October 3, 2025Open Access

Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations

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The PersonaEvolve algorithm achieved an 84% average reduction in distributional divergence in simulations.
Refined personas generalize well to novel scenarios, improving overall behavioral realism and reliability.
The framework addresses the behavior-realism gap found in generative agents' performance.
It provides a structured approach to developing trustworthy LLM-driven social simulations.

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The study proposes Persona-Environment Behavioral Alignment framework, enhancing language agent behavior in high-stakes scenarios.

Wang et al. (2025) studied this question.

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