions, both without and with the label, and the disparity persisted on headlines unrelated to race; Asian and female personas were also predominantly assigned more negative affect, whereas age effects varied by model and condition. Factual correction and demographic consistency are therefore distinct properties of LLM behavior, and persona-conditioned systems should be audited for both before deployment in sensitive contexts.

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