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  "text": "// Skill Learning for Autonomous Web Agents //\n\nWeb agents can navigate a page, but ask them to repeat a checkout flow they already completed, and they start from scratch every time.\n\nThis work introduces WebXSkill, a skill learning framework where web agents extract reusable skills from synthetic trajectories.\n\nEach skill pairs a parameterized action program with step-level natural language guidance, making it both executable by the runtime and interpretable by the agent.\n\nTwo deployment modes let the agent either auto-execute skills as atomic tool calls (grounded mode) or follow them as step-by-step instructions while retaining autonomy to adapt (guided mode).\n\nResults:\n\n- On WebArena, WebXSkill improves task success rate by up to 9.8 points over baselines (69.5% vs 59.7%).\n\n- On WebVoyager, grounded mode reaches 86.1%, a 14.2-point gain over vanilla agents. Skills even transfer across environments: guided mode using only WebArena-extracted skills scores 85.1% on WebVoyager.\n\nStronger models benefit more from grounded execution, while weaker models gain more from guided mode, suggesting the deployment strategy should match model capability.\n\nPaper: https://t.co/KAMYMLXywg\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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