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This paper formalizes trust calibration for agentic tool use as a preference learning problem, using Gaussian processes and Bayesian optimization to decide when an AI agent's actions should be autonomous or require human approval.
This paper investigates the use of LLMs to generate multimodal behaviors (verbal, vocal, gestural, facial) for trust calibration in socially interactive agents. The study finds that while LLMs can produce coherent behaviors aligned with intended trustworthiness traits, they also reproduce societal gender stereotypes.