CaptchaArena is released for non-commercial academic research only (CC-BY-NC-4.0); commercial use is prohibited. Data sharing has not started yet — we will begin granting access once our paper is available on arXiv. You are welcome to submit a request now; requests will be reviewed after the paper is released.

⏳ Access is not open yet. We will start sharing CaptchaArena once our paper is posted on arXiv — access requests will be reviewed at that time, so please check back after the paper release. By requesting access you agree to use the dataset solely for non-commercial academic research; any commercial use is prohibited. Requests are reviewed by the dataset authors.

CaptchaArena

CaptchaArena is a multimodal benchmark for evaluating GUI / computer-use agents on their ability to solve CAPTCHA puzzles. It spans 20 CAPTCHA task families (point-and-click, counting, rotation alignment, drag-to-fit, press-and-hold, connect-the-icons, …), each rendered as one or more screenshot images with an instruction and a machine-checkable ground-truth answer, so agent solutions can be scored automatically. Held-out Val / Test splits give a standardized evaluation setting, with a larger Train split also included.

⚠️ Access & use: This dataset is gated and released for non-commercial academic research only — commercial use is prohibited (CC-BY-NC-4.0). Request access above and agree to the terms.

🔗 Code: github.com/X0X0X00/CaptchaArena — puzzle generation, mock-solving, and evaluation.

Task families (20)


Bingo	Click_Order	Connect_Icon	Coordinates
Dart_Count	Dice_Count	Geometry_Click	Hold_Button
Image_Matching	Image_Recognition	Misleading_Click	Object_Match
Patch_Select	Path_Finder	Pick_Area	Place_Dot
Rotation_Match	Select_Animal	Slide_Puzzle	Unusual_Detection

Each family is a distinct CAPTCHA style: some are single-image / single-step (e.g. Bingo, Geometry_Click), others are multi-image / multi-step (e.g. Patch_Select, Connect_Icon, Slide_Puzzle).

Counts

	Train	Val	Test	Total
Puzzles	42,000	4,000	4,000	50,000
Image files (PNG)	67,332	9,935	9,867	87,134

2,100 puzzles per task in Train, 200 per task in Val and Test, across all 20 families.
Image files outnumber puzzles because multi-image families (e.g. Patch_Select, Coordinates, Connect_Icon) render several PNGs per puzzle (options / references / grid tiles).
Total size on disk ≈ 53.9 GB.

Splits & structure

CaptchaArena/
├── Train/          # 2100 puzzles per task
│   ├── Bingo_2100/
│   ├── Click_Order_2100/
│   ├── Connect_Icon_2100/      # packaged as a .tar (see Notes)
│   ├── …
│   └── Hold_Button_2100/
├── Val/            # 200 puzzles per task
│   ├── Bingo_200/
│   └── …
└── Test/           # 200 puzzles per task
    ├── Bingo_200/
    └── …

Train 2,100 puzzles / task; Val and Test 200 puzzles / task, across all 20 families.
Folder naming: <Split>/<Task>_<N>/ where N is the target puzzle count.
Puzzle images are PNG. Layout varies by task: single-image families store flat PNGs (e.g. Train/Bingo_2100/bingo1.png); multi-image families group each puzzle in its own sub-folder.
Every task folder also ships two label files — ground_truth.json and ground_truth_cu.json (see Ground truth).

Ground truth

Every task folder ships its labels as two JSON files, both keyed by image filename:

ground_truth.json — base answer + metadata.
ground_truth_cu.json — the same entries plus answer_cu, a pixel-space Computer-Use action sequence (click / drag / …) that solves the puzzle, tagged with answer_cu_kind (e.g. "tool_calls").

The answer key(s) are family-specific. A few representative shapes:

// grid pick (Select_Animal, Patch_Select, Image_Recognition, …)
"image1.png": {
  "prompt": "Pick a whale",
  "target_object": "whale",
  "grid_size": [2, 3],
  "correct_patches": [1],                 // index(es) of the correct cell(s)
  "description": "A 2x3 grid of images; pick the named one."
}

// option select (Coordinates, Object_Match, …)
"coord_0140_…__J70.png": {
  "prompt": "Using the arrows, move Jerry to the indicated seat",
  "correct_option_index": 4,              // index into option_images
  "option_images": [".../I72.png", ".../H69.png", "…", ".../J70.png"]
}

// rotation (Rotation_Match)
"puzzle_rotation_232_45.json": {
  "prompt": "Use the arrows to rotate the object to match the reference.",
  "answer": 45, "correct_angle": 45,      // degrees
  "reference_image": "ref_232_45.png", "object_base_image": "232.png"
}

// swap-to-line (Bingo)
"bingo2301.png": {
  "prompt": "Exchange two images to line up identical ones.",
  "answer": [[3, 4]],                     // swap cell 3 <-> cell 4
  "grid_size": [3, 3],
  "solution_line": { "vertical": [1, 4, 7] }
}

Other families use analogous keys (counting families store the target count; position families a target point + tolerance). The _cu file adds the replayable pixel action sequence:

// ground_truth_cu.json — same entry + a ready-to-replay action sequence
"image1.png": {
  "correct_patches": [1],
  "answer_cu": [
    { "action": "click", "arguments": { "x": 640, "y": 413 } },
    { "action": "click", "arguments": { "x": 640, "y": 885 } }   // e.g. confirm / submit
  ],
  "answer_cu_kind": "tool_calls"
}

Notes

Train/Connect_Icon_2100/ is shipped as a single archive, Connect_icon_2100.tar (~5.3 GB, LFS). Extract it with:
```
tar xf Connect_icon_2100.tar
```

Loading

After your access request is approved, log in and download with the Hub client:

pip install -U huggingface_hub
hf auth login          # required: this dataset is gated
hf download ZHEN-04/CaptchaArena --repo-type dataset --local-dir CaptchaArena

Or grab a single split / task folder:

from huggingface_hub import snapshot_download
snapshot_download(
    "ZHEN-04/CaptchaArena", repo_type="dataset",
    allow_patterns=["Val/Bingo_200/*"], local_dir="CaptchaArena",
)

Relation to CaptchaArena-Trajectories

ZHEN-04/CaptchaArena (this repo) — the puzzles: images + instruction + ground-truth answer (base + Computer-Use pixel variant).
ZHEN-04/CaptchaArena-Trajectories — CoT Computer-Use agent trajectories that solve these puzzles, in per-turn SFT format.

License

Released under CC-BY-NC-4.0 (Creative Commons Attribution–NonCommercial 4.0). Non-commercial academic research use only — commercial use is prohibited. Access is gated: you must request access and agree to these terms before downloading. Please attribute when using this dataset.

Citation

If you use CaptchaArena, please cite this repository and the code at github.com/X0X0X00/CaptchaArena. (Formal citation / paper reference: TODO.)

Downloads last month: 284

Total file size:

53.9 GB