Results Gen

This talk argues that current evaluation methods for role-playing language agents (RPLAs) are fundamentally flawed. These methods, which prioritize fluency and personality consistency, fail to detect a dominant failure mode: anachronistic compositing. This occurs when models generate personas that sound like historical figures but reason using knowledge and moral frameworks that postdate them, often due to the overwhelming influence of culturally dominant, later representations in training data. The proposed solution is a shift towards epistemic simulation, emphasizing corpus-boundedness, temporal anchoring, and expert-loop evaluation.