AI Research
Token Efficient Task Execution via Application Behavior Modeling for Web Agents
Reducing the massive token overhead in automated web workflows through structural application modeling.
The strong performance of AI Agents across an impressive variety of tasks is driving an unprecedented investment in agentic infrastructures, however the cost of processing tokens is fast increasing. Web agents automate the execution of web-application tasks described in natural l...
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ArXiv CS.AI
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ArXiv CS.AI
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Generated insight by OnAirToday AI
By modeling how web applications behave rather than just consuming raw tokens, developers can significantly reduce the computational overhead of AI-driven automation. This advancement addresses the critical scalability hurdle currently facing high-cost agentic workflows in production environments.
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