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

• URL: http://arxiv.org/abs/2602.11931v1
• Date: Thu, 12 Feb 2026 13:26:56 GMT
• Title: AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection
• Authors: Pretam Ray, Pratik Prabhanjan Brahma, Zicheng Liu, Emad Barsoum,
• Abstract summary: Evolutionary agentic systems intensify the trade-off between computational efficiency and reasoning capability.<n>We introduce AdaptEvolve: Adaptive Selection for Multi-LLM Evolutionary Refinement.
• Score: 14.17960333915609
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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