GPT-5.1 Pro 2025-12-13
This is the variant of GPT-5.1 that you get on the web UI. It is unknown exactly how it correlates with the API.
Output constraints:
  • Input and output have the same size
  • Supposing background is black, input and output contain the same number of objects of each color
    • the lower right part of each object (non-diagonal) does not move
      • the rest of each object outside the lower right part moves by 1 square to the right
Input constraints:
  • inputs are 3x6
Output constraints:
  • outputs are 3x9
TODO: this one is quite challenging.
Hard input constraints:
  • inputs have two colors: green and black
Hard output constraints:
  • output has three colors: black, green and yellow
  • output has same size as input
  • green is copied from input to output
    • output differs from input by making some black pixels yellow. Which pixels are becoming yellow?
Hard input constraints:
  • inputs are 3x3
  • inputs contain only 2 colors monocolored: black and another
Hard output constraints:
  • output is 3x input width and height. Suggests that the output is a 3x3 grid based on the input.
    • stronger: if output is split as a 3x3 grid, then each 3x3 block is either black or a copy as input. Which is which?
      • stronger: each pixel of the input determines if block is black or copy (final solution)
  • output contains only two colors: black and another
Input output comparison:
  • input appears pasted on output multiple times: suggests it is being copy pasted
Hard output constraints:
  • output is 3x input width and height: suggests that the output is a 3x3 grid based on the input
    If that is the case, let's try to figure out what is placed on each output grid.
    Notice: each grid element is either blank or the input.
    OK so let's determine what in the input determines each output grid.
    Because input in 3x3 maybe there's a direct mapping.
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/

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