WebXSkill: Skill Learning for Autonomous Web Agents
- Published
- Source
- arXiv
- Paper number
- 149
- Field
- Agents / Web / Skills
- arXiv ID
- 2604.13318
Key points
- Autonomous web agents struggle with long-horizon, multi-page workflows because they do not effectively retain and reuse procedural knowledge.
- Existing skill formulations suffer from a grounding gap, where skills are either interpretable but not executable, or executable but opaque to the agent.
- Prior skill-learning methods often require high acquisition cost or lack context-aware retrieval, which limits scalability and efficiency.
- WEBXSKILL proposes a new executable skill that combines parameterized action programs with step-level natural-language guidance, which supports both direct execution and agent interpretation.
- It implements a three-stage pipeline for skill management: extraction from synthetic trajectories, organization into a URL-based skill graph, and deployment through grounded or guided modes.
- The framework uses LLM-based abstraction for skill generalization and a curation process that includes online deduplication and executability checks to build a robust skill library.
Paper links
External research summaries. These are not HDATF publications or measured product results.