Your 8 HVAC models already perform abstract reasoning:
- AQUILO: Maps electrical patterns → fault types (abstraction!)
- VULCAN: Vibration patterns → mechanical states (pattern recognition!)
- APOLLO: Combines multiple abstractions → final diagnosis (meta-reasoning!)
This is EXACTLY what ARC tasks require!
class ARCTask:
def __init__(self, train_examples, test_input):
self.train = train_examples # 3-5 examples
self.test = test_input # What we need to solve
# Extract features similar to your HVAC models
self.features = {
'grid_size': self.analyze_dimensions(),
'colors_used': self.extract_color_palette(),
'objects': self.segment_objects(),
'symmetries': self.detect_symmetries(),
'patterns': self.find_repeating_patterns()
}class PatternEngine:
def __init__(self):
# Inspired by your 8-model architecture
self.recognizers = {
'spatial': SpatialPatternModel(), # Like ZEPHYRUS (airflow)
'color': ColorMappingModel(), # Like diagnostic states
'counting': CountingModel(), # Like sensor readings
'transformation': TransformModel(), # Like VULCAN (mechanical)
'relational': RelationalModel(), # Like NAIAD (water flow)
'composite': CompositeModel(), # Like COLOSSUS
'validation': ValidationModel(), # Like GAIA
'master': MasterSolver() # Like APOLLO
}class ARCDSL:
"""Similar to how IRIS routes metrics to models"""
# Primitive operations
primitives = {
'move': lambda obj, dx, dy: translate(obj, dx, dy),
'rotate': lambda obj, angle: rotate(obj, angle),
'mirror': lambda obj, axis: flip(obj, axis),
'color': lambda obj, mapping: recolor(obj, mapping),
'fill': lambda obj, color: flood_fill(obj, color),
'copy': lambda obj, n: replicate(obj, n),
'scale': lambda obj, factor: resize(obj, factor)
}
# Compositional operations (like your model pipeline)
def compose(self, ops):
"""Chain operations like your 8-model pipeline"""
def composed(grid):
result = grid
for op in ops:
result = op(result)
return result
return composedclass HypothesisGenerator:
def __init__(self, hailo_device):
self.device = hailo_device
self.hypothesis_space = []
def generate_hypotheses(self, task):
"""Generate 1000s of possible solutions in parallel"""
# Level 1: Simple transformations
simple_ops = self.generate_simple_operations(task)
# Level 2: Conditional logic
conditional_ops = self.generate_conditionals(task)
# Level 3: Complex compositions
complex_ops = self.generate_compositions(task)
# Test all in parallel on Hailo-8
return self.parallel_evaluate(simple_ops + conditional_ops + complex_ops)class SolutionVerifier:
"""Similar to GAIA - validates safety/correctness"""
def verify(self, program, task):
# Test on all training examples
for example in task.train:
predicted = program(example.input)
if not self.exact_match(predicted, example.output):
return False
return True
def exact_match(self, pred, true):
"""ARC requires exact match - every pixel must be correct"""
return np.array_equal(pred, true)def solve_object_manipulation(task):
"""Many ARC tasks involve moving/transforming objects"""
# Segment objects (like detecting HVAC components)
objects = segment_objects(task.train[0].input)
# Track object transformations
for example in task.train:
input_objects = segment_objects(example.input)
output_objects = segment_objects(example.output)
# Learn transformation rules
transformation = infer_object_transform(input_objects, output_objects)
# Apply to test
test_objects = segment_objects(task.test_input)
return apply_transform(test_objects, transformation)def solve_pattern_completion(task):
"""Like predicting sensor readings from partial data"""
# Detect pattern type
pattern_type = detect_pattern(task.train)
if pattern_type == 'periodic':
period = find_period(task.train)
return extend_periodic(task.test_input, period)
elif pattern_type == 'symmetrical':
axis = find_symmetry_axis(task.train)
return complete_symmetry(task.test_input, axis)def solve_conditional(task):
"""Like your fault diagnosis logic"""
# Extract conditions from examples
conditions = []
for example in task.train:
condition = infer_condition(example.input, example.output)
conditions.append(condition)
# Build decision tree
decision_tree = build_tree(conditions)
# Apply to test
return decision_tree.predict(task.test_input)class ARCSolverOnDelphi:
def __init__(self):
self.models = {
'pattern': load_on_hailo('pattern_recognition.hef'),
'spatial': load_on_hailo('spatial_reasoning.hef'),
'logical': load_on_hailo('logical_inference.hef')
}
def solve_batch(self, tasks):
"""Solve multiple ARC tasks in parallel"""
results = []
for task in tasks:
# Two-attempt strategy
attempt1 = self.geometric_solver(task)
attempt2 = self.symbolic_solver(task)
results.append([attempt1, attempt2])
return results# Don't train on massive datasets - train on reasoning!
def meta_learning_approach():
"""Learn to learn - like humans do"""
# Start with primitive operations
primitives = learn_primitives(arc_training_data)
# Learn to compose primitives
compositions = learn_compositions(primitives)
# Learn when to apply what
strategy_selector = learn_strategy_selection(compositions)
return ARCSolver(primitives, compositions, strategy_selector)# submission.py
import json
import numpy as np
class ARCSubmission:
def __init__(self):
self.solver = ARCSolver()
def predict(self, challenges):
submission = {}
for task_id, task in challenges.items():
# Generate two attempts per task
predictions = []
for test_input in task['test']:
attempt1 = self.solver.solve_geometric(task, test_input)
attempt2 = self.solver.solve_symbolic(task, test_input)
predictions.append({
'attempt_1': attempt1.tolist(),
'attempt_2': attempt2.tolist()
})
submission[task_id] = predictions
return submission- Pre-compute pattern library
- Use Cython for critical loops
- Parallelize hypothesis testing
- Early stopping for difficult tasks
- Not Data-Hungry: ARC punishes memorization, rewards reasoning
- Leverages Your Strengths: Abstract pattern recognition
- Hardware Advantage: Parallel hypothesis testing on Hailo
- Novel Approach: Neurosymbolic, not pure neural or symbolic
The key is that ARC is about reasoning, not pattern matching. Your experience with diagnostic reasoning is perfect for this!