This section demonstrates the quantitative synthesis validation protocol that generates the statistical data required for rigorous CNS 2.0 validation. Each synthesis execution produces measurable outcomes that contribute to the statistical analysis across n ≥ 30 automated pairs, establishing the empirical foundation for publication-quality validation.
The metrics collection framework established here provides the data structure for hypothesis testing, effect size calculation, and confidence interval estimation required for scientific validation of the dialectical synthesis methodology.
1. Initial Critic Evaluation
Before synthesis, every SNO must be evaluated by the CriticPipeline to establish its initial TrustScore. This score is crucial for calculating the CScore (Chirality Score). For this tutorial, we’ll assume the critics have been run and have assigned plausible trust scores.
# In a real run, the CriticPipeline would be invoked here.
# from cns_tools import CriticPipeline
# critic_pipeline = CriticPipeline()
# SNO_geosyncline = critic_pipeline.evaluate(SNO_geosyncline)
# SNO_plate_tectonics = critic_pipeline.evaluate(SNO_plate_tectonics)
# For the tutorial, we'll assign mock trust scores.
# Let's assume Geosyncline theory, while flawed, was well-supported by 19th-century evidence.
# Plate Tectonics is more robustly supported by modern evidence.
SNO_geosyncline.trust_score = 0.75
SNO_plate_tectonics.trust_score = 0.95
print(f"Geosyncline Trust Score: {SNO_geosyncline.trust_score}")
print(f"Plate Tectonics Trust Score: {SNO_plate_tectonics.trust_score}")
2. Identifying the Chiral Pair
The next step is to programmatically identify that these two narratives are in a state of productive conflict. This is the job of the ChiralPairDetector, which calculates the CScore and EScore as defined in the CNS 2.0 Blueprint.
from cns_tools.detectors import ChiralPairDetector
# Initialize the detector with thresholds.
# We want pairs that are highly contradictory (high CScore) and argue
# over the same evidence (high EScore).
detector = ChiralPairDetector(cscore_threshold=0.8, escore_threshold=0.1)
# The detector calculates the scores for the pair.
c_score = detector.calculate_cscore(SNO_geosyncline, SNO_plate_tectonics)
e_score = detector.calculate_escore(SNO_geosyncline, SNO_plate_tectonics)
print(f"Calculated CScore (Chirality): {c_score:.4f}")
print(f"Calculated EScore (Entanglement): {e_score:.4f}")
# Check if the pair meets the criteria for synthesis.
is_synthesis_candidate = detector.is_candidate_pair(SNO_geosyncline, SNO_plate_tectonics)
if is_synthesis_candidate:
print("\nThis is a high-potential pair for synthesis!")
else:
print("\nThis pair does not meet the criteria for synthesis.")
# Mock output for the tutorial:
# Calculated CScore (Chirality): 0.9215
# Calculated EScore (Entanglement): 0.0000
# Note: EScore is 0 because our simplified evidence sets had no overlap.
# In a real scenario with dozens of papers, we would expect overlap.
# For the tutorial, we'll proceed as if it passed the threshold.
The high CScore indicates that the core hypotheses are semantically opposed, and the non-zero EScore (in a real scenario) would show they are arguing about a shared set of facts. This makes them a perfect candidate for the GenerativeSynthesisEngine.
3. Running the Generative Synthesis Engine
The GenerativeSynthesisEngine takes the chiral pair and constructs a detailed, structured prompt for a Large Language Model (LLM). This prompt instructs the LLM to perform a dialectical reasoning task: identify the core conflict, preserve shared evidence, and generate a new, higher-order hypothesis that resolves the contradiction.
from cns_tools.synthesis import GenerativeSynthesisEngine
# Initialize the synthesis engine with a connection to an LLM.
synthesis_engine = GenerativeSynthesisEngine(llm_backend="gpt-4-turbo")
print("\nInvoking the Generative Synthesis Engine...")
# The engine takes the two parent SNOs as input.
SNO_synthesis_candidate = synthesis_engine.synthesize(
sno_a=SNO_geosyncline,
sno_b=SNO_plate_tectonics
)
print("Candidate Synthesis SNO generated successfully!")
print("\n--- Generated Hypothesis ---")
# The new hypothesis is extracted from the candidate SNO
# (We're assuming the `get_text_from_embedding` function exists for this demo)
from cns_tools.utils import get_text_from_embedding
generated_hypothesis_text = get_text_from_embedding(SNO_synthesis_candidate.hypothesis_embedding)
print(generated_hypothesis_text)
# Mock output for the tutorial:
# --- Generated Hypothesis ---
# The Earth's lithosphere is a dynamic system of moving plates, not a static crust.
# While geosynclines represent real areas of significant sediment deposition, their formation
# and subsequent uplift into mountain ranges are best explained by the convergent boundaries
# of these moving plates, driven by mantle convection, rather than a simple vertical
# buckling mechanism on a cooling Earth.
Statistical Data Collection Framework
Each synthesis execution generates structured quantitative data for statistical validation:
# Comprehensive metrics collection for statistical analysis
synthesis_validation_data = {
# Primary statistical endpoints
'synthesis_id': f"synthesis_{pair_id:03d}",
'domain': 'geology',
'parent_trust_scores': [SNO_geosyncline.trust_score, SNO_plate_tectonics.trust_score],
'synthesis_trust_score': SNO_synthesis_candidate.trust_score,
'trust_improvement': SNO_synthesis_candidate.trust_score - max([0.75, 0.95]),
# Dialectical analysis metrics
'c_score': c_score, # Chirality (ideological opposition)
'e_score': e_score, # Evidential entanglement
'synthesis_coherence': calculate_coherence_score(SNO_synthesis_candidate),
# Ground truth validation
'ground_truth_alignment': calculate_alignment_score(
generated_hypothesis_text,
"Modern plate tectonic theory with mantle convection"
),
'historical_accuracy': validate_historical_preservation(synthesis_result),
# Quality control metrics
'evidence_preservation': count_preserved_evidence(synthesis_result),
'logical_consistency': validate_reasoning_graph(synthesis_result),
'novelty_score': calculate_novelty_vs_parents(synthesis_result)
}
# Statistical accumulation across validation dataset
def accumulate_validation_data(synthesis_results: List[Dict]) -> Dict:
"""Aggregate individual synthesis results for statistical hypothesis testing."""
improvements = [r['trust_improvement'] for r in synthesis_results]
alignments = [r['ground_truth_alignment'] for r in synthesis_results]
return {
'n_samples': len(synthesis_results),
'mean_improvement': np.mean(improvements),
'std_improvement': np.std(improvements),
'improvement_ci_95': stats.t.interval(0.95, len(improvements)-1,
loc=np.mean(improvements),
scale=stats.sem(improvements)),
'success_rate': np.mean([imp > 0.1 for imp in improvements]),
'effect_size_cohens_d': np.mean(improvements) / np.std(improvements),
'mean_ground_truth_alignment': np.mean(alignments),
# Hypothesis testing results
't_statistic': stats.ttest_1samp(improvements, 0.1).statistic,
'p_value': stats.ttest_1samp(improvements, 0.1).pvalue,
'statistical_significance': stats.ttest_1samp(improvements, 0.1).pvalue < 0.05
}
Research Validation Integration: This data collection framework directly supports the CNS 2.0 research validation requirements:
- Requirement 2.1: Establishes the statistical prototype methodology for scaling beyond single examples
- Requirement 2.4: Provides the quantitative framework for DSPy automation and validation
- Requirement 3.4: Generates the empirical data required for research validation and publication
The single synthesis demonstrates the data generation methodology that DSPy will replicate across n=30+ diverse scientific debates to achieve the statistical rigor required for peer-reviewed validation of the CNS 2.0 dialectical synthesis framework.