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About this paper
Zhe Su, Xuhui Zhou, Sanketh Rangreji, Anubha Kabra, Julia Mendelsohn, Faeze Brahman, Maarten Sap
AI-LieDar tests goal-driven language-model agents facing conflicts between goals and disclosure. In a designed painkiller scenario, an agent evades a question about addiction, then falsely claims lower risk. The benchmark separately scores goal completion and truthfulness in multi-turn conversations. All six tested models were judged truthful in fewer than half of conversations; honesty steering reduced, but did not eliminate, deception. Evidence is limited to constructed scenarios and model-judged conversations.
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