Liyan Tang and colleagues write about WikiSkill, a framework that co-evolves reusable agent skills with a persistent knowledge base to systematically accumulate LLM agent experience. The system separates raw execution experience, accumulated knowledge, and executable skills, continuously consolidating experience into the wiki so subsequent skill updates can build on prior learning.
- Larger models benefit more from evolved skills, while smaller models equipped with skills can outperform substantially larger ones without them
- Skills evolved by one model can outperform self-evolved skills in another, enabling cross-model transfer
- No official code exists; a community member implemented it faithfully, catching two harmful skills via the validation gate and documenting a negative run where the proposer honestly produced no useful skills
- Ablation studies confirm that persistent knowledge accumulation in the wiki is critical for effective skill evolution
- Larger models benefit more from evolved skills, while smaller models equipped with skills can outperform substantially larger ones without them
- Skills evolved by one model can outperform self-evolved skills in another, enabling cross-model transfer
- No official code exists; a community member implemented it faithfully, catching two harmful skills via the validation gate and documenting a negative run where the proposer honestly produced no useful skills
- Ablation studies confirm that persistent knowledge accumulation in the wiki is critical for effective skill evolution
