3 articles tagged with #skill-extraction. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce Anything2Skill, a framework that converts external knowledge sources into reusable, executable skills for AI agents. By combining skill extraction with retrieval-augmented generation, the system achieves 98.85% success on command-line tasks and 94.10% on GitHub operations, significantly outperforming RAG-only approaches.
Researchers developed a three-stage pipeline to automatically extract skill libraries from computer-using agent interaction data, achieving high readability (95% purity on labeled benchmarks) but failing to improve downstream policy performance across domains. The study reveals that while trajectory mining can expose interpretable skill structure, current technical limitations prevent reliable cross-domain transfer improvements.
Researchers propose LifeSkill, a reinforcement learning framework that enables LLM agents to continuously learn and adapt during test-time interactions rather than relying on static parameters. The system combines skill extraction with real-time parameter updates, achieving 7% performance improvement over existing lifelong learning baselines on benchmark tasks.
