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--- |
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language: |
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- en |
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pretty_name: "Augmented ARC-AGI Grids" |
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tags: |
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- tabular |
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- datasets |
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- pretraining |
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license: "cc-by-sa-4.0" |
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--- |
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# JDWebProgrammer/arg-agi-augmented |
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## Dataset Description |
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### Overview |
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This dataset is an augmented version of grids extracted from the [ARC-AGI dataset](https://huggingface.co/datasets/dataartist/arc-agi) (Abstraction and Reasoning Corpus). It focuses on **individual grids** rather than full tasks or games, providing an expanded collection for pretraining and testing models like autoencoders (AEs) or latent-space reasoners. |
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- **Source**: Derived from the `training` split of ARC-AGI (all demonstration and test grids). |
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- **Augmentations**: Each original grid is expanded with 5 transformations (horizontal flip, vertical flip, 90°/180°/270° rotations), resulting in 6 variants per grid (original + 5 augments). |
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- **Key Note**: This is **not the full games/tasks** from ARC-AGI. It contains only the raw, augmented grids (as 2D lists of integers 0-10) for standalone use in perceptual pretraining or reconstruction testing. Use the original ARC-AGI for full few-shot reasoning tasks. |
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### Dataset Structure |
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- **Format**: Hugging Face `Dataset` object. |
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- **Splits**: Single split (`train`) with one field: |
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- `augmented_grids`: List of 2D lists (grids). Each grid is `[[int, ...], ...]` (H x W, values 0-10). |
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- **Size**: ~48,000 grids (from ~400 ARC training tasks × ~4 grids/task × 6 augments). |
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- **Metadata**: See `metadata.json` for stats (original grids, augmentation factor). |
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Example grid entry: |
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```python |
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augmented_grids[0] = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] # Example 3x3 grid |
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``` |
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### Usage |
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Load and use for pretraining: |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("JDWebProgrammer/arc-agi-augmented") |
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grids = ds['augmented_grids'] # List of all grids |
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``` |
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Ideal for: |
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- Pretraining perceptual models. |
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- Testing reconstruction accuracy (compare original vs. augmented). |
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- Data augmentation for fluid intelligence tasks (e.g., ARC-like pattern inference). |
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### Generation |
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- Extracted all input/output grids from ARC-AGI `training` split demos/tests. |
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- Applied deterministic augmentations (flips/rotations) to expand variety without labels. |
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- No synthetic generation — pure augmentation of real ARC data. |
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### Limitations |
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- Grids only (no task structure/context) — not for end-to-end ARC solving. |
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- Augmentations preserve structure but may introduce artifacts (e.g., rotations on asymmetric grids). |
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- Values 0-10 (ARC standard); normalize for models. |
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### License |
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- Based on ARC-AGI (CC BY-SA 4.0) — inherits same license. |
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- Augmentations: MIT (free for research/commercial). |
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### Citation |
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```bibtex |
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@misc{dataartist/arc-agi, |
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title = {ARC-AGI }, |
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author = {dataartist}, |
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year = {2025}, |
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url = {https://huggingface.co/datasets/dataartist/arc-agi} |
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} |
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``` |
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--- |
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*Generated for pretraining perceptual models on ARC-style puzzles. Not a substitute for full ARC tasks.* |
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