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Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation
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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.
Forward citations
Cited by 6 Pith papers
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Per-pixel confidence routing of discrete diffusion experts outperforms global scalar composition (SuperDiff, FKC, RNE) on ARC-AGI-style tasks, especially for complementary specialists.
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The authors build a pipeline that converts GIFs into ARC-style puzzles with analogy labels and executable solutions, and report small in-context experiments suggesting the analogy labels shift an LLM's stated reasoning style.
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Recursive Vision Language Models for General Symbolic Reasoning
R-Qwen, a LoRA-adapted Qwen model that iteratively refines explicit candidate solutions under constraint projection, outperforms prior recursive models and zero-shot frontier LLMs on eight symbolic reasoning benchmarks.
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Channel-Wise MLPs Improve the Generalization of Recurrent Convolutional Networks
Adding a gated channel-wise MLP to a recurrent convolutional network raises median exact-match accuracy on 185 Re-ARC tasks from 78.75% to 92.19% in-distribution and from 2.34% to 14.58% on harder out-of-distribution tasks.
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From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark
A staged knowledge-prompting method (KAAR) improves LLM test accuracy on ARC by about 5 absolute points over repeated-sampling plan-guided code generation, reaching 35% with GPT-o3-mini.
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