task_categories:image-to-text task_categories:text-to-image language:en license:mit size_categories:1K<n<10K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
task_categories:
- image-to-text
- text-to-image
language:
- en
pretty_name: simons ARC (abstraction & reasoning corpus) solve fractal version 8
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: data.jsonl
---

# Version 1

ARC-AGI Tasks where the job is to transform fractal input/output images.

example count: 2-4.

test count: 1-2.

image size: 2-3.

# Version 2

Scale up the input/output images. Scale factor: 1-3.

# Version 3

Scale up the input/output images. Scale factor: 1-3.

Randomly invert the pattern_image.

# Version 4

Random add padding around the input image, that the model has to crop.

max_pad_count = 5.

# Version 5

Bigger images

max_image_size = 5

max_pad_count = 8

# Version 6

Migrating from fitting inside 512 context length to fitting inside 1024 context length. Removing tasks that are too long to fit inside the context length.

Bigger scale factor: 1-5.

# Version 7

Earlier predictions added to some of the rows.

Ignoring fractals that RLE compress too badly. Previous cutoff was 1000 tokens. Now it's 800 tokens, so there is room for the earlier prediction.

# Version 8

Added fields: `arc_task`, `test_index`, `earlier_output`.