feat: implement lexicon agentic inversion & paraconsistent synthesis

[projectedanx]

This PR addresses the requested updates originating from LEXICON.md (DRP-LEXICON-992) for mapping emergent behaviors specifically defined under PAT-011 (Human-AI Symbiosis Engine), PAT-012 (Paraconsistent Synthesis Node), PAT-013 (Agentic Inversion Engine), and PAT-014 (Lexical Cartography).

Updates include:

1. Translating definitions and structural maps into functionally simulated Python structures within PDLLexiconEvaluator.
2. Fully executing and generating rigorous assertions using unittest in the dedicated testing file to validate limits (Golden Scar constraint, drift thresholds, and semiotic blind spot exceptions).
3. Extracting and centralizing relational insights out of the implementation into LESSONS_LEARNED.md.
4. Formatting all code base edits strictly under flake8 rules.

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PR created automatically by Jules for task 4244225502872043818 started by @projectedanx

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[gemini-code-assist]

Code Review

This pull request introduces several new methods to the lexicon_simulation.py module, including simulate_productivity_j_curve, compute_paraconsistent_tension, calculate_epistemic_drift_and_leap, and process_lexical_cartography, along with corresponding unit tests and documentation updates. Feedback focused on improving the robustness and efficiency of these new methods. Specifically, recommendations were made to add input validation for None and negative values in the productivity simulation, replace magic numbers with constants in the tension calculation, and optimize the edge processing logic in the lexical cartography method to improve performance for large datasets.

[gemini-code-assist]

The simulate_productivity_j_curve method lacks validation for the time_t parameter. If a negative value is passed, the exponential decay term math.exp(-time_t) will grow extremely large, potentially leading to overflow or nonsensical simulation results. Additionally, as per general defensive programming practices in Python, a check for None should be included to prevent a TypeError during arithmetic operations.

    def simulate_productivity_j_curve(
            self, time_t: float, friction_coefficient: float = 0.5,
            efficiency_gain: float = 1.2) -> float:
        """
        PAT-011: Human-AI Symbiosis Engine
        Anticipates initial cognitive friction (Productivity J-Curve)
        followed by massive efficiency gains.
        Returns the simulated productivity score.
        """
        if time_t is None:
            return 1.0 - friction_coefficient

        # Ensure time is non-negative for valid simulation results
        t = max(0.0, time_t)
        dip = friction_coefficient * math.exp(-t)
        gain = efficiency_gain * (t ** 2) / 10.0
        return 1.0 - dip + gain

[gemini-code-assist]

The compute_paraconsistent_tension method should include null checks for human_entropy and ai_determinism to avoid a TypeError when calculating their difference. Furthermore, the value 1.618 (the Golden Ratio Φ) is used as a magic number; defining it as a named constant or adding a comment clarifying its significance improves maintainability.

    def compute_paraconsistent_tension(
            self, human_entropy: float, ai_determinism: float) -> float:
        """
        PAT-012: Paraconsistent Synthesis Node
        Tension computation mapping divergent ontological planes
        into an
        Isomorphism of Friction, resolving output
        with the Golden Scar constraint (Φ = 1.618).
        """
        if human_entropy is None or ai_determinism is None:
            return 0.0

        tension = abs(human_entropy - ai_determinism)
        # Golden Scar constraint (Φ ≈ 1.618)
        return 1.618 if tension > 1.0 else tension

[gemini-code-assist]

The current implementation of process_lexical_cartography is inefficient for large inputs because it processes all hasse_edges into a defaultdict before filtering for the requested target_nodes. A more efficient approach is to identify the relevant targets first and only process edges associated with them. Additionally, the set of allowed zones is recreated in every iteration of the loop, and the method lacks robustness against None inputs for hasse_edges or target_nodes.

    def process_lexical_cartography(
            self, hasse_edges: list[tuple[str, str]],
            target_nodes: set[str]) -> dict[str, list[str]]:
        """
        PAT-014: Lexical Cartography
        Processing semantic space through Semantic Drift, Connotation Vectors,
        Semiotic Blind Spots, and Ambiguity Zones
        to extract Isomorphisms of Friction.
        Mechanism: Paraconsistent Hasse lattice mapping.
        """
        if hasse_edges is None or target_nodes is None:
            return {}

        if 'semiotic_blind_spots' in target_nodes:
            raise ValueError(
                "Semiotic blind spot detected, "
                "paraconsistent mapping collapses.")

        allowed_zones = {'semantic_drift', 'connotation_vectors', 'ambiguity_zones'}
        # Identify relevant targets using set intersection for efficiency
        relevant_targets = allowed_zones.intersection(target_nodes)

        grouped_edges = collections.defaultdict(list)
        for source, target in hasse_edges:
            if target in relevant_targets:
                grouped_edges[target].append(source)

        return {node: grouped_edges[node] for node in relevant_targets}