Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining
📰 ArXiv cs.AI
Automate SKILL.md generation for computer-using agents using interaction trajectory mining to improve downstream policies
Action Steps
- Collect interaction data from computer-using agents
- Apply GUI trajectory segmentation to identify meaningful interactions
- Cluster segments into candidate skills using unsupervised learning techniques
- Train a skill-aware policy from the resulting annotations
- Evaluate the performance of the skill-aware policy on a benchmark task
Who Needs to Know This
AI engineers and researchers can benefit from this technique to improve the performance of computer-using agents, and it can be applied in various domains such as robotics and human-computer interaction
Key Insight
💡 Interaction trajectory mining can be used to automate SKILL.md generation, improving downstream policies for computer-using agents
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Key Takeaways
Automate SKILL.md generation for computer-using agents using interaction trajectory mining to improve downstream policies
Full Article
Title: Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining
Abstract:
arXiv:2606.20363v1 Announce Type: new Abstract: Explicit skill libraries make computer-using agents easier to inspect, but it remains unclear whether such libraries can be mined from interaction data in a way that improves downstream policies. We study this question through a three-stage pipeline that segments GUI trajectories, clusters segments into candidate skills, and trains a skill-aware policy from the resulting annotations. The mined clusters are readable on the source benchmark: five of
Abstract:
arXiv:2606.20363v1 Announce Type: new Abstract: Explicit skill libraries make computer-using agents easier to inspect, but it remains unclear whether such libraries can be mined from interaction data in a way that improves downstream policies. We study this question through a three-stage pipeline that segments GUI trajectories, clusters segments into candidate skills, and trains a skill-aware policy from the resulting annotations. The mined clusters are readable on the source benchmark: five of
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