SHYRS: A proof of concept for specifying how humans solve ARC-AGI-2 puzzles
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Updated
Dec 9, 2025
SHYRS: A proof of concept for specifying how humans solve ARC-AGI-2 puzzles
PUMA – Program Understanding & Meta-learning Architecture Neuroscience-inspired meta-learning system for solving ARC-AGI-2 tasks through RFT-(Relational Frame Theory), symbolic reasoning, neural guidance, and test-time adaptation. #ARC2025
Synalinks ARCAGI2 public benchmark
A very simplistic approach to solving ARC-AGI-2. Designed to be a neural network built off of primitive functions with special tweaks. This model strives to be as simple and quick as possible as well.
Pure-Python baseline solver for Kaggle ARC Prize 2025 on ARC-AGI-2
Official package for "A Neural Affinity Framework for Abstract Reasoning." Includes the validated 9-category ARC taxonomy, pre-computed fine-tuning results, and scripts to verify the Compositional Gap.
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