Gödel Ecology: Productivity-Guided Continuous Co-Evolution of Agents and Evaluators for Collective Recursive Self-Improvement

Self-improving coding agents rely on evolutionary search over their own implementations, with evaluators determining which variants survive. Recent work has begun to co-evolve agents and evaluators, but evaluator updates remain periodic and anchored to fixed criteria. We introduce Gödel Ecology, a framework of collective recursive self-improvement via productivity-guided continuous co-evolution of self-improving agents and evaluators. Evaluators are scored jointly with Co-Clade Metaproductivity of the agent lineages they induce, and a continuously updated soft mixture of evaluator candidates forms an adaptive evaluation landscape. Delayed credit assignment and adversarial interventions are additionally adopted to counter self-reinforcing evaluator drift with reliable external evidence. Together, these interacting populations form an evolving ecology in which agents and evaluation criteria actively interplay and shape one another. Across experiments on coding, math, and writing domains, Gödel Ecology produces stronger evaluator signals, better downstream search decisions, and improvements that transfer across benchmarks and model backbones. These results suggest that continuously adapting the evaluation criteria that determine which directions of improvement are worth pursuing helps make better recursive self-improvement.