kofdai/Verantyx-arc-agi2-7.4

Verantyx ARC-AGI-2 — 7.4%

Pure program synthesis solver for ARC-AGI-2 — no neural networks, no LLMs, no hardcoded patterns.

Score

Approach

Zero-shot program synthesis:

1. 1.Decompose — Break input→output relationships into composable grid transformations
2. 2.Synthesize — Generate candidate programs from 50+ DSL operations
3. 3.Verify — CEGIS against ALL training pairs (must match exactly)
4. 4.Compose — 2-step pipelines when single rules don't suffice

No cheating: Test outputs are never accessed. Only training I/O pairs are used for rule discovery.

Key Innovation: Neighborhood Rule Learning

The most powerful operation: learns a deterministic mapping from each cell's local neighborhood (3×3 or 5×5 window) to its output value. Solves 240 of 1000 training tasks alone.

This is not a neural network — it's an exact lookup table built from training examples and verified to be consistent across all pairs.

DSL Operations (50+)

Score Progression

Usage

git clone https://github.com/Ag3497120/verantyx-arc-agi2
cd verantyx-arc-agi2

# Download ARC-AGI-2 data
git clone https://github.com/arcprize/arc-agi-2.git /tmp/arc-agi-2

# Run evaluation
python -m arc.eval_cross --split training

Properties

• —✅ No neural networks — pure symbolic reasoning
• —✅ No LLMs — no language model of any kind
• —✅ No hardcoded patterns — all rules are synthesized from training data
• —✅ No test data leakage — only training I/O pairs are used
• —✅ Deterministic — same input always produces same output
• —✅ Zero dependencies — pure Python, no pip install needed
• —✅ Fast — ~0.4s per task average

Links

• —GitHub: Ag3497120/verantyx-arc-agi2
• —HLE Solver: kofdai/verantyx-hle-2.6 (same Verantyx philosophy)
• —ARC-AGI-2: arcprize.org