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Refresh ARC datasets section

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@@ -39,17 +39,15 @@ span corruption and data augmentation (to mimic incorrect answers) and passed
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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 Link: https://github.com/michaelhodel/re-arc
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- Note: This is the repository from Michael Hodel, which procedurally generates examples for the 400 ARC training tasks. We also include RE-ARC eval and ARC 1.5 (also by Michael Hodel).
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- ConceptARC Link: https://github.com/victorvikram/ConceptARC
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- 1D-ARC (likely "ID ARC") Link: https://khalil-research.github.io/LLM4ARC/
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- ARC_gym
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- Sort-of-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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- There is a large amount of ARC-related tasks that are not solving for the board (like generating code, predicting various parameters or features related to the task). There are other non-ARC related tasks.
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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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