| --- |
| pretty_name: VisRO |
| license: cc-by-sa-4.0 |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: tasks/*/*/train/*.tar |
| - split: eval |
| path: tasks/*/*/eval/*.tar |
| - split: ood |
| path: tasks/*/*/ood/*.tar |
| - config_name: block_delta |
| data_files: |
| - split: train |
| path: tasks/spatial_transformations/block_delta/train/*.tar |
| - split: eval |
| path: tasks/spatial_transformations/block_delta/eval/*.tar |
| - split: ood |
| path: tasks/spatial_transformations/block_delta/ood/*.tar |
| - config_name: count_3d_blocks |
| data_files: |
| - split: train |
| path: tasks/spatial_transformations/count_3d_blocks/train/*.tar |
| - split: eval |
| path: tasks/spatial_transformations/count_3d_blocks/eval/*.tar |
| - split: ood |
| path: tasks/spatial_transformations/count_3d_blocks/ood/*.tar |
| - config_name: count_clusters |
| data_files: |
| - split: train |
| path: tasks/discrimination_grouping/count_clusters/train/*.tar |
| - split: eval |
| path: tasks/discrimination_grouping/count_clusters/eval/*.tar |
| - split: ood |
| path: tasks/discrimination_grouping/count_clusters/ood/*.tar |
| - config_name: count_points_on_lines |
| data_files: |
| - split: train |
| path: tasks/discrimination_grouping/count_points_on_lines/train/*.tar |
| - split: eval |
| path: tasks/discrimination_grouping/count_points_on_lines/eval/*.tar |
| - split: ood |
| path: tasks/discrimination_grouping/count_points_on_lines/ood/*.tar |
| - config_name: cube_unfold |
| data_files: |
| - split: train |
| path: tasks/spatial_transformations/cube_unfold/train/*.tar |
| - split: eval |
| path: tasks/spatial_transformations/cube_unfold/eval/*.tar |
| - split: ood |
| path: tasks/spatial_transformations/cube_unfold/ood/*.tar |
| - config_name: find_the_different |
| data_files: |
| - split: train |
| path: tasks/discrimination_grouping/find_the_different/train/*.tar |
| - split: eval |
| path: tasks/discrimination_grouping/find_the_different/eval/*.tar |
| - split: ood |
| path: tasks/discrimination_grouping/find_the_different/ood/*.tar |
| - config_name: find_the_same |
| data_files: |
| - split: train |
| path: tasks/discrimination_grouping/find_the_same/train/*.tar |
| - split: eval |
| path: tasks/discrimination_grouping/find_the_same/eval/*.tar |
| - split: ood |
| path: tasks/discrimination_grouping/find_the_same/ood/*.tar |
| - config_name: find_the_shadow |
| data_files: |
| - split: train |
| path: tasks/discrimination_grouping/find_the_shadow/train/*.tar |
| - split: eval |
| path: tasks/discrimination_grouping/find_the_shadow/eval/*.tar |
| - split: ood |
| path: tasks/discrimination_grouping/find_the_shadow/ood/*.tar |
| - config_name: flow_free |
| data_files: |
| - split: train |
| path: tasks/paths_connectivity/flow_free/train/*.tar |
| - split: eval |
| path: tasks/paths_connectivity/flow_free/eval/*.tar |
| - split: ood |
| path: tasks/paths_connectivity/flow_free/ood/*.tar |
| - config_name: jigsaw_completion |
| data_files: |
| - split: train |
| path: tasks/planar_transformations/jigsaw_completion/train/*.tar |
| - split: eval |
| path: tasks/planar_transformations/jigsaw_completion/eval/*.tar |
| - split: ood |
| path: tasks/planar_transformations/jigsaw_completion/ood/*.tar |
| - config_name: logic_matrix |
| data_files: |
| - split: train |
| path: tasks/planar_transformations/logic_matrix/train/*.tar |
| - split: eval |
| path: tasks/planar_transformations/logic_matrix/eval/*.tar |
| - split: ood |
| path: tasks/planar_transformations/logic_matrix/ood/*.tar |
| - config_name: maze |
| data_files: |
| - split: train |
| path: tasks/paths_connectivity/maze/train/*.tar |
| - split: eval |
| path: tasks/paths_connectivity/maze/eval/*.tar |
| - split: ood |
| path: tasks/paths_connectivity/maze/ood/*.tar |
| - config_name: metro_map |
| data_files: |
| - split: train |
| path: tasks/paths_connectivity/metro_map/train/*.tar |
| - split: eval |
| path: tasks/paths_connectivity/metro_map/eval/*.tar |
| - split: ood |
| path: tasks/paths_connectivity/metro_map/ood/*.tar |
| - config_name: mirroring_patterns |
| data_files: |
| - split: train |
| path: tasks/planar_transformations/mirroring_patterns/train/*.tar |
| - split: eval |
| path: tasks/planar_transformations/mirroring_patterns/eval/*.tar |
| - split: ood |
| path: tasks/planar_transformations/mirroring_patterns/ood/*.tar |
| - config_name: overlap_match |
| data_files: |
| - split: train |
| path: tasks/discrimination_grouping/overlap_match/train/*.tar |
| - split: eval |
| path: tasks/discrimination_grouping/overlap_match/eval/*.tar |
| - split: ood |
| path: tasks/discrimination_grouping/overlap_match/ood/*.tar |
| - config_name: overlapping_glyphs |
| data_files: |
| - split: train |
| path: tasks/discrimination_grouping/overlapping_glyphs/train/*.tar |
| - split: eval |
| path: tasks/discrimination_grouping/overlapping_glyphs/eval/*.tar |
| - split: ood |
| path: tasks/discrimination_grouping/overlapping_glyphs/ood/*.tar |
| - config_name: paper_folding |
| data_files: |
| - split: train |
| path: tasks/spatial_transformations/paper_folding/train/*.tar |
| - split: eval |
| path: tasks/spatial_transformations/paper_folding/eval/*.tar |
| - split: ood |
| path: tasks/spatial_transformations/paper_folding/ood/*.tar |
| - config_name: path_tracing |
| data_files: |
| - split: train |
| path: tasks/paths_connectivity/path_tracing/train/*.tar |
| - split: eval |
| path: tasks/paths_connectivity/path_tracing/eval/*.tar |
| - split: ood |
| path: tasks/paths_connectivity/path_tracing/ood/*.tar |
| - config_name: reconstruction |
| data_files: |
| - split: train |
| path: tasks/planar_transformations/reconstruction/train/*.tar |
| - split: eval |
| path: tasks/planar_transformations/reconstruction/eval/*.tar |
| - split: ood |
| path: tasks/planar_transformations/reconstruction/ood/*.tar |
| - config_name: rotation_patterns |
| data_files: |
| - split: train |
| path: tasks/planar_transformations/rotation_patterns/train/*.tar |
| - split: eval |
| path: tasks/planar_transformations/rotation_patterns/eval/*.tar |
| - split: ood |
| path: tasks/planar_transformations/rotation_patterns/ood/*.tar |
| - config_name: tangram_reconstruction |
| data_files: |
| - split: train |
| path: tasks/planar_transformations/tangram_reconstruction/train/*.tar |
| - split: eval |
| path: tasks/planar_transformations/tangram_reconstruction/eval/*.tar |
| - split: ood |
| path: tasks/planar_transformations/tangram_reconstruction/ood/*.tar |
| - config_name: three_d_views |
| data_files: |
| - split: train |
| path: tasks/spatial_transformations/three_d_views/train/*.tar |
| - split: eval |
| path: tasks/spatial_transformations/three_d_views/eval/*.tar |
| - split: ood |
| path: tasks/spatial_transformations/three_d_views/ood/*.tar |
| - config_name: view_reconstruction |
| data_files: |
| - split: train |
| path: tasks/spatial_transformations/view_reconstruction/train/*.tar |
| - split: eval |
| path: tasks/spatial_transformations/view_reconstruction/eval/*.tar |
| - split: ood |
| path: tasks/spatial_transformations/view_reconstruction/ood/*.tar |
| - config_name: viewpoint_rotation |
| data_files: |
| - split: train |
| path: tasks/spatial_transformations/viewpoint_rotation/train/*.tar |
| - split: eval |
| path: tasks/spatial_transformations/viewpoint_rotation/eval/*.tar |
| - split: ood |
| path: tasks/spatial_transformations/viewpoint_rotation/ood/*.tar |
| --- |
| |
| # VisRO: Visual Routines |
|
|
| [](https://arxiv.org/abs/2610.21323) |
| [](https://github.com/Lyy-iiis/AdaLoop) |
| [](https://huggingface.co/Lyy0725/AdaLoop) |
| [](https://creativecommons.org/licenses/by-sa/4.0/) |
|
|
| This is the VisRO dataset from the paper [AdaLoop: Thinking with the Vision Encoder](https://arxiv.org/abs/2610.21323). |
| VisRO is a synthetic visual reasoning dataset of 24 procedurally generated tasks in four categories. Every example is |
| one image, one question and one short reference answer. Each task has four difficulty levels for training and |
| in-distribution evaluation (easy, medium, hard, xhard) and a harder fifth level (xxhard) that is held out for |
| out-of-distribution evaluation. The generator code and the reference scorer are released with the paper's [code](https://github.com/Lyy-iiis/AdaLoop), and the AdaLoop checkpoints trained on VisRO are on [Hugging Face](https://huggingface.co/Lyy0725/AdaLoop). |
|
|
| <p align="center"> |
| <img src="assets/visro.png" alt="Examples of the six VisRO-Core6 tasks, in distribution and out of distribution" width="85%"/> |
| </p> |
| <p align="left"> |
| <em><strong>VisRO-Core6</strong>, the six tasks of the paper's experiments: (a) in-distribution examples at different |
| difficulty levels and (b) out-of-distribution examples.</em> |
| </p> |
|
|
| | 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/<category>/<task>/train/00000.tar ... 00019.tar |
| tasks/<category>/<task>/eval/00000.tar |
| tasks/<category>/<task>/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, `<key>.json` (its |
| record) and `<key>.png` (its image), where `<key>` 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 `<key>.json` member holds one record that follows `schema.json`: |
| |
| | Field | Meaning | |
| |---|---| |
| | `id` | `visro24/<task>/<split>/<eight-digit index>` | |
| | `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, `<key>.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. |
| |
