AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection

arXiv cs.CL / 4/27/2026

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Key Points

The paper addresses how evolutionary AI agents can balance computational efficiency and reasoning quality when they repeatedly call LLMs during inference.
It proposes AdaptEvolve, which adaptively selects among multiple LLMs at each evolutionary refinement step using intrinsic generation confidence to estimate real-time solvability.
Compared with static routing approaches (heuristics or external controllers), confidence-driven routing better accounts for model uncertainty.
Experiments show an average 37.9% reduction in total inference cost while preserving 97.5% of the accuracy of static large-model baselines, yielding a favorable efficiency–accuracy Pareto frontier.
The authors provide an implementation at the linked GitHub repository for reproducibility and further experimentation.

Abstract

Evolutionary agentic systems intensify the trade-off between computational efficiency and reasoning capability by repeatedly invoking large language models (LLMs) during inference. This setting raises a central question: how can an agent dynamically select an LLM that is sufficiently capable for the current generation step while remaining computationally efficient? While model cascades offer a practical mechanism for balancing this trade-off, existing routing strategies typically rely on static heuristics or external controllers and do not explicitly account for model uncertainty. We introduce AdaptEvolve: Adaptive LLM Selection for Multi-LLM Evolutionary Refinement within an evolutionary sequential refinement framework that leverages intrinsic generation confidence to estimate real-time solvability. Empirical results show that confidence-driven selection yields a favourable Pareto frontier, reducing total inference cost by an average of 37.9% across benchmarks while retaining 97.5% of the upper-bound accuracy of static large-model baselines. Our code is available at https://github.com/raypretam/adaptive_llm_selection.