Triadic Werewolf: A Jester Role for Multi-Hop Theory of Mind in LLMs
Theory-of-mind evaluations of large language models typically use dyadic social-deduction games, where every observable cue points to a single hidden side, so a model with strong language priors can score well without ever simulating opponents' incentives. We extend the Werewolf game with a Jester, a third faction whose utility on peer suspicion is inverted because it wins by being voted out, so optimal play requires reasoning across three opposing utility functions. Across 60 games on GPT-4.1, DeepSeek-V3.1, and Llama-3.3-70B with Jester self-learning on and off, the Jester wins 60-70% of games while Werewolves never exceed 20%, and GPT-4.1 wolves vote the Jester out on day 1 in 60-70% o...
| Authors | Avni Mittal |
|---|---|
| Primary category | cs.CL |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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