fchollet/ARC-AGI
The Abstraction and Reasoning Corpus
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
No releases in the last 90 days; the most recent was 862 days ago, with 21 contributors on GitHub.
Gained 6% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a benchmark dataset and interface for testing artificial general intelligence through grid-based abstraction and reasoning tasks.
- Enables evaluation of AI systems and humans on general fluid intelligence by requiring exact output grid transformations from novel inputs.
- Use for developing and testing program synthesis algorithms that learn cognitive priors from visual reasoning tasks.
- Use for psychometric research to compare human and AI intelligence on abstract problem-solving challenges.
- Use for building AI models that aim to emulate human-like general intelligence via symbolic and spatial reasoning.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
Watch-outs
- Warning: No commits in over a year (last push Apr 2025).
General guidance from repository metadata, not legal advice. How we score
From the README
This repository contains the ARC-AGI-1 task data, as well as a browser-based interface for humans to try their hand at solving the tasks manually.
See also the ARC-AGI-2 repository.
"ARC can be seen as a general artificial intelligence benchmark, as a program synthesis benchmark, or as a psychometric intelligence test. It is targeted at both humans and artificially intelligent systems that aim at emulating a human-like form of general fluid intelligence."
A complete description of the dataset, its goals, and its underlying logic, can be found in: On the Measure of Intelligence.
As a reminder, a test-taker is said to solve a task when, upon seeing the task for the first time, they are able to produce the correct output grid for all test inputs in the task (this includes picking the dimensions of the output grid). For each test input, the test-taker is allowed 3 trials (this holds for all test-takers, either humans or AI).
The
datadirectory contains two subdirectories:The tasks are stored in JSON format. Each task JSON file contains a dictionary with two fields:
A "pair" is a dictionary with two fields:
A "grid" is a rectangular matrix (list of lists) of integers between 0 and 9 (inclusive). The smallest possible grid size is 1x1 and the largest is 30x30.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
727+5
Open issues
38+2
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
| D4Vinci/Scrapling | 98.1K | +6.9K | 65 | 1 day ago |
| Leonxlnx/taste-skill | 110.7K | +5.2K | 55 | 1 day ago |
| calesthio/OpenMontage | 77.2K | +4.2K | 40 | — |
| heygen-com/hyperframes | 79.6K | +4.1K | 45 | today |
| fchollet/ARC-AGI | 4.9K | — | 18 | 862 days ago |
| Category median | 98.1K | +5.2K | 45 | — |
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