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[2602.16113] Evolutionary Context Search for Automated Skill Acquisition
Summary
The paper presents Evolutionary Context Search (ECS), a novel method for automated skill acquisition in large language models, enhancing performance without retraining.
Why It Matters
ECS addresses a critical limitation in current AI models, which struggle to adapt and acquire new knowledge post-deployment. By introducing a method that identifies effective context combinations, ECS provides a more efficient alternative to traditional fine-tuning and prompt engineering, potentially transforming how AI systems learn and adapt.
Key Takeaways
- ECS improves task performance by discovering non-obvious context pairings.
- The method is model-agnostic, allowing for effective transfer across different AI models.
- ECS offers a cost-effective solution for automated skill acquisition without the need for retraining.
- Empirical results show significant performance improvements on benchmark tasks.
- This approach may reduce reliance on manual prompt engineering.
Computer Science > Neural and Evolutionary Computing arXiv:2602.16113 (cs) [Submitted on 18 Feb 2026] Title:Evolutionary Context Search for Automated Skill Acquisition Authors:Qi Sun, Stefan Nielsen, Rio Yokota, Yujin Tang View a PDF of the paper titled Evolutionary Context Search for Automated Skill Acquisition, by Qi Sun and 3 other authors View PDF HTML (experimental) 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 $\tau$-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 co...
