re-arc 2025-12-13
By the author of ARC-DSL.
README says:
This repository presents code to procedurally generate examples for the ARC training tasks. For each of the 400 tasks, an example generator is provided.
arxiv.org/html/2404.07353v1 says:
Each generator is a standalone Python function merely making use of the DSL and functions from the random module from the standard library. The median generator consists of 40 lines of code and uses 22 DSL primitive calls and 10 calls to the random module.
Cool!
Original:
https://web.archive.org/web/20250216160803im_/https://github.com/michaelhodel/re-arc/raw/main/00d62c1b_original.png
Generated:
https://web.archive.org/web/20250216160803im_/https://github.com/michaelhodel/re-arc/raw/main/00d62c1b_generated.png
This section is about unofficial ARC-AGI-like problem sets.
These are interesting from both a:
  • practical point of view, as they provide more training data for potential solvers. If you believe that they are representative that is of course.
  • theoretical point of view, as they might help to highlight missing or excessive presumptions of the official datasets
github.com/neoneye/arc-dataset-collection contains a fantastic collection of such datasets, with visualization at: neoneye.github.io/arc/
MathArena Apex 2025-12-13
A subsets of problems that they curate from competitions.
The extreme overfitting case of training is to have a map where each input leads to one output.
However it is cool that this overfit does not allow you to compute the final input for which there is no known output.
This therefore forces the creation of more general solution rules.
While in some cases solutions can work for any input, in many others they require specific assumptions about input, but the model could simply check that the assumptions apply to all inputs and use them for the final algorithm.

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