SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent
Teaching AI agents to build and refine their own instruction libraries
When AI language models tackle similar tasks repeatedly, they can build up a library of reusable instructions—called skills—that live in the model's working memory rather than changing its weights. A new system called SkillProx improves how these skills evolve by adding explicit diagnosis of what goes wrong and a structured way to prune unhelpful knowledge, achieving 3 percentage points higher accuracy than previous methods across multiple benchmark tests.
As AI agents take on more complex real-world tasks, the ability to learn and refine their own strategies becomes critical. SkillProx makes this learning process more transparent and efficient—you can audit which knowledge pieces actually help—and shows the approach generalizes to tasks the system has never seen before. This points toward AI systems that improve themselves more reliably without expensive retraining.