arXiv:2603.25158v5 Announce Type: replace Abstract: Large Language Model (LLM) agents increasingly rely on domain-specific skills, yet manually authoring such skills does not scale, and skills generated purely from parametric knowledge often miss critical operational pitfalls. We introduce Trace2Skill, a framework that consolidates broad execution trajectories in parallel into a unified skill directory through inductive reasoning over agent experience. Trace2Skill supports both deepening existing human-written skills and creating useful skills from weak LLM-generated drafts. Experiments demons
The increasing reliance on domain-specific skills for Large Language Model (LLM) agents, coupled with the limitations of manual skill authoring and purely parametric knowledge, necessitates new frameworks.
This framework addresses a core challenge in scaling autonomous AI agents by enabling the automatic distillation and refinement of skills, leading to more robust and transferable agent capabilities.
The ability to automatically generate and deepen agent skills from execution trajectories significantly reduces the manual effort and expertise required to deploy sophisticated AI systems.
- · AI Agent Developers
- · Enterprises Adopting AI
- · Machine Learning Researchers
- · Automation Software Providers
- · Manual AI Skill Design Services
- · Companies Relying Solely on Generic LLMs
AI agents become more capable and require less human oversight to perform complex tasks.
The proliferation of advanced, specialized AI agents could accelerate automation in white-collar sectors.
This could lead to a significant re-evaluation of knowledge work and a shift towards human-AI collaboration on novel problems.
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Read at arXiv cs.AI