Evaluating LLM Social Cognition Through Multi-Agentic Strategic Games
TL;DR
A strategic game arena is introduced that situates LLM agents in social deduction scenarios inspired by Among Us, requiring theory of mind, deception detection, and cooperative deliberation under uncertainty, and motivates architectural changes for virtual agents that must cooperate with humans.
Abstract
Large language models (LLMs) now power the reasoning core of intelligent virtual agents deployed across an expanding range of social settings, from tutoring students and supporting patients in healthcare, to mediating group discussions and representing humans in various social settings. Effective deployment demands social cognition, the capacity to model what others believe, detect deception, and coordinate strategic action under incomplete information. These capacities, exemplified in the social dynamics of the game Among Us, remain poorly characterized in current LLM evaluation frameworks. We introduce a strategic game arena that situates LLM agents in social deduction scenarios inspired by Among Us, requiring theory of mind, deception detection, and cooperative deliberation under uncertainty. We evaluate 19 open-weight models across 10,134 games and 289,614 utterances, testing both homogeneous and heterogeneous crews. Our experiments reveal three findings. First, crewmates voting through generative reasoning reach only \(50.4\% \pm 4.4\%\) F1 when identifying imposters, while a logistic regression classifier trained on the same discussion transcripts achieves \(85.3\%\) F1. Second, scaling model parameters yields a statistically significant but practically marginal improvement. Medium models (60–82B) reach \(52.8\%\) F1 against \(46.1\%\) for small models (7–20B), a 6.7 point gain (Mann–Whitney U, p = 1.5 × 10− 34). Third, agents fail to integrate evidence coherently during deliberation. Imposters self-incriminate in \(4.12\%\) of their statements, yet crewmates eject the confessing agent only \(33.8\%\) of the time. Crewmates reverse their stated suspect between consecutive rounds without new justification in \(41.9\%\) of cases. Sentiment remains uniformly neutral whether an agent is reporting a body or delivering a routine update. These gaps identify concrete limits in the social cognition of current LLM-powered agents and motivate architectural changes for virtual agents that must cooperate with humans. The source code and the live arena viewer are available at https://ufdatastudio.com/projects/agents-among-us.
