AdaptEvolve: Uncertainty-Aware Model Selection for Evolutionary AI Agents

AdaptEvolve is a novel framework designed to enhance the efficiency of evolutionary AI agents by intelligently balancing performance and computational cost. The system addresses the high expense of iterative refinement by using intrinsic confidence metrics, such as token entropy, to determine when a task requires a massive language model or a smaller, more efficient one. Rather than relying on rigid rules, it employs a lightweight decision tree and online adaptation to route queries dynamically based on the complexity of the specific generation step. Empirical results demonstrate that this approach can reduce inference costs by nearly 38% while maintaining the vast majority of the accuracy seen in high-capacity models. This research highlights that uncertainty-aware selection is a highly effective strategy for scaling agentic reasoning without necessitating expensive external controllers. #ai #reasoning #llm #agent