From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills
arXiv:2605.23899v1 Announce Type: new Abstract: Language agents increasingly improve by reusing \emph{skills} -- structured procedural artifacts distilled from past experience. In particular, \emph{domain-level} and \emph{model-generated} skills are especially promising. They offer fast adaptation within a domain by encoding domain-specific recurring procedures, and they scale beyond labor-intensive hand-crafting. However, while extraction methods continue to proliferate, understanding remains limited, with no comprehensive study spanning the full skill lifecycle -- \textbf{experience generati
The proliferation of language models and increasing demand for autonomous systems is driving research into more efficient and scalable skill acquisition for AI agents.
This research outlines a pathway for AI agents to self-improve and adapt more rapidly, significantly accelerating their utility and deployment across various domains.
AI agents will become more capable of learning and reusing complex procedures autonomously, reducing reliance on manual programming and enabling faster development cycles.
- · AI software developers
- · Automation companies
- · Businesses adopting AI agents
- · Tasks requiring repetitive human judgment
- · Traditional software development methodologies
Increased agent autonomy will lead to more sophisticated automated workflows in many industries.
The ability of agents to consume and generate skills could create new marketplaces for procedural knowledge.
Self-improving AI agents might accelerate the development of general artificial intelligence by autonomously tackling complex problems.
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Read at arXiv cs.AI