---
license: apache-2.0
task_categories:
- table-question-answering
tags:
- arc
- agi
- arc-agi
pretty_name: ARC AGI v1
size_categories:
- 1K<n<10K
---

# ARC-AGI-V1 Dataset (A Take On Format)

This dataset is a reorganized version of the [ARC-AGI v1](https://github.com/fchollet/ARC-AGI) (Abstraction and Reasoning Corpus) benchmark, formatted for HuggingFace Datasets.

## Dataset Structure

The original ARC-AGI dataset has been transformed from its file-based JSON structure into a standardized HuggingFace dataset with two splits:

- **train** (400 examples): Tasks from the original `training` directory
- **test** (400 examples): Tasks from the original `evaluation` directory

### Original Structure

The original ARC-AGI dataset consisted of:
- A `training` directory with JSON files (one per task)
- An `evaluation` directory with JSON files (one per task)
- Each JSON file named with a task ID (e.g., `007bbfb7.json`)
- Each file containing:
  - `train`: Array of input/output example pairs for learning the pattern
  - `test`: Array of input/output pairs representing the actual task to solve

### Transformed Structure

Each row in this dataset represents a single ARC-AGI task with the following schema:

```
{
    "id": string,           // Task ID from the original filename
    "list": [               // Combined training examples and test inputs
        [                   // Training example inputs (from original 'train')
            [[int]], [[int]], ...
        ],
        [                   // Training example outputs (from original 'train')
            [[int]], [[int]], ...
        ],
        [                   // Test inputs (from original 'test')
            [[int]], [[int]], ...
        ]
    ],
    "label": [              // Test outputs (from original 'test')
        [[int]], [[int]], ...
    ]
}
```

#### Field Descriptions

- **`id`**: The unique task identifier from the original filename
- **`list`**: A nested list containing three components in order:
  1. **Example inputs** (`list[0]`): All input grids from the original `train` array
  2. **Example outputs** (`list[1]`): All output grids from the original `train` array (paired with example inputs)
  3. **Test inputs** (`list[2]`): All input grids from the original `test` array
- **`label`**: The correct output grids for the test inputs (from original `test` array outputs)

### Data Format

Each grid is represented as a 2D array of integers (0-9), where:
- Values range from 0 to 9 (representing different colors/states)
- Grid dimensions vary from 1×1 to 30×30
- Each integer represents a colored cell in the grid

### Example

```json
{
    "id": "007bbfb7",
    "list": [
        [
            [[0, 7, 7],                            // Example input 1
            [7, 7, 7],                             // 
            [0, 7, 7]],                            // 
            [[4, 0, 4], [0, 0, 0], [0, 4, 0]],     // Example input 2
            [[0, 0, 0], [0, 0, 2], [2, 0, 2]]      // Example input 3
        ],
        [
            [[0, 0, 0, 0, 7, 7, 0, 7, 7],          // Example output 1
            [0, 0, 0, 7, 7, 7, 7, 7, 7],
            [0, 0, 0, 0, 7, 7, 0, 7, 7],
            [0, 7, 7, 0, 7, 7, 0, 7, 7],
            [7, 7, 7, 7, 7, 7, 7, 7, 7],
            [0, 7, 7, 0, 7, 7, 0, 7, 7],
            [0, 0, 0, 0, 7, 7, 0, 7, 7],
            [0, 0, 0, 7, 7, 7, 7, 7, 7],
            [0, 0, 0, 0, 7, 7, 0, 7, 7]],
            [[], [], [], [], [], [], [], [], []],  // etc..
        ],
        [
            [[7, 0, 7], [7, 0, 7], [7, 7, 0]]      // Test input 1
        ]
    ],
    "label": [
        [[7, 0, 7, 0, 0, 0, 7, 0, 7],              // Test output 1 (ground truth)
        [7, 0, 7, 0, 0, 0, 7, 0, 7],
        [7, 7, 0, 0, 0, 0, 7, 7, 0],
        [7, 0, 7, 0, 0, 0, 7, 0, 7],
        [7, 0, 7, 0, 0, 0, 7, 0, 7],
        [7, 7, 0, 0, 0, 0, 7, 7, 0],
        [7, 0, 7, 7, 0, 7, 0, 0, 0],
        [7, 0, 7, 7, 0, 7, 0, 0, 0],
        [7, 7, 0, 7, 7, 0, 0, 0, 0]]
    ]
}
```

## Usage Philosophy

pprint(dataset['train']['list'][0][0][0])
pprint(dataset['train']['list'][0][1][0])
print('')
pprint(dataset['train']['list'][0][2][0])
pprint(dataset['train']['label'][0][0])

This ARC-AGI dataset format allows (me at least) to think about the tasks in this way:
1. **Learn from examples**: Study the input/output pairs:
   - input: `dataset['train']['list'][0][0][0]`
   - output: `dataset['train']['list'][0][1][0]`
   - input: `dataset['train']['list'][0][0][1]`
   - output: `dataset['train']['list'][0][1][1]`
   - where:
     - 1st num: `task number`
     - 2nd num: `either 0: example input || 1: example output`
     - 3rd num: `which example?`
2.  **Then 'Get the tests'**:
    - `dataset['train']['list'][0][2][0]`
3. **Apply the pattern**: Use the learned rule to make your two guesses
4. **Evaluate performance**: Compare model predictions against the `label` field
   - `dataset['train']['label'][0][0]`

### Training Split
- Contains all tasks from the original `training` directory
- Intended for model training and development
- Both example pairs and test solutions are provided

### Test Split
- Contains all tasks from the original `evaluation` directory
- Intended for final model evaluation
- In competition settings, test labels may be withheld

## Dataset Features

```python
Features({
    'id': Value('string'),
    'list': List(List(List(List(Value('int64'))))),
    'label': List(List(List(Value('int64'))))
})
```

## Loading the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("ardea/arc_agi_v1")

# Access splits
train_data = dataset['train']
test_data = dataset['test']

# Example: Get a single task
task = train_data[0]
task_id = task['id']
example_inputs = task['list'][0]
example_outputs = task['list'][1]
test_inputs = task['list'][2]
test_outputs = task['label']

# Example: Get a task by id
task = list(filter(lambda t: t['id'] == '007bbfb7', train_data))
```

## Transparency
I've left the script I used on the original dataset here as `arc_to_my_hf.py`

## Citation

If you use this dataset, please cite the original ARC-AGI work:

```bibtex
@misc{chollet2019measure,
    title={On the Measure of Intelligence},
    author={François Chollet},
    year={2019},
    eprint={1911.01547},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}
```

## License

This dataset maintains the Apache 2.0 license from the original ARC-AGI corpus.