# MobileViews: A Million-scale and Diverse Mobile GUI Dataset

arXiv: https://arxiv.org/abs/2409.14337 · HTML: https://mlsanity.com/paper/2409.14337/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- First posted on arXiv 22 Sep 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)
- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Built on by (most cited first, 4 of 4)

- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [Every Software as an Agent: Blueprint and Case Study](https://mlsanity.com/paper/2502.04747/) (2025)
- [Software Engineering for and with GUI Agent](https://mlsanity.com/paper/2608.09278/) (2026)
- [GAIR: GUI Automation via Information-Joint Reasoning and Group Reflection](https://mlsanity.com/paper/2512.09396/) (2025)

## Abstract (arXiv)

Visual language models (VLMs) empower mobile GUI agents to interpret complex mobile screens and respond to user requests. Training such capable agents requires large-scale, high-quality mobile GUI data. However, existing mobile GUI datasets are limited in scale, data comprehensiveness, and fidelity. To overcome this, we utilize two mobile SoC clusters to provide over 200 native, high-fidelity mobile environments, along with a VLM-enhanced automatic application traversal framework for highly parallel, automated dataset collection with minimal human intervention. With this system, we propose MobileViews, a million-scale mobile GUI dataset comprising over 1.2 million unique screenshot-view hierarchy pairs from more than 30K modern Android applications. We assess the effectiveness of MobileViews by training four VLMs using the reinforcement learning-based GUI grounding task and evaluating them on two representative GUI grounding benchmarks. Results show that MobileViews significantly enhances grounding accuracy by up to 6.1%. Further analysis of data scale and quality underscores the critical role of large, high-quality datasets as reliable sources for training mobile GUI agents. The MobileViews dataset is publicly available at https://huggingface.co/datasets/mllmTeam/MobileViews.