It Takes Two: Co-Evolving Social Worlds and Agent Architectures

The worlds in which large language model agents operate are becoming increasingly complex and open-ended, placing growing demands on the architectures that organize their reasoning, memory, and action. However, agent architectures are still typically designed or optimized in fixed worlds that quickly saturate, weakening the evolutionary pressure needed to discover stronger structures and strategies. We propose WoArc, a world–architecture co-evolution framework that makes the world itself an adaptive part of agent design, allowing worlds to evolve around the limitations of current architectures and architectures to evolve against the resulting challenges. WoArc closes this loop by letting current architectures guide the search for informative new worlds, accepting challenges through method-agnostic criteria, and feeding them back into executable architecture evolution, while a resolution-calibrated controller decides whether to keep evolving architectures, refresh the worlds, or stop. Experiments on hidden-information social interactions show that world evolution restores meaningful discrimination among increasingly capable agents, while the resulting architectures outperform published alternatives on test interactions. Our code and the evolved benchmark are available at https://anonymous.4open.science/r/WoArc-29CC.