GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation
arXiv:2603.26266v3 Announce Type: replace Abstract: Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction. However, due to insufficient exposure to domain-specific software operation data during training, these agents exhibit significant domain bias - they lack familiarity with the specific operation workflows (planning) and UI element layouts (grounding) of particular applications, limiting their real-world task performance. In this paper, we present GUIDE (GUI Unbiasing via Instructional-Video Driven Expertise), a tr
The rapid advancement and adoption of large vision-language models for GUI agents are quickly revealing practical limitations like domain bias, prompting immediate research into solutions for real-world application performance.
Improving GUI agents' ability to learn and adapt to specific software interfaces will significantly accelerate the deployment of autonomous systems in diverse enterprise and consumer applications, collapsing workflows.
GUI agents can now resolve domain bias through real-time web video retrieval and annotation, moving closer to general-purpose, robust application interaction.
- · AI software developers
- · Enterprise software companies
- · Businesses adopting automation
- · AI consulting firms
- · Tasks requiring manual GUI interaction
- · Legacy automation platforms
Enterprise software usage becomes increasingly agent-driven, reducing human interaction points.
The demand for human-like interactive software testing and user support roles diminishes.
GUI agent capabilities become a critical differentiator for software platforms, driving innovation in interface design for agent compatibility.
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Read at arXiv cs.AI