Evolutionary Context Search for Automated Skill Acquisition

• URL: http://arxiv.org/abs/2602.16113v1
• Date: Wed, 18 Feb 2026 00:47:02 GMT
• Title: Evolutionary Context Search for Automated Skill Acquisition
• Authors: Qi Sun, Stefan Nielsen, Rio Yokota, Yujin Tang,
• Abstract summary: We introduce Evolutionary Context Search (ECS), an evolutionary method that searches context combinations using accuracy on a small development set.<n>Our empirical results show that ECS improves BackendBench by 27% and $$-bench airline by 7%.
• Score: 22.405645674869433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models cannot reliably acquire new knowledge post-deployment -- even when relevant text resources exist, models fail to transform them into actionable knowledge without retraining. Retrieval-Augmented Generation attempts to bridge this gap by surfacing relevant documents at inference time, yet similarity-based retrieval often fails to identify context that actually improves task performance. We introduce Evolutionary Context Search (ECS), an evolutionary method that searches context combinations using accuracy on a small development set, requiring only inference calls without weight updates. ECS moves beyond semantic similarity to discover non-obvious context pairings that significantly boost performance. Our empirical results show that ECS improves BackendBench by 27\% and $τ$-bench airline by 7\%. The evolved contexts are model-agnostic, as those evolved with Gemini-3-Flash transfer effectively to Claude Sonnet and DeepSeek. This suggests that ECS opens a path toward automated context discovery for skill acquisition -- an efficient alternative to manual prompt engineering or costly fine-tuning.

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