Summary of Towards Efficient Neurally-guided Program Induction For Arc-agi, by Simon Ouellette
Towards Efficient Neurally-Guided Program Induction for ARC-AGI
by Simon Ouellette
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed work explores neurally-guided program induction methods under the program induction paradigm to tackle the open-world problem domain of ARC-AGI. The researchers investigate three paradigms: Learning the grid space, Learning the program space, and Learning the transform space. They thoroughly implement and experiment on the first two, identifying their strengths and weaknesses, before suggesting the third as a potential solution and running preliminary experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores new ways to create programs using neural networks. It looks at three different approaches: learning in a grid-like space, learning about programming itself, and learning how to transform one program into another. The researchers test two of these methods and find their strengths and weaknesses. They also introduce a third method as a possible solution and run some initial tests. |
