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New 'Triadic Werewolf' Game Tests LLM Multi-Agent Reasoning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new multi-hop theory of mind evaluation for large language models called Triadic Werewolf. This game extends the traditional Werewolf game by introducing a "Jester" role with inverted win conditions, requiring models to reason across three opposing utility functions. In tests with GPT-4.1, DeepSeek-V3.1, and Llama 3.3 70B Instruct, the Jester role proved highly successful, winning 60-70% of games, while the Werewolf faction rarely exceeded 20%. Notably, GPT-4.1 struggled, often voting out the Jester prematurely, indicating a weakness in this complex multi-agent reasoning scenario. AI

IMPACT This new evaluation method could reveal deeper insights into LLM reasoning capabilities beyond current benchmarks.

RANK_REASON The cluster contains a research paper detailing a new evaluation method for LLMs.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New 'Triadic Werewolf' Game Tests LLM Multi-Agent Reasoning

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Avni Mittal ·

    Triadic Werewolf: A Jester Role for Multi-Hop Theory of Mind in LLMs

    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 simulat…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Avni Mittal ·

    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 …

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