Social dilemmas lose their fixed rules: OpenSocialJax samples hidden mechanics from tens of millions of configurations, and eight LLMs still cannot hold a shared resource
OpenSocialJax extends Clean Up and Commons Harvest into an open-ended distribution: each episode samples one of tens of millions of hidden configurations and holds it fixed across five trials, so agents must infer tool recipes, payoffs and regrowth rules through interaction. Eight large language models were evaluated in homogeneous teams and a mixed arena; rule discovery often improved performance but did not reliably sustain cooperation, with the cleaning burden concentrating among fewer agents in OpenCleanup and every team exhausting the shared resource in OpenHarvest. In the mixed arena, one cooperative agent sustained the public good in OpenCleanup at a cost to its own return, but could not prevent resource depletion in OpenHarvest.