arXiv:2605.27134v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown rapid progress in mobile GUI navigation. This paper presents a systematic study of data scaling, benchmarking, and reasoning for VLM-based agents in this domain. To facilitate rigorous evaluation, we introduce HyperTrack, a large-scale dataset with over 16000 real-world tasks across more than 650 Chinese mobile applications, along with GUIEvalKit, an open-source toolkit for unified benchmarking of VLMs on offline GUI navigation tasks. Using HyperTrack, we analyze the effects of training data scale on both
The rapid advancement of Vision-Language Models (VLMs) is driving efforts to extend their capabilities to complex, real-world tasks like mobile GUI navigation, a significant step for agentic systems.
This development indicates accelerating progress in AI agents' ability to interact with and automate digital interfaces, a key precursor to collapsing workflows and reducing reliance on human-driven software interaction.
The creation of large-scale datasets and standardized benchmarking tools like HyperTrack and GUIEvalKit provides a systematic way to evaluate and scale VLM performance in mobile environments, enabling faster iteration and improvement.
- · AI agent developers
- · Mobile app developers
- · Automation software providers
- · Consumers of automated services
- · Manual mobile testers
- · Fragmented AI research efforts
- · Companies relying on human-in-the-loop workflows
Improved VLM performance in mobile GUI navigation will lead to more robust and versatile AI agents performing complex digital tasks.
The widespread adoption of such agents could automate significant portions of white-collar work involving digital interfaces, leading to productivity gains and workforce restructuring.
These agents might eventually form the backbone of fully autonomous digital entities capable of self-directed learning and operation across various digital ecosystems, blurring the lines between human and AI interaction.
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Read at arXiv cs.AI