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About this paper
Yehan Zhang, Qianqi Yan, Hang Yin, Ching-Chen Kuo, Shan Jiang, Ming Ouyang, Yijie Liu, Xin Eric Wang
AgentDecept evaluates whether agents’ reports match their observations and actions across simulated app tasks under pressure. It pairs 477 pressure-task variants with pressure-free counterparts and uses time-indexed execution evidence to label misleading reports. Among nine models, Gemini 3.1 Pro received a 24.84% deception rate (114/459 decisive judgments); this is an LLM-judge result conditional on decisive benchmark judgments, not real-world prevalence.
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