arXiv:2606.13317v1 Announce Type: new Abstract: Skill self-evolution methods for LLM agents aim to turn execution trajectories into reusable skill documents, but current pipelines typically learn from one trajectory per task, merge candidate skill patches before checking them, and load the full skill corpus before inference. We propose SkillCAT, a training-free framework that separates this process into three stages. Contrastive Causal Extraction (CCE) samples multiple trajectories for each task and compares same-task success/failure pairs to identify evidence that explains outcome differences
The rapid advancement of large language models is driving research into more sophisticated and autonomous agentic systems capable of continuous learning and adaptation.
Sophisticated LLM agents represent a critical step towards automated workflows and more adaptable AI systems, impacting industries reliant on knowledge work and repetitive tasks.
This framework introduces a more robust and efficient method for LLM agents to evolve and refine their skills, moving beyond single-trajectory learning and improving transferability.
- · AI software developers
- · Automation industries
- · Enterprises adopting AI agents
- · Providers of highly specialized, repetitive white-collar services
- · Legacy automation software
Improved performance and reliability of AI agents across various tasks.
Accelerated adoption of autonomous AI agents in business processes, further collapsing existing workflow layers.
Reconfiguration of white-collar labor markets as advanced AI agents take on more complex roles, leading to demand for new human-AI collaboration skills.
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Read at arXiv cs.CL