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Eye on AI Weekly Research Watch · October 7 · 2 min

FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution

LLM-guided evolutionary methods like AlphaEvolve tackle hard optimization problems, but they are usually judged on gain per iteration rather than per dollar. FrugalEvo pairs a stronger, costlier LLM that proposes solution strategies with a cheaper one that implements and refines the code, plus a harness designed to maximize prompt-prefix cache reuse. The authors introduce BA-AUC to measure quality under a fixed budget. It matches or beats baselines on ten optimization tasks and sets a new circle-packing result for under two dollars. Authors: Hui Chen, Xuan Qi, James Xu Zhao, Zhaopeng Feng, Shilong Liu, Kuang Xu, Pang Wei Koh, Bryan Hooi Paper: https://arxiv.org/abs/2610.03675v1

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LLM-guided evolutionary methods like AlphaEvolve tackle hard optimization problems, but they are usually judged on gain per iteration rather than per dollar. FrugalEvo pairs a stronger, costlier LLM that proposes solution strategies with a cheaper one that implements and refines the code, plus a harness designed to maximize prompt-prefix cache reuse. The authors introduce BA-AUC to measure quality under a fixed budget. It matches or beats baselines on ten optimization tasks and sets a new circle-packing result for under two dollars.

Authors: Hui Chen, Xuan Qi, James Xu Zhao, Zhaopeng Feng, Shilong Liu, Kuang Xu, Pang Wei Koh, Bryan Hooi

Paper: https://arxiv.org/abs/2610.03675v1

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