Researchers have developed GRASP (Gated Regression-Aware Skill Proposer), a method for self-improving large language model (LLM) agents. GRASP treats agent improvement as a sequence of edits to a bounded skill library and checks each new item to ensure it preserves previously correct behavior. The authors evaluate GRASP on two clinical benchmarks, where it outperforms existing methods by significant margins. They attribute the gain to specific components of GRASP rather than the skill writing process itself. --- Why it matters: This matters because self-improving LLM agents are crucial for applications in healthcare and other structured environments, but current methods can lead to silent regressions. GRASP's ability to prevent such regressions could improve the reliability and effectiveness of these agents. Source: https://arxiv.org/abs/2605.29668