Ardea/arc_agi_v1

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

This dataset is a reorganized version of the ARC-AGI v1 (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

{
    "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

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

Loading the Dataset

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:

@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.