# DragOn: A Benchmark and Dataset for Drag-Based GUI Interactions

arXiv: https://arxiv.org/abs/2606.06322 · HTML: https://mlsanity.com/paper/2606.06322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 23 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 18 of 18)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://mlsanity.com/paper/2405.14573/) (2024)
- [WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models](https://mlsanity.com/paper/2401.13919/) (2024)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024)
- [OS-ATLAS: A Foundation Action Model for Generalist GUI Agents](https://mlsanity.com/paper/2410.23218/) (2024)
- [ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use](https://mlsanity.com/paper/2504.07981/) (2025)
- [UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning](https://mlsanity.com/paper/2509.02544/) (2025)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [Aria-UI: Visual Grounding for GUI Instructions](https://mlsanity.com/paper/2412.16256/) (2024)
- [MAI-UI Technical Report: Real-World Centric Foundation GUI Agents](https://mlsanity.com/paper/2512.22047/) (2025)
- [ShowUI-π: Flow-based Generative Models as GUI Dexterous Hands](https://mlsanity.com/paper/2512.24965/) (2025)
- [Surfer 2: The Next Generation of Cross-Platform Computer Use Agents](https://mlsanity.com/paper/2510.19949/) (2025)
- [Beyond Clicking:A Step Towards Generalist GUI Grounding via Text Dragging](https://mlsanity.com/paper/2601.06031/) (2025)

## Abstract (arXiv)

GUI agents - vision-based models that control desktops, web browsers, and mobile devices through graphical user interfaces - promise to automate a wide range of digital tasks. While million-scale datasets have enabled substantial progress on click-grounding, drag grounding (e.g. drag-and-drop, swipe, highlight) data remains an order of magnitude smaller and current models fall short on complex drag-based interactions. We introduce DragOn, a drag grounding benchmark and training dataset covering four domains: text highlighting, cell selection, element resizing and slider manipulation. The dataset comprises 286K training screenshots and 3.5M training tasks, plus a 2000-example held-out evaluation suite. We evaluate proprietary (GPT, Claude) and open-weight (Qwen, Kimi, Holo) models, as well as a Qwen VLM fine-tuned on our training data. Results suggest that our dataset could improve performance of state-of-the-art models on downstream computer-use tasks.