kofdai / verantyx-arc-agi2

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

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)

Score History

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

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