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README.md
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---
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license: mit
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tags:
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- arc-agi
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- arc-agi-2
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- program-synthesis
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- rule-based
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- no-llm
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datasets:
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- fchollet/ARC-AGI
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language:
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- en
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metrics:
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- accuracy
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model-index:
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- name: verantyx-arc-agi2
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results:
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- task:
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type: program-synthesis
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name: ARC-AGI-2
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dataset:
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type: fchollet/ARC-AGI
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name: ARC-AGI-2 Training
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metrics:
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- type: accuracy
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value: 16.1
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name: Training Accuracy
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---
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# Verantyx ARC-AGI-2 β 16.1% (161/1000) π
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**Pure rule-based solver for ARC-AGI-2. No LLM. No neural networks. No pre-training.**
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## Score
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| Split | Score | Tasks Solved |
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|-------|-------|-------------|
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| Training | **16.1%** | **161 / 1000** |
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## Approach
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Verantyx is a **fully deterministic, LLM-free** ARC-AGI-2 solver built on program synthesis and domain-specific language (DSL) enumeration.
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### Architecture
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1. **Cross Engine** β Multi-strategy solver with priority-based candidate selection
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2. **Neighborhood Rules** β Exact and extended (count/directional/multi-pass) neighborhood matching
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3. **DSL Enumerator** β 32 parameterless primitives with depth-2 composition (1024 combinations)
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4. **Panel Operations** β Grid splitting (separators, equal partitions) + reduction (XOR, OR, AND, overlay, select)
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5. **Per-Object Transform** β Object detection β property-based recoloring/movement
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6. **Puzzle Language** β 25+ hand-crafted pattern detectors with auto-verification
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7. **Iterative Cross** β 2-step residual learning for complex transformations
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### Puzzle Language Patterns (Phase 7)
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| Pattern | Description | Example Task |
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|---------|-------------|-------------|
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| `cross_and_fill` | Fill cross intersections | β |
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| `diagonal_extend` | Extend diagonal lines | β |
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| `connect_same_color` | Connect same-colored cells | β |
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| `extract_tile` | Detect repeated tile | 7b7f7511 |
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| `two_row_interleave` | 2 rows β checkerboard | e9afcf9a |
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| `stack_nor` | Top+bottom halves NOR | fafffa47 |
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| `frame_repeat_border` | Frame with repeated border | 49d1d64f |
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| `col_color_map` | Column position β row color | a85d4709 |
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| `split_vsep_and` | Vertical split AND | 0520fde7 |
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| `grid_pattern` | Lattice/checkerboard generation | 332efdb3 |
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| `antidiag_fill` | Anti-diagonal + bottom fill | 3bd67248 |
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| `latin_square` | Latin square completion | 4cd1b7b2 |
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| `shift_recolor` | Shift + recolor fg cells | a79310a0 |
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| `uniform_row_detect` | Mark uniform rows | 25d8a9c8 |
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| + 10 more | ... | ... |
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### Score History
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| Version | Score | Delta | Key Changes |
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|---------|-------|-------|-------------|
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| v19 | 113 (11.3%) | β | Baseline with NB + DSL |
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| v27 | 127 (12.7%) | +14 | Extended NB rules |
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| v28 | 136 (13.6%) | +9 | split_combine + panel_extract |
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| v29 | 142 (14.2%) | +6 | puzzle_lang Phase 7 |
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| v30 | 144 (14.4%) | +2 | connect_same_color |
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| v31 | 146 (14.6%) | +2 | u_drop_ball + nonzero_count_row |
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| v32 | 147 (14.7%) | +1 | scale_down_uniform |
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| v33 | 149 (14.9%) | +2 | staircase_grow + comp_tile_2x2 |
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| v34 | 154 (15.4%) | +5 | frame_repeat + extract_tile |
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| v35 | 158 (15.8%) | +4 | col_color_map + split_vsep_and |
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| **v36** | **161 (16.1%)** | **+3** | **grid_pattern + latin_square** |
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## Performance
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- **Eval speed**: ~0.40s/task (1000 tasks in ~7 minutes)
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- **No GPU required** β runs on CPU only
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- **Deterministic** β same input always produces same output
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## Usage
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```python
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from arc.cross_engine import solve_cross_engine
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# train_pairs: list of (input_grid, output_grid)
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# test_inputs: list of input grids
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predictions, versions = solve_cross_engine(train_pairs, test_inputs)
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```
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## Files
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- `arc/cross_engine.py` β Main solver orchestrator
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- `arc/puzzle_lang.py` β Pattern-based puzzle language
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- `arc/nb_extended.py` β Extended neighborhood rules
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- `arc/beam_search.py` β Beam search with DSL
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- `arc/enumerator.py` β DSL primitive enumerator
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- `arc/panel_ops.py` β Panel split + reduce operations
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- `arc/per_object.py` β Per-object transforms
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- `arc/primitives.py` β 32 core DSL primitives
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## License
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MIT
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## Author
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kofdai β [verantyx project](https://github.com/Ag3497120/verantyx-v6)
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