# Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation

arXiv: https://arxiv.org/abs/2404.07353 · HTML: https://mlsanity.com/paper/2404.07353/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 2 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Built on by (most cited first, 30 of 33)

- [The Surprising Effectiveness of Test-Time Training for Few-Shot Learning](https://mlsanity.com/paper/2411.07279/) (2024)
- [Product of Experts with LLMs: Boosting Performance on ARC Is a Matter of Perspective](https://mlsanity.com/paper/2505.07859/) (2025)
- [LoopViT: Scaling Visual ARC with Looped Transformers](https://mlsanity.com/paper/2602.02156/) (2026)
- [Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects](https://mlsanity.com/paper/2410.06405/) (2024)
- [ARC-GEN: A Mimetic Procedural Benchmark Generator for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2511.00162/) (2025)
- [Don't throw the baby out with the bathwater: How and why deep learning for ARC](https://mlsanity.com/paper/2506.14276/) (2025)
- [Think Visually, Reason Textually: Vision-Language Synergy in ARC](https://mlsanity.com/paper/2511.15703/) (2025)
- [Compositional-ARC: Assessing Systematic Generalization in Abstract Spatial Reasoning](https://mlsanity.com/paper/2504.01445/) (2025)
- [CausalARC: Abstract Reasoning with Causal World Models](https://mlsanity.com/paper/2509.03636/) (2025)
- [One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models](https://mlsanity.com/paper/2604.18839/) (2026)
- [Abductive Symbolic Solver on Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2411.18158/) (2024)
- [From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark](https://mlsanity.com/paper/2505.17482/) (2025)
- [GIFARC: Synthetic Dataset for Leveraging Human-Intuitive Analogies to Elevate AI Reasoning](https://mlsanity.com/paper/2505.20672/) (2025)
- [ARC-TGI: Human-Validated Task Generators with Reasoning Chain Templates for ARC-AGI](https://mlsanity.com/paper/2603.05099/) (2026)
- [Slots, Transitions, Loops: Learning Composable World Models for ARC](https://mlsanity.com/paper/2606.12316/) (2026)
- [Implicit Rule Induction with Test-Time Task Embeddings in ARC-like Tasks](https://mlsanity.com/paper/2609.21181/) (2026)
- [Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task](https://mlsanity.com/paper/2410.11324/) (2024)
- [CellARC: Measuring Intelligence with Cellular Automata](https://mlsanity.com/paper/2511.07908/) (2025)
- [Vector Symbolic Algebras for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2511.08747/) (2025)
- [PotARCin: Multi-Dimensional Evaluation of Skill Acquisition in Abstract Reasoning Tasks](https://mlsanity.com/paper/2609.27288/) (2026)
- [EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis](https://mlsanity.com/paper/2412.17330/) (2024)
- [Channel-Wise MLPs Improve the Generalization of Recurrent Convolutional Networks](https://mlsanity.com/paper/2508.08298/) (2025)
- [COGITAO: A Visual Reasoning Framework To Study Compositionality & Generalization](https://mlsanity.com/paper/2509.05249/) (2025)
- [Reasoning is a Modality](https://mlsanity.com/paper/2601.13562/) (2026)
- [DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2606.26530/) (2026)
- [Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2](https://mlsanity.com/paper/2606.31543/) (2026)
- [From Global to Factor-Wise Expert Composition in Discrete Diffusion Models](https://mlsanity.com/paper/2607.11758/) (2026)
- [Recursive Vision Language Models for General Symbolic Reasoning](https://mlsanity.com/paper/2608.01534/) (2026)
- [BDH-CQ: In-Context Learning with Recurrent Latent Reasoning](https://mlsanity.com/paper/2608.09888/) (2026)
- [Structured Sparse Memory for Recurrent Reasoning](https://mlsanity.com/paper/2609.33270/) (2026)

## Abstract (arXiv)

This work presents code to procedurally generate examples for the ARC training tasks. For each of the 400 tasks, an example generator following the transformation logic of the original examples was created. In effect, the assumed underlying distribution of examples for any given task was reverse engineered by implementing a means to sample from it. An attempt was made to cover an as large as reasonable space of possible examples for each task. That is, whenever the original examples of a given task may be limited in their diversity e.g. by having the dimensions of the grids, the set of symbols or number of objects constant or within tight bounds, even though the transformation does not require it, such constraints were lifted. Having access to not just a few examples per task, as the case for ARC, but instead very many, should enable a wide range of experiments that may be important stepping stones towards making leaps on the benchmark.