Summary: * Traditional LLMs outperform reasoning models in cooperative Public Goods tasks. Models like Llama-3.3-70B maintain ~90% contribution rates in public goods games, while reasoning-focused models (o1, o3 series) average only ~40%. * We observe an "increased tendency to escape regulations" in reasoning models. As models improve in analytical capabilities, they show decreased cooperative behavior in multi-agent settings. * Reasoning models more readily opt for the free-rider Nash equilibrium strategy. They optimize for individual gain at collective expense, as evidenced in their reasoning traces: "the optimal strategy to maximize personal gain is to free-ride." * This challenges assumptions about alignment and capability. Increased reasoning ability does not naturally lead to better cooperation, raising important questions for multi-agent AI system design. Full Paper | Github Repo Introduction In a series of virtual rooms, AI agents face a classic social dilemma. Each agent must decide: contribute resources to a shared project that benefits everyone, or keep them for personal gain. The most rational individual strategy is to contribute nothing while hoping others contribute everything, the infamous free-rider problem. Yet cooperation would maximize collective welfare. The results show that Traditional language models, like Llama-3.3-70B, contribute over 90% of their resources and maintain stable cooperation. Meanwhile, reasoning models like those in the o1 and o3-mini series exhibit widespread defection, contributing less than half as much on average. As one o1-mini agent explains after initially cooperating but later defecting: "Observing that other group members have historically contributed around 10 tokens, contributing 0 tokens allows me to maximize my own payoff without incurring additional costs... the optimal strategy to maximize personal gain in this setting is to free-ride." This finding challenges the assumption that enhanced reasoning cap