README.md

# JDWebProgrammer/arg-agi-augmented

## Dataset Description

### Overview
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.

- **Source**: Derived from the `training` split of ARC-AGI (all demonstration and test grids).
- **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).
- **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.

### Dataset Structure
- **Format**: Hugging Face `Dataset` object.
- **Splits**: Single split (`train`) with one field:
  - `augmented_grids`: List of 2D lists (grids). Each grid is `[[int, ...], ...]` (H x W, values 0-10).
- **Size**: ~48,000 grids (from ~400 ARC training tasks × ~4 grids/task × 6 augments).
- **Metadata**: See `metadata.json` for stats (original grids, augmentation factor).

Example grid entry:
```python
augmented_grids[0] = [[0, 1, 0], [1, 0, 1], [0, 1, 0]]  # Example 3x3 grid
```

### Usage
Load and use for AE pretraining:
```python
from datasets import load_dataset
ds = load_dataset("JDWebProgrammer/arc-agi-augmented")
grids = ds['train']['augmented_grids']  # List of all grids

# Example: Batch grids for AE
def grid_to_tensor(grid):
    h, w = len(grid), len(grid[0])
    return torch.tensor(grid, dtype=torch.float).view(1, -1) / 10.0  # Normalize 0-1

batch = torch.cat([grid_to_tensor(g) for g in grids[:32]])  # Batch of 32
# Feed to AE: z = ae.encode(batch); recon = ae.decode(z)
```

Ideal for:
- Pretraining perceptual models.
- Testing reconstruction accuracy (compare original vs. augmented).
- Data augmentation for fluid intelligence tasks (e.g., ARC-like pattern inference).

### Generation
- Extracted all input/output grids from ARC-AGI `training` split demos/tests.
- Applied deterministic augmentations (flips/rotations) to expand variety without labels.
- No synthetic generation — pure augmentation of real ARC data.

### Limitations
- Grids only (no task structure/context) — not for end-to-end ARC solving.
- Augmentations preserve structure but may introduce artifacts (e.g., rotations on asymmetric grids).
- Values 0-10 (ARC standard); normalize for models.

### License
- Based on ARC-AGI (CC BY-SA 4.0) — inherits same license.
- Augmentations: MIT (free for research/commercial).

### Citation
```bibtex
@misc{dataartist/arc-agi,
  title = {ARC-AGI },
  author = {dataartist},
  year = {2025},
  url = {https://huggingface.co/datasets/dataartist/arc-agi}
}
```

---

*Generated for pretraining perceptual models on ARC-style puzzles. Not a substitute for full ARC tasks.*