AutoRPA: Efficient GUI Automation through LLM-Driven Code Synthesis from Interactions
The article discusses AutoRPA, a framework designed to enhance GUI automation using large language models (LLMs). It aims to improve efficiency in repetitive tasks by converting LLM-driven interactions into robust robotic process automation (RPA) functions. The framework demonstrates significant reductions in token usage and improvements in runtime efficiency across various GUI environments.
- ▪AutoRPA automatically distills decision logic from LLM agents into RPA functions.
- ▪The framework includes a translator-builder pipeline and a hybrid repair strategy for code verification.
- ▪Experiments show that AutoRPA reduces token usage by 82% to 96%, enhancing efficiency and reusability.
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Computer Science > Artificial Intelligence arXiv:2605.21082 (cs) [Submitted on 20 May 2026] Title:AutoRPA: Efficient GUI Automation through LLM-Driven Code Synthesis from Interactions Authors:Minghao Chen, Xinyi Hu, Zhou Yu, Yufei Yin View a PDF of the paper titled AutoRPA: Efficient GUI Automation through LLM-Driven Code Synthesis from Interactions, by Minghao Chen and 3 other authors View PDF HTML (experimental) Abstract:Large Language Model (LLM) based agents have demonstrated proficiency in multi-step interactions with graphical user interfaces (GUIs). While most research focuses on improving single-task performance, practical scenarios often involve repetitive GUI tasks for which invoking LLM reasoning repeatedly, i.e., the ReAct paradigm, is inefficient.
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