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

kofdai/verantyx-arc-agi2

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

Score

Approach

Architecture

Puzzle Language Patterns (Phase 7)

Score History

Performance

Usage

Files

License

Author

kofdai/
verantyx-arc-agi2