Evolutionary Context Search for Skill Acquisition

TMLR Paper9283 Authors

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 τ2-bench airline by 5%. The evolved contexts are model-agnostic, as those evolved with Gemini-3-Flash transfer effectively to Claude-4.5-Sonnet and DeepSeek-V3.2. 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.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Akanksha_Saran1

Submission Number: 9283