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This paper introduces PersuasionTrace, a framework for studying multi-turn persuasion in human-LLM interaction, using a Bayesian-network simulated target that models belief updates. The framework reveals that LLMs are persuasive across topics and modalities, and that the Bayesian target better matches human belief dynamics than vanilla LLM simulators.