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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 Data Formatting
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- ARC tasks ship as JSON where each `task_id` contains `train` pairs and `test` inputs; every grid is a rectangular list of lists with integers `0-9`. Dimensions follow the original 1×1–30×30 spec, though the evaluator accepts up to 50×50.
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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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- ARC tasks ship as JSON where each `task_id` contains `train` pairs and `test` inputs; every grid is a rectangular list of lists with integers `0-9`. Dimensions follow the original 1×1–30×30 spec, though the evaluator accepts up to 50×50.
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