arXiv:2511.00810v4 Announce Type: replace-cross Abstract: Graphical user interface (GUI) grounding is a key capability for computer-use agents, mapping natural-language instructions to actionable regions on the screen. Existing Multimodal Large Language Model (MLLM) approaches typically formulate GUI grounding as a text-based coordinate generation task. However, directly generating precise coordinates from visual inputs is challenging and often data-intensive. A more intuitive strategy is to first identify instruction-relevant visual patches and then determine the exact click location within t
This paper presents a novel approach to improving GUI grounding accuracy and efficiency, addressing current limitations of MLLMs in interpreting and interacting with graphical user interfaces.
Improved GUI grounding directly enhances the capability of AI agents to autonomously operate computers and software, accelerating the automation of white-collar tasks.
The proposed 'context anchor' method offers a more robust and data-efficient way for AI systems to understand and interact with digital interfaces, moving beyond mere coordinate generation.
- · AI agent developers
- · Automation software companies
- · Knowledge workers adopting AI tools
- · Manual data entry services
- · Traditional RPA providers without advanced AI
- · Software interfaces poorly designed for AI interaction
AI agents become significantly more capable at navigating and using complex software applications.
This leads to accelerated adoption of AI agents across various industries, replacing manual screen-based tasks.
The enhanced agency of AI systems pressures software developers to design interfaces that are both human-friendly and AI-understandable, driving a new era of 'agent-native' applications.
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