Chapter 4: Building on the Foundation

The successful completion of our Minimum Viable Experiment (MVE) establishes the foundational proof-of-concept for CNS 2.0. However, the acknowledged limitations—manual SNO creation and heuristic-based evaluation—define precise research objectives for scaling beyond controlled experimentation to autonomous operation.

This chapter specifies two critical research projects comprising the Foundational Work phase, each with mathematical validation frameworks and statistical success criteria. These projects bridge the gap between our manual prototype and the self-optimizing system architecture detailed in the Developer’s Guide Chapter 7, establishing the technical prerequisites for advanced research phases.

Foundational Project #1: The Narrative Ingestion Pipeline

The transition from manual SNO creation to automated ingestion represents a critical scaling bottleneck requiring rigorous experimental validation. This project transforms unstructured text into structured SNOs through DSPy-optimized extraction pipelines.

Mathematical Validation Framework

The ingestion pipeline’s performance is quantified through a composite accuracy metric:

where:

• $\text{Precision}_H$: Hypothesis extraction precision against expert-labeled ground truth
• $\text{Recall}_C$: Claim identification recall across reasoning graph vertices
• $\text{F1}_G$: F1-score for reasoning graph edge reconstruction

Statistical Success Criteria: To ensure our automated pipeline is reliable, we’ve set clear, measurable targets.

• Minimum composite accuracy: 0.75: The pipeline must be correct at least 75% of the time, a result that must be statistically significant (p < 0.05) based on a test of at least 200 documents.
• Inter-annotator agreement (Cohen’s κ) ≥ 0.70: This measures the level of agreement between our automated system and human experts, with κ ≥ 0.70 indicating substantial agreement.
• Effect size (Cohen’s d) ≥ 0.8: We are aiming for a large (d ≥ 0.8) improvement over simpler, non-optimized approaches.

DSPy Optimization Integration

The pipeline leverages the DSPy compilation framework through programmatic prompt optimization:

class DocumentToSNO(dspy.Signature):
    """Extracts structured narrative components from academic text."""
    document_text: str = dspy.InputField()
    central_hypothesis: str = dspy.OutputField()
    claims: List[ExtractedClaim] = dspy.OutputField()
    reasoning_edges: List[ReasoningEdge] = dspy.OutputField()

The optimization process uses our multi-component critic pipeline as the objective function, creating a self-improving extraction system where ingestion quality is measured by the system’s own evaluation standards.

Resource Requirements and Timeline

Technical Prerequisites:

• DSPy framework integration (2 developer-months)
• Validation dataset creation: 500 expert-annotated documents (6 researcher-months)
• Multi-model evaluation infrastructure (1 developer-month)

Estimated Timeline: 12 months

• Months 1-3: Dataset creation and annotation protocol establishment
• Months 4-8: DSPy pipeline development and initial optimization
• Months 9-12: Statistical validation and performance benchmarking

Computational Resources:

• Training: 100 GPU-hours for DSPy optimization across model variants
• Evaluation: 50 GPU-hours for statistical significance testing

Foundational Project #2: From Heuristics to a Data-Driven Critic

The evolution from heuristic-based evaluation to learned models requires systematic validation of improved performance across logical coherence and evidential grounding assessment. This project replaces the transparent heuristics detailed in Developer’s Guide Chapter 3 with statistically validated machine learning models.

Mathematical Validation Framework

Grounding Critic Enhancement: The NLI-based grounding model performance is measured through:

Statistical Success Criteria:

• Minimum AUC improvement: 0.10: The new model must be at least 10% better than the old one, an improvement that is highly statistically significant (p < 0.01) based on a large dataset.
• Cross-validation stability: σ(AUC) ≤ 0.02: This ensures the model’s performance is consistent and not a fluke, by checking that the performance variation is low across different subsets of the data.
• Calibration error ≤ 0.05: This ensures that when the model says it’s “90% confident,” it’s correct about 90% of the time, making its confidence scores reliable.

Logic Critic Enhancement: The GNN-based logic model validation follows:

where classifications distinguish valid vs. fallacious reasoning graphs.

Statistical Success Criteria:

• Minimum classification accuracy: 0.80: The model must correctly identify valid vs. fallacious reasoning at least 80% of the time, with very high statistical significance (p < 0.001) on a large dataset.
• Precision ≥ 0.75 for fallacy detection: When the model flags an argument as fallacious, it must be correct at least 75% of the time, which helps avoid incorrectly dismissing valid reasoning.
• Recall ≥ 0.85 for valid reasoning identification: The model must successfully identify at least 85% of all the genuinely valid reasoning graphs.

DSPy Self-Optimization Integration

The enhanced critics integrate with the self-optimizing synthesis loop where the improved evaluation models serve as more sophisticated objective functions for DSPy compilation:

def enhanced_critic_pipeline_metric(example, pred, trace=None) -> float:
    """Uses learned NLI and GNN models as DSPy optimization targets."""
    candidate_sno = create_sno_from_prediction(pred)
    
    # Enhanced grounding evaluation
    nli_grounding_score = nli_grounding_critic.evaluate(candidate_sno)
    
    # Enhanced logic evaluation  
    gnn_logic_score = gnn_logic_critic.evaluate(candidate_sno)
    
    # Weighted combination for DSPy optimization
    return 0.4 * nli_grounding_score + 0.4 * gnn_logic_score + 0.2 * novelty_score

This creates a feedback loop where synthesis quality improves through optimization against increasingly sophisticated evaluation criteria.

Resource Requirements and Timeline

Technical Prerequisites:

• Grounding Critic: NLI model fine-tuning infrastructure (1 developer-month)
• Logic Critic: GNN training pipeline and graph dataset creation (4 developer-months)
• Integration: DSPy metric integration and validation framework (2 developer-months)

Dataset Requirements:

• Grounding: 5,000 expert-labeled claim-evidence pairs (8 researcher-months)
• Logic: 3,000 annotated reasoning graphs with validity labels (12 researcher-months)

Estimated Timeline: 18 months

• Months 1-6: Dataset creation and annotation protocols
• Months 7-12: Model development and initial training
• Months 13-18: Statistical validation and DSPy integration

Computational Resources:

• NLI Training: 200 GPU-hours for fine-tuning and hyperparameter optimization
• GNN Training: 500 GPU-hours for architecture search and training
• Validation: 100 GPU-hours for statistical significance testing

Integration with System Architecture

The enhanced critic models integrate seamlessly with the existing multi-component pipeline architecture, maintaining the transparent, weighted evaluation framework while dramatically improving individual component accuracy. This preserves the system’s explainability while achieving the performance necessary for autonomous operation at scale.

The completion of both foundational projects establishes the technical infrastructure for advanced research phases, enabling autonomous CNS 2.0 operation with statistically validated performance guarantees across the complete knowledge discovery pipeline.