| # ARC Prize 2026 - ARC-AGI-2 Solver |
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| Multi-strategy ensemble solver for the [ARC Prize 2026 Kaggle competition](https://www.kaggle.com/competitions/arc-prize-2026-arc-agi-2). |
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| ## Architecture |
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| Three-pronged ensemble: |
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| 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. |
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| 2. **Object Solver** — Connected components detection: extracts largest objects, converts object sets to color bars. |
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| 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). |
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| **Ensemble logic**: DSL → Object → TTT → Identity fallback |
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| ## Performance |
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| - **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 |
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| ## Usage |
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| 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` |
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| ## Files |
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| | File | Description | |
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| | `kaggle_notebook.py` | Complete Kaggle submission notebook | |
| | `kaggle_solver.py` | Standalone Python module (more features) | |
| | `train_sft_barc.py` | Optional: SFT pre-training on BARC dataset | |
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| ## References |
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| Built on research from: |
| - 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 |
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| ## License |
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| MIT — free to use, modify, and distribute. |
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