arc-prize 0.1.0

ARC-AGI-2 solver using geometric embeddings and adversarial structure probing

arc-prize

ARC-AGI-2 solver using geometric embeddings, hyperbolic rule inference, and adversarial structure probing.

Approach

This solver combines three key ideas:

1. Hyperbolic Rule Encoding — ARC transformation rules are hierarchical (e.g., "tile the pattern" contains "repeat", "mirror", "offset"). The Poincaré ball naturally represents this hierarchy: general rules near the origin, specific sub-rules near the boundary.

2. Radar-like Structure Probing — Parametric transforms at controllable intensity [0,1] are applied to grids and the model's latent response is measured. Transforms the model is invariant to reveal learned symmetries; transforms it's sensitive to reveal structural features it uses. This is adapted from the Bond Index adversarial fuzzing framework.

3. Test-Time Refinement — Per-task fine-tuning on augmented training pairs (the dominant winning pattern from ARC Prize 2025). Leave-one-out training on dihedral augmentations provides more signal per task.

Architecture

Training Pairs → PairEncoder → z_pairs → HyperbolicRuleEncoder → h_rules
                                                                      ↓
                                                         Attention Aggregation
                                                                      ↓
Test Input → GridEncoder ─────────────────────────→ GridDecoder ← z_rule
                                                         ↓
                                                   Output Grid

The adversarial training component (gradient reversal) forces the encoder to be invariant to surface features (color palette, position) while remaining sensitive to structural features (pattern, symmetry, count).

Installation

pip install arc-prize

Or for development:

git clone https://github.com/ahb-sjsu/arc-prize.git
cd arc-prize
pip install -e ".[dev]"

Quick Start

from arc_prize.solver import ARCSolver, SolverConfig
from arc_prize.data import load_arckit_tasks

# Load ARC-AGI-1 tasks
train_tasks, eval_tasks = load_arckit_tasks()

# Initialize solver
solver = ARCSolver(SolverConfig(device="cuda"))

# Solve a task
task = eval_tasks[0]
train_pairs = [(p.input, p.output) for p in task.train]
test_inputs = [p.input for p in task.test]

predictions = solver.solve_task(train_pairs, test_inputs, refine=True)
# predictions[i] = [candidate_1, candidate_2] for test input i

Structure Probing

The radar-like probe system reveals what the model has learned:

from arc_prize.fuzzer import StructureProbe, make_transform_suite
from arc_prize.encoder import GridEncoder

encoder = GridEncoder(z_dim=128)
probe = StructureProbe(encoder, transforms=make_transform_suite())

report = probe.scan(task_id="abc123", grid=input_grid, device="cuda")

# What is the model invariant to?
print(report.invariance_profile)
# {'color_permute': 0.95, 'rotate': 0.88, 'add_noise': 0.12, ...}

# How well does it separate structure from surface?
print(f"Robustness Index: {report.robustness_index:.3f}")

Modules

Module	Description
grid.py	Grid ↔ tensor conversion, padding, object extraction
encoder.py	CNN + attention pooling grid encoder, pair encoder
geometric.py	Poincaré ball operations, hyperbolic rule encoder
decoder.py	FiLM-conditioned CNN grid decoder
fuzzer.py	Parametric structure probing (the "radar")
adversarial.py	Gradient reversal for surface-invariant encoding
augment.py	Dihedral group + color permutation augmentations
solver.py	End-to-end solver with test-time refinement
data.py	Task loading from JSON / arckit
evaluate.py	Exact-match scoring
submit.py	Kaggle submission generator

License

MIT