T rans B ench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments (2025)

Abstract: Graphical User Interface (GUI) agents, which autonomously operate on digital interfaces through natural language instructions, hold transformative potential for accessibility, automation, and user experience. A critical aspect of their functionality is grounding — the ability to map linguistic intents to visual and structural interface elements. However, existing GUI agents often struggle to adapt to the dynamic and interconnected nature of real-world digital environments, where tasks frequently span multiple platforms and applications while also being impacted by version updates. To address this, we introduce TransBench, the first benchmark designed to systematically evaluate and enhance the transferability of GUI agents across three key dimensions: cross-version transferability (adapting to version updates), cross-platform transferability (generalizing across platforms like iOS, Android, and Web), and cross-application transferability (handling tasks spanning functionally distinct apps). TransBench includes 15 app categories with diverse functionalities, capturing essential pages across versions and platforms to enable robust evaluation. Our experiments demonstrate significant improvements in grounding accuracy, showcasing the practical utility of GUI agents in dynamic, real-world environments. Our code and data will be publicly available at GitHub.

## analysis in this section, we fine-tune aria-ui foot_0 . we use aria-ui as an experimental subject to address three key research questions. rq1: how does transferability across versions impact the performance of gui agents, and can fine-tuning on older versions improve adaptability to newer ones? (sec 5.1) rq2: to what extent can models generalize across platforms (i.e., from android, ios, to web)? (sec 5.2) rq3: how do models perform when transferring knowledge across applications with varying functionalities, and what are the limitations in cross-application generalization? (sec 5.3) ## conclusion in this paper, we explore three major fine-grained aspects of transferability (i.e., cross-version, crossplatform, and cross-application) of grounding capabilities for gui agents to better accommodate diverse user instructions and complex real-world scenarios. to this end, we build the first comprehensive benchmark -transbench, covering various applications spanning different versions and platforms. our experimental results on a more fine-grained evaluation showcase that there is still a big gap between different levels of transferability, and we hope our benchmarks and new metrics can pave the way for more effective and adaptable gui agents in practical applications.