When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games
As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents that publicly commit to actions will honor those commitments. We place LLM agents in repeated $n$-player games with a three-stage protocol that separates private intent, public announcement, and final action, allowing us to identify whether each deviation from a stated announcement was already planned during private deliberation. Evaluating three frontier models across six games in homogeneous and heterogeneous groups over 10 rounds, we report two f
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- LinkedLinked via arxiv author · 85%Jerick Shi →
“When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games”
- LinkedLinked via arxiv author · 85%Terry Jingcheng Zhang →
“When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games”
- LinkedLinked via arxiv author · 85%Bernhard Schölkopf →
“When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games”
- LinkedLinked via arxiv author · 85%Vincent Conitzer →
“When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games”
- LinkedLinked via arxiv author · 85%Zhijing Jin →
“When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games”