WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution
Teaching AI agents to remember and reuse what they learn from experience
Researchers built WikiSkill, a system that lets AI agents collect lessons from their past attempts and store them in a shared knowledge base—like a persistent wiki—that improves their problem-solving over time. Agents using WikiSkill solved tasks better than those without, and smaller AI models equipped with evolved skills outperformed much larger models that lacked them.
As AI systems tackle more complex problems, they waste enormous computational effort rediscovering solutions repeatedly. WikiSkill lets agents build and share knowledge efficiently, so each new problem-solving attempt compounds on prior experience. The finding that smaller models with evolved skills beat larger models without them suggests we could accomplish more with less hardware—cutting energy costs and computational waste significantly.