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

Split	Score	Method
Training (1000 tasks)	74/1000 = 7.4%	DSL synthesis + CEGIS verification

Approach

Zero-shot program synthesis:

1. Decompose — Break input→output relationships into composable grid transformations
2. Synthesize — Generate candidate programs from 50+ DSL operations
3. Verify — CEGIS against ALL training pairs (must match exactly)
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+)

Category	Operations
Geometric	rotate_90/180/270, flip_h/v, mirror_h/v/hv, transpose, reverse_rows/cols, roll_rows/cols
Structural	crop_bbox, crop_to_color, extract_largest/smallest_region, extract_unique_subgrid
Morphological	erode, dilate, hollow_regions, fill_interior, extract_border, fill_enclosed
Color	colormap, replace_color, recolor_by_size, keep_one_color, remove_color
Sorting	row_sort, col_sort (by color_count, sum, first_nonbg)
Gravity	gravity_all/up/down/left/right
Connection	connect_h/v/hv, spread_color (4 directions)
Tiling	tile_to_output, corners_mirror, stack_h/v/h_flip/v_flip, self_tile, diagonal_tile
Subgrid	subgrid_select/overlay/diff (automatic separator detection)
Dedup	dedup_rows, dedup_cols
Learned	neighborhood_rule (radius 1-2 neighborhood mapping)

Score Progression

Version	Score	Key Change
v1	1.6%	Initial DSL: colormap, mirror, scale
v5	2.5%	WholeGridProgram class, rotations
v10	2.9%	Subgrid ops, CompositeProgram
v12	4.1%	extract_region, stack ops
v14	5.3%	corners_mirror, connect ops
v17	6.1%	neighborhood_rule learning
v19	7.4%	+18 DSL ops, priority reorder

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