--- pretty_name: VisRO license: cc-by-sa-4.0 task_categories: - visual-question-answering language: - en size_categories: - 1M Examples of the six VisRO-Core6 tasks, in distribution and out of distribution

VisRO-Core6, the six tasks of the paper's experiments: (a) in-distribution examples at different difficulty levels and (b) out-of-distribution examples.

| Split | Per task | All 24 tasks | Difficulty levels | |---|---:|---:|---| | train | 100,000 | 2,400,000 | easy / medium / hard / xhard, 25,000 each | | eval | 600 | 14,400 | easy / medium / hard / xhard, 150 each | | ood | 600 | 14,400 | xxhard | | Total | 101,200 | 2,428,800 | | ## Tasks | Category | Tasks | |---|---| | discrimination_grouping | count_clusters, count_points_on_lines, find_the_different, find_the_same, find_the_shadow, overlap_match, overlapping_glyphs | | paths_connectivity | flow_free, maze, metro_map, path_tracing | | planar_transformations | jigsaw_completion, logic_matrix, mirroring_patterns, reconstruction, rotation_patterns, tangram_reconstruction | | spatial_transformations | block_delta, count_3d_blocks, cube_unfold, paper_folding, three_d_views, view_reconstruction, viewpoint_rotation | Per-task, per-split and per-difficulty counts are in `counts.json`. ## Layout ```text README.md schema.json counts.json SHA256SUMS assets/visro.png tasks///train/00000.tar ... 00019.tar tasks///eval/00000.tar tasks///ood/00000.tar ``` There are 528 uncompressed tar shards in [WebDataset](https://github.com/webdataset/webdataset) format. A training shard holds 5,000 examples; an eval or ood shard holds 600. Each example is two consecutive members, `.json` (its record) and `.png` (its image), where `` is the eight-digit index of its `id`; a shard holds nothing else. The default configuration covers all 24 tasks with the splits `train`, `eval` and `ood`; each task also has a configuration of its own, named after the task. ## Records Every `.json` member holds one record that follows `schema.json`: | Field | Meaning | |---|---| | `id` | `visro24///` | | `task`, `category` | one of the 24 tasks and its category | | `split` | `train`, `eval` or `ood` | | `difficulty` | `easy`, `medium`, `hard`, `xhard` (train / eval) or `xxhard` (ood) | | `question` | the question; it states the required answer format | | `answer` | the reference answer, always a string | | `answer_type` | `choice` (answer is one of `options`) or `free_form` | | `options` | the answer options of a choice question; can also list suggested values; empty otherwise | | `image` | the name of the image member in the shard, `.png` | | `image_sha256`, `image_size` | the sha256 of the PNG bytes and `[width, height]` | Examples are shuffled within a shard, so every shard mixes the difficulty levels. ## Read a shard With `datasets` (the `png` member becomes an image, the `json` member the record): ```python from datasets import load_dataset maze_eval = load_dataset("Lyy0725/VisRO", "maze", split="eval") row = maze_eval[0] # row["png"] (PIL image), row["json"] (the record), row["__key__"] ``` Or with the standard library: ```python import json import tarfile with tarfile.open("00000.tar", "r:") as shard: members = shard.getmembers() for meta, image in zip(members[0::2], members[1::2]): row = json.loads(shard.extractfile(meta).read()) png_bytes = shard.extractfile(image).read() # row["question"], row["answer"], png_bytes ``` ## Generating More Data VisRO is a fixed release: its items were generated once, then given public ids and shuffled. The [generator code](https://github.com/Lyy-iiis/AdaLoop/tree/main/visro) regenerates items of the same tasks, difficulty levels and record schema, byte for byte for a given task, level, split and seed, but it does not re-create the published items one for one. The paper's experiments use the six core tasks (count_clusters, flow_free, maze, metro_map, overlapping_glyphs, path_tracing). ## License and attribution The dataset is released under CC BY-SA 4.0. The images contain icons from third-party collections, which keep their own terms: - [game-icons.net](https://game-icons.net) icons, CC BY 3.0, by game-icons.net and each icon's individual author; - [OpenMoji](https://openmoji.org) graphics, CC BY-SA 4.0 (OpenMoji, the open-source emoji and icon project, HfG Schwäbisch Gmünd and contributors); this share-alike term is why the dataset is CC BY-SA 4.0; - [Microsoft Fluent UI Emoji](https://github.com/microsoft/fluentui-emoji), MIT, Microsoft Corporation; - [Material Design Icons](https://pictogrammers.com/library/mdi/) by Pictogrammers, Apache 2.0. Text in the images is rendered with the DejaVu Sans fonts. The code repository's [`NOTICE`](https://github.com/Lyy-iiis/AdaLoop/blob/main/NOTICE) lists every bundled asset with its license and attribution. ## Citation If you find this dataset useful in your research, please consider citing our paper :) ```bib @article{adaloop2026, title={AdaLoop: Thinking with the Vision Encoder}, author={Han, Xinran Nicole and Lu, Yiyang and Gan, Katelyn and Zickler, Todd}, journal={arXiv preprint arXiv:2610.21323}, year={2026} } ``` ## Checksums `SHA256SUMS` lists the sha256 of every file in this repository except itself.