ARC Prize 2026 - ARC-AGI-2 Solver

Architecture

1. DSL Solver — 32 primitive transforms (rotations, flips, cropping, border extraction, hole filling, object detection, color operations) + depth-2 composition. Solves geometric/structural tasks in milliseconds.
2. Object Solver — Connected components detection: extracts largest objects, converts object sets to color bars.
3. TTT Solver — 236K-parameter encoder-decoder Transformer trained from scratch per task using test-time training. 16 augmentations per example (D8 symmetries + color permutations).

Performance

• DSL accuracy on ARC-AGI-2 training: ~1.8% (18/1000 tasks)
• TTT model: 236K parameters, ~5-10s per task on L4 GPU
• Optimized for: Kaggle 4×L4 GPU, 12-hour time limit

Usage

1. Download kaggle_notebook.py
2. Upload to Kaggle as a notebook
3. Run — it reads from /kaggle/input/arc-prize-2026/ and writes /kaggle/working/submission.json

Files

References

• NVARC (2025 winner, 24% on ARC-AGI-2): Test-time training + heavy augmentation
• Product-of-Experts (Franzen et al., 2025): DFS + probability threshold + PoE scoring
• SOAR (2025, 2nd place paper): Self-improving evolutionary program synthesis
• MARC (NeurIPS 2025): The Surprising Effectiveness of TTT
• CompressARC (2025, 3rd place paper): MDL-based code golf, 76K parameters

License