SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent

LLM agents that handle recurring tasks often build up reusable "skills"—textual knowledge stored without retraining weights—but current methods for refining these skills lack proper diagnostic feedback and treat deletion carelessly. SkillProx introduces a proximal-gradient-inspired process that diagnoses failures, rolls back unsuccessful edits, and selectively consolidates or removes skill components based on measured utility. This could improve agentic systems that operate over long deployments, such as customer service bots or coding assistants, by making their accumulated procedural knowledge more accurate and efficient over time, ultimately boosting task accuracy across varied benchmarks and multiple backbone LLMs. Paper: https://arxiv.org/abs/2608.07449