arXiv:2606.27909v1 Announce Type: cross Abstract: 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-
The paper was published on arXiv, indicating ongoing academic research and development in AI theory of mind, specifically addressing limitations of current evaluation methods.
This research indicates a significant step towards developing AI models with more sophisticated understanding of complex social incentives, crucial for advanced agentic systems.
Current theory-of-mind evaluations are challenged, leading to new benchmarks and potentially more robust AI agents capable of navigating multi-hop social dilemmas.
- · AI researchers
- · Developers of AI agents
- · Companies investing in advanced AI
- · LLMs with superficial social reasoning
- · Simpler AI evaluation methodologies
More accurate and nuanced evaluations of LLM social reasoning capabilities will emerge.
This could lead to the development of highly sophisticated AI agents capable of complex strategic interaction in diverse environments.
Such agents might redefine human-AI collaboration and autonomous system behavior in fields like negotiation, gaming, and potentially even diplomacy.
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Read at arXiv cs.AI