Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races

This paper studies safety behavior in repeated multi-agent AI development races involving two to five players. Before interpreting actions strategically, the authors audit the game engine, rule recall, state tracking, payoff calculation, and robustness to equivalent task descriptions. Across seven tested model endpoints, strong rule recall sometimes coexists with weak state tracking and expected-payoff calculation. Verified arithmetic and alternative response formats can change subsequent actions. Trajectory-level behavior also varies substantially by model, risk condition, persona, and race position, while adding competitors does not produce one consistent effect. The authors describe the findings as exploratory and limited to the tested models, prompts, and decoding settings.