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#skill-refinement News & Analysis

2 articles tagged with #skill-refinement. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles
AIBullisharXiv – CS AI · May 127/10
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SkillEvolver: Skill Learning as a Meta-Skill

SkillEvolver introduces a meta-learning framework that automatically improves AI agent skills through iterative refinement based on real-world deployment failures, achieving 56.8% accuracy on benchmark tasks compared to 43.6% for manually curated skills. The system learns by modifying skill prose and code rather than model weights, enabling seamless integration with any compatible agent without retraining.

AIBullisharXiv – CS AI · Jun 26/10
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SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

Researchers introduce SkillRevise, a framework that automatically refines LLM agent skills through execution-grounded iteration, improving task success rates from 36% to 62% on benchmarks. The approach addresses the cold-start problem in agent development by diagnosing defects from execution traces and applying targeted repairs, while demonstrating strong cross-model transferability.