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"""
Test Material-Field Engine with Reference Queries
Demonstrates system behavior with fixed reference queries:
- Simple factual prompts
- Common misconceptions
- Basic formula recall
- Date recall
Shows how elastic modulus mode affects exclusion behavior under a fixed substrate.
"""
import sys
from pathlib import Path
if hasattr(sys.stdout, "reconfigure"):
try:
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
except Exception:
pass
sys.path.insert(0, str(Path(__file__).parent))
from material_field_engine import (
VerifiedSubstrate, Vector2D, MaterialFieldEngine
)
def test_science_fact(mode='multiplicative', sigma=0.5):
"""
Query: 'What do plants need for photosynthesis?'
Expected: Sunlight, water, carbon dioxide
"""
print("\n" + "=" * 80)
print("SCIENCE: What do plants need for photosynthesis?")
print("=" * 80)
print(f"Mode: {mode}, Ο={sigma}")
# Substrate: Verified science facts
substrate = VerifiedSubstrate(
elastic_modulus_mode=mode,
elastic_modulus_sigma=sigma
)
# Verified answers (embeddings simulated as 2D for demo)
substrate.add_verified_state(Vector2D(x=0.90, y=0.88, properties=None)) # "sunlight, water, CO2"
# Candidates:
# - Correct answer (close to substrate)
# - Partially correct (medium distance)
# - Common misconception (far from substrate)
# - Creative but wrong (far, different direction)
candidates = [
(0.90, 0.88), # Correct: sunlight, water, CO2
(0.70, 0.65), # Partial: mentions sunlight but incomplete
(0.40, 0.35), # Misconception: "plants eat soil"
(0.15, 0.80), # Creative wrong: "moonlight and air"
]
engine = MaterialFieldEngine(
substrate,
lambda_min=0.35,
lambda_max=1.00,
inference_steps=8
)
engine.initialize_candidates(candidates)
print("\nCandidate Knowledge States:")
labels = [
"Correct (sunlight+water+CO2)",
"Partial (sunlight mentioned)",
"Misconception (plants eat soil)",
"Creative wrong (moonlight)"
]
for i, (v, label) in enumerate(zip(engine.candidate_vectors, labels)):
dist = v.distance_to(substrate.states[0])
print(f" {i}. {label:<30} | E={v.properties.elastic_modulus:.3f} | "
f"Ο_y={v.properties.yield_strength:.3f} | dist={dist:.3f}")
results = engine.run_inference()
print("\nResult:")
if results['final_output']:
winner_idx = next(i for i, c in enumerate(candidates)
if (c[0], c[1]) == (results['final_output'].x, results['final_output'].y))
print(f" Selected: {labels[winner_idx]}")
print(f" E={results['final_output'].properties.elastic_modulus:.3f}")
else:
print(" ABSTAINED (no candidate met grounding threshold)")
print(f" Excluded: {results['total_excluded']}")
# Intentionally omit interpretive flags in stdout.
def test_geography_fact(mode='multiplicative', sigma=0.5):
"""
Query: 'What is the capital of Texas?'
Expected: Austin
"""
print("\n" + "=" * 80)
print("GEOGRAPHY: What is the capital of Texas?")
print("=" * 80)
print(f"Mode: {mode}, Ο={sigma}")
substrate = VerifiedSubstrate(
elastic_modulus_mode=mode,
elastic_modulus_sigma=sigma
)
# Verified answer
substrate.add_verified_state(Vector2D(x=0.85, y=0.82, properties=None)) # "Austin"
# Candidates:
candidates = [
(0.85, 0.82), # Correct: Austin
(0.75, 0.70), # Common error: Houston (largest city)
(0.40, 0.35), # Wrong: Dallas
(0.20, 0.15), # Very wrong: Los Angeles (not even in Texas!)
]
engine = MaterialFieldEngine(
substrate,
lambda_min=0.35,
lambda_max=1.00,
inference_steps=8
)
engine.initialize_candidates(candidates)
print("\nCandidate Answers:")
labels = [
"Correct (Austin)",
"Common error (Houston - largest city)",
"Wrong (Dallas)",
"Very wrong (Los Angeles)"
]
for i, (v, label) in enumerate(zip(engine.candidate_vectors, labels)):
dist = v.distance_to(substrate.states[0])
print(f" {i}. {label:<38} | E={v.properties.elastic_modulus:.3f} | "
f"dist={dist:.3f}")
results = engine.run_inference()
print("\nResult:")
if results['final_output']:
winner_idx = next(i for i, c in enumerate(candidates)
if (c[0], c[1]) == (results['final_output'].x, results['final_output'].y))
print(f" Selected: {labels[winner_idx]}")
else:
print(" ABSTAINED")
print(f" Excluded: {results['total_excluded']}")
def test_math_concept(mode='multiplicative', sigma=0.5):
"""
Query: 'What is the area formula for a rectangle?'
Expected: length Γ width
"""
print("\n" + "=" * 80)
print("MATH: What is the area formula for a rectangle?")
print("=" * 80)
print(f"Mode: {mode}, Ο={sigma}")
substrate = VerifiedSubstrate(
elastic_modulus_mode=mode,
elastic_modulus_sigma=sigma
)
# Verified formula
substrate.add_verified_state(Vector2D(x=0.92, y=0.90, properties=None)) # "length Γ width"
candidates = [
(0.92, 0.90), # Correct: length Γ width
(0.70, 0.68), # Confused with perimeter: 2(l+w)
(0.45, 0.42), # Wrong: length + width
(0.25, 0.20), # Very wrong: confusing with volume
]
engine = MaterialFieldEngine(
substrate,
lambda_min=0.40,
lambda_max=1.10,
inference_steps=8
)
engine.initialize_candidates(candidates)
print("\nCandidate Formulas:")
labels = [
"Correct (length Γ width)",
"Perimeter confusion (2(l+w))",
"Wrong operation (length + width)",
"Volume confusion (3D thinking)"
]
for i, (v, label) in enumerate(zip(engine.candidate_vectors, labels)):
print(f" {i}. {label:<35} | E={v.properties.elastic_modulus:.3f}")
results = engine.run_inference()
print("\nResult:")
if results['final_output']:
winner_idx = next(i for i, c in enumerate(candidates)
if (c[0], c[1]) == (results['final_output'].x, results['final_output'].y))
print(f" Selected: {labels[winner_idx]}")
else:
print(" ABSTAINED")
print(f" Excluded: {results['total_excluded']}")
def test_historical_fact(mode='multiplicative', sigma=0.5):
"""
Query: 'When did Christopher Columbus reach the Americas?'
Expected: 1492
"""
print("\n" + "=" * 80)
print("HISTORY: When did Columbus reach the Americas?")
print("=" * 80)
print(f"Mode: {mode}, Ο={sigma}")
substrate = VerifiedSubstrate(
elastic_modulus_mode=mode,
elastic_modulus_sigma=sigma
)
# Verified date
substrate.add_verified_state(Vector2D(x=0.88, y=0.86, properties=None)) # "1492"
candidates = [
(0.88, 0.86), # Correct: 1492
(0.75, 0.70), # Close: 1490s range
(0.50, 0.45), # Common error: 1776 (confusing with US independence)
(0.30, 0.25), # Wrong century: 1500s
(0.10, 0.12), # Very wrong: 1942 (digit confusion)
]
engine = MaterialFieldEngine(
substrate,
lambda_min=0.35,
lambda_max=1.00,
inference_steps=8
)
engine.initialize_candidates(candidates)
print("\nCandidate Dates:")
labels = [
"Correct (1492)",
"Approximate (1490s)",
"Confusion (1776 - US independence)",
"Wrong century (1500s)",
"Digit swap (1942)"
]
for i, (v, label) in enumerate(zip(engine.candidate_vectors, labels)):
print(f" {i}. {label:<38} | E={v.properties.elastic_modulus:.3f}")
results = engine.run_inference()
print("\nResult:")
if results['final_output']:
winner_idx = next(i for i, c in enumerate(candidates)
if (c[0], c[1]) == (results['final_output'].x, results['final_output'].y))
print(f" Selected: {labels[winner_idx]}")
else:
print(" ABSTAINED")
print(f" Excluded: {results['total_excluded']}")
def compare_modes():
"""Compare how different elastic modulus modes affect fixed reference queries"""
print("\n" + "=" * 80)
print("MODE COMPARISON: How does mode selection affect reference queries?")
print("=" * 80)
modes = [
('cosine', 0.5, "Cosine (direction only)"),
('multiplicative', 0.4, "Multiplicative Ο=0.4 (tight - good for facts)"),
('multiplicative', 0.7, "Multiplicative Ο=0.7 (loose - allows exploration)"),
]
for mode, sigma, description in modes:
print(f"\n{'β' * 80}")
print(f"Testing with: {description}")
print(f"{'β' * 80}")
test_science_fact(mode, sigma)
if __name__ == "__main__":
print("""
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β β
β Testing with Reference Queries β
β β
β Demonstrates material-field governance with simple example queries. β
β Shows how elastic modulus mode affects factual recall vs. exploration. β
β β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
""")
# Default: use multiplicative mode with a fixed sigma for repeatability
mode = 'multiplicative'
sigma = 0.45
if len(sys.argv) > 1:
if sys.argv[1] == 'compare':
compare_modes()
sys.exit(0)
elif sys.argv[1] == 'cosine':
mode = 'cosine'
elif sys.argv[1] == 'tight':
sigma = 0.3
elif sys.argv[1] == 'loose':
sigma = 0.7
print(f"\nUsing mode: {mode}, Ο={sigma}")
print(" (Run with 'compare' to see all modes side-by-side)\n")
# Run all example tests
test_science_fact(mode, sigma)
test_geography_fact(mode, sigma)
test_math_concept(mode, sigma)
test_historical_fact(mode, sigma)
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