---
license: mit
tags:
- arc-agi
- arc-agi-2
- program-synthesis
- rule-based
- no-llm
datasets:
- fchollet/ARC-AGI
language:
- en
metrics:
- accuracy
model-index:
- name: verantyx-arc-agi2
  results:
  - task:
      type: program-synthesis
      name: ARC-AGI-2
    dataset:
      type: fchollet/ARC-AGI
      name: ARC-AGI-2 Training
    metrics:
    - type: accuracy
      value: 16.1
      name: Training Accuracy
---

# Verantyx ARC-AGI-2 — 16.1% (161/1000) 🏆

**Pure rule-based solver for ARC-AGI-2. No LLM. No neural networks. No pre-training.**

## Score

| Split | Score | Tasks Solved |
|-------|-------|-------------|
| Training | **16.1%** | **161 / 1000** |

## Approach

Verantyx is a **fully deterministic, LLM-free** ARC-AGI-2 solver built on program synthesis and domain-specific language (DSL) enumeration.

### Architecture

1. **Cross Engine** — Multi-strategy solver with priority-based candidate selection
2. **Neighborhood Rules** — Exact and extended (count/directional/multi-pass) neighborhood matching
3. **DSL Enumerator** — 32 parameterless primitives with depth-2 composition (1024 combinations)
4. **Panel Operations** — Grid splitting (separators, equal partitions) + reduction (XOR, OR, AND, overlay, select)
5. **Per-Object Transform** — Object detection → property-based recoloring/movement
6. **Puzzle Language** — 25+ hand-crafted pattern detectors with auto-verification
7. **Iterative Cross** — 2-step residual learning for complex transformations

### Puzzle Language Patterns (Phase 7)

| Pattern | Description | Example Task |
|---------|-------------|-------------|
| `cross_and_fill` | Fill cross intersections | — |
| `diagonal_extend` | Extend diagonal lines | — |
| `connect_same_color` | Connect same-colored cells | — |
| `extract_tile` | Detect repeated tile | 7b7f7511 |
| `two_row_interleave` | 2 rows → checkerboard | e9afcf9a |
| `stack_nor` | Top+bottom halves NOR | fafffa47 |
| `frame_repeat_border` | Frame with repeated border | 49d1d64f |
| `col_color_map` | Column position → row color | a85d4709 |
| `split_vsep_and` | Vertical split AND | 0520fde7 |
| `grid_pattern` | Lattice/checkerboard generation | 332efdb3 |
| `antidiag_fill` | Anti-diagonal + bottom fill | 3bd67248 |
| `latin_square` | Latin square completion | 4cd1b7b2 |
| `shift_recolor` | Shift + recolor fg cells | a79310a0 |
| `uniform_row_detect` | Mark uniform rows | 25d8a9c8 |
| + 10 more | ... | ... |

### Score History

| Version | Score | Delta | Key Changes |
|---------|-------|-------|-------------|
| v19 | 113 (11.3%) | — | Baseline with NB + DSL |
| v27 | 127 (12.7%) | +14 | Extended NB rules |
| v28 | 136 (13.6%) | +9 | split_combine + panel_extract |
| v29 | 142 (14.2%) | +6 | puzzle_lang Phase 7 |
| v30 | 144 (14.4%) | +2 | connect_same_color |
| v31 | 146 (14.6%) | +2 | u_drop_ball + nonzero_count_row |
| v32 | 147 (14.7%) | +1 | scale_down_uniform |
| v33 | 149 (14.9%) | +2 | staircase_grow + comp_tile_2x2 |
| v34 | 154 (15.4%) | +5 | frame_repeat + extract_tile |
| v35 | 158 (15.8%) | +4 | col_color_map + split_vsep_and |
| **v36** | **161 (16.1%)** | **+3** | **grid_pattern + latin_square** |

## Performance

- **Eval speed**: ~0.40s/task (1000 tasks in ~7 minutes)
- **No GPU required** — runs on CPU only
- **Deterministic** — same input always produces same output

## Usage

```python
from arc.cross_engine import solve_cross_engine

# train_pairs: list of (input_grid, output_grid)
# test_inputs: list of input grids
predictions, versions = solve_cross_engine(train_pairs, test_inputs)
```

## Files

- `arc/cross_engine.py` — Main solver orchestrator
- `arc/puzzle_lang.py` — Pattern-based puzzle language
- `arc/nb_extended.py` — Extended neighborhood rules
- `arc/beam_search.py` — Beam search with DSL
- `arc/enumerator.py` — DSL primitive enumerator
- `arc/panel_ops.py` — Panel split + reduce operations
- `arc/per_object.py` — Per-object transforms
- `arc/primitives.py` — 32 core DSL primitives

## License

MIT

## Author

kofdai — [verantyx project](https://github.com/Ag3497120/verantyx-v6)