arXiv:2605.21082v1 Announce Type: new 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. Prior to LLMs, traditional Robotic Process Automation (RPA) offers runtime efficiency but demands significant manual effort to develop and maintain. To bridge this gap, we propose AutoRPA, a framework that au
The rapid advancement of Large Language Models (LLMs) and their integration with Robotic Process Automation (RPA) is creating new possibilities for automated interaction with graphical user interfaces.
This development indicates a significant step towards more efficient and autonomous software agents, potentially transforming how businesses automate repetitive tasks and manage digital workflows.
The prior inefficiency of LLM-based agents in repetitive tasks and the high manual effort of traditional RPA can now be bridged by frameworks like AutoRPA, leading to more scalable and flexible automation.
- · LLM developers
- · RPA providers
- · Businesses with complex digital workflows
- · AI agent developers
- · Tasks requiring manual GUI interaction
- · Traditional RPA consultancies focused solely on manual scripting
Companies will adopt more sophisticated automation tools that combine LLM intelligence with RPA efficiency.
This will lead to significant productivity gains and a reevaluation of white-collar job functions involving repetitive digital tasks.
The increased adoption of LLM-driven automation could accelerate the development of truly autonomous AI agents capable of managing complex business operations with minimal human oversight.
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Read at arXiv cs.AI