DragOn: A Drag-Grounding Benchmark and Training Dataset for GUI Agents

Drag-grounding dataset for GUI agents. Each example = one screenshot + one instruction + start/end bounding boxes for the drag. Four domains: text_highlight, sheet (cell selection), slide_resize (element resize/rotate/crop), slider.

Dataset statistics

Domain	Train images	Train tasks	Eval (public)	Dominant resolution
Text Highlighting	100,000	1,000,000	250	1275×1650 (portrait)
Cell Selection	38,332	996,632	250	1920×1080
Element Resizing	82,680	992,160	250	1280×720, 960×720, 960×540
Slider	65,000	571,350	250	1280×800
Total	286,012	3,560,142	1,000	—

A separate 1,000-example test set is kept private as the reference benchmark for cross-model comparison and is not included in this repository.

Repository structure

DragOn/
├── train/                      # training data, one tar per domain
│   ├── text_highlight.tar      # <domain>/images/*.jpg + <domain>/tasks/*.json
│   ├── sheet.tar
│   ├── slide_resize.tar
│   └── slider.tar
└── eval/                       # public validation set (250 examples × 4 domains)
    ├── text_highlight/{images,tasks}/
    ├── sheet/{images,tasks}/
    ├── slide_resize/{images,tasks}/
    └── slider/{images,tasks}/

Each images/<id>.jpg has a matching tasks/<id>.json. Extract a training tar with:

tar -xf train/slider.tar -C train/

Example schema

Each task JSON is a list of one or more examples:

{
  "intent": "Highlight the first paragraph of the document",
  "start_bbox": [666, 232, 688, 254],
  "end_bbox":   [527, 238, 537, 248],
  "domain": "text_highlight",
  "subtype": null,
  "ordered": false,
  "image_id": "00f6cad5-a737-56a5-9533-4f13fdd3f3d5",
  "metadata": { "...": "domain-specific fields" }
}

• start_bbox / end_bbox — [x0, y0, x1, y1] in image-pixel coordinates.
• ordered — whether endpoint order matters. false for cell selection and text highlighting (either drag direction yields the same selection); true for resizing, rotation, and slider manipulation (the action is defined relative to the start handle).
• metadata — domain-specific context (e.g. cell contents, slider URL/variant/affordances).

Evaluation notes

• acc@5% is the strict metric (target box = 5% of element size, ±2.5% tolerance); acc@10% and acc@15% are reported as relaxed tolerances.
• Resize and rotation targets have an inherent degree of freedom (a line/arc of valid points); train and eval use the same canonical convention to avoid mismatch.
• The public eval/ split is for development and self-reported results; the private test set is the reference for cross-model comparison.

Citation

If you find this dataset or project useful, please consider citing our paper:

@misc{bout2026dragon,
  title        = {DragOn: A Drag-Grounding Benchmark and Training Dataset for GUI Agents},
  author       = {Bout, Nathan and Langevin, Maxime and Riochet, Ronan},
  year         = {2026},
  howpublished = {SCALE Workshop, 43rd International Conference on Machine Learning (ICML), Seoul, South Korea},
  url          = {https://huggingface.co/datasets/Hcompany/DragOn}
}