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README.md
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along with the prompt for refinement (model outputs the full corrected grid).
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Note: This did not result in better performance when used during inference (only used during TTT).
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📚 ARC-Related Datasets & Frameworks
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RE-ARC
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Andreas Koepf - Generated many tasks based upon the RE-ARC methodology using various foundation models. Additionally generated from a generator Andreas wrote based on the icecuber solution. It also includes extra tasks like predicting the solution graph.
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Jack Cole - Wrote generators for 60-80 tasks. Many were inspired by ARC items. Others were large concept datasets (cellular automata, math equation derived boards).
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## ARC Data Formatting
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along with the prompt for refinement (model outputs the full corrected grid).
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Note: This did not result in better performance when used during inference (only used during TTT).
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📚 **ARC-Related Datasets & Frameworks**
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- [RE-ARC](https://github.com/michaelhodel/re-arc) — procedurally generates examples for the 400 ARC training tasks (we also include RE-ARC eval + ARC 1.5).
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- [ConceptARC](https://github.com/victorvikram/ConceptARC)
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- [1D-ARC](https://khalil-research.github.io/LLM4ARC/)
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- ARC_gym, Sort-of-ARC
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- Andreas Koepf’s generator suites (includes RE-ARC-style grids, code generation targets, and solution graphs).
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- Jack Cole’s custom generators covering ~70 tasks plus larger concept sets (cellular automata, math-derived boards, etc.).
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Several auxiliary datasets predict task metadata (graphs, heuristics, explanations) rather than final boards; they are part of the broader instruction mixture this model saw during pretraining.
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## ARC Data Formatting
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