MUSE-Autoskill
A self-evolving agent framework that treats skills as long-lived, testable assets with creation, memory, management, evaluation, and refinement.
How can self-evolving skill libraries avoid stale, conflicting, or unsafe procedures while preserving cumulative performance gains?
MUSE-Autoskill focuses on a practical form of self-improvement: reusable skills. Rather than treating skills as static prompt snippets, the framework gives them a lifecycle: creation, memory, management, evaluation, and refinement.
Why it matters
Skill-level memory lets an agent accumulate experience around each reusable capability. The paper also emphasizes unit tests and runtime feedback, which are necessary if skills are going to improve without becoming stale or unsafe.
ASI relevance
Recursive improvement does not have to start by rewriting model weights. It can begin with agents accumulating, testing, and refining the procedures they use to solve tasks.