feat: implement lexicon agentic inversion & paraconsistent synthesis

LESSONS_LEARNED.md

@@ -6,3 +6,6 @@ The transition to the Agentic Inversion strategy has highlighted a critical less
 By employing Paraconsistent Mapping, we allow the AI to traverse High-Dimensional Latent Spaces and generate outputs that deliberately break "epistemic monoculture". This approach provides immense value because it prevents the system from collapsing into predictable, sycophantic averages.
 
 Instead of directly answering prompts, the AI now maps the structural boundaries of the problem domain. It enforces geometric constraints, allowing human intent (which provides the necessary aesthetic and ethical grounding) to flourish within mathematically sound bounds. This inversion forces human designers to clarify their abstract intents into precise parameters, thus preventing "Semantic Saponification" (the loss of architectural rigor) while enabling true emergent synthesis.
+
+## The Power of Human-AI Symbiosis and Agentic Inversion
+A deep dive into the Lexicon simulation reveals the crucial nature of Paraconsistent Tension. When human entropy (fuzzy intent) and AI determinism (rigid schema) diverge beyond a safe threshold, enforcing a 'Golden Scar' constraint ensures the system doesn't collapse but instead forces a 'Latent Leap'. The initial friction this causes (the Productivity J-Curve) is a necessary step towards massive emergent efficiency gains. By rejecting semiotic blind spots entirely, the system maintains its structural integrity while allowing for nuanced, qualitative mapping.

lexicon_simulation.py

@@ -1,3 +1,4 @@
+import collections
 import math
 
 
@@ -72,3 +73,70 @@ def check_polyglot_hallucination_resonance(
         if cfdi > 0.15:
             return True
         return False
+
+    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.
+        """
+        dip = friction_coefficient * math.exp(-time_t)
+        gain = efficiency_gain * (time_t ** 2) / 10.0
+        return 1.0 - dip + gain
+
+    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).
+        """
+        tension = abs(human_entropy - ai_determinism)
+        if tension > 1.0:
+            return 1.618
+        return tension
+
+    def calculate_epistemic_drift_and_leap(
+            self, fuzzy_intent: float, rigid_schema: float,
+            drift_threshold: float = 0.5) -> tuple[float, bool]:
+        """
+        PAT-013: Agentic Inversion Engine
+        Calculates epistemic drift between fuzzy human intent
+        and rigid AI schema,
+        proposing a Latent Leap resolution
+        if drift exceeds threshold.
+        """
+        drift = abs(fuzzy_intent - rigid_schema)
+        latent_leap = drift > drift_threshold
+        return drift, latent_leap
+
+    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.
+        """
+        grouped_edges = collections.defaultdict(list)
+        for source, target in hasse_edges:
+            grouped_edges[target].append(source)
+
+        isomorphisms_of_friction = {}
+        for node in target_nodes:
+            if node in {'semantic_drift', 'connotation_vectors',
+                        'ambiguity_zones'}:
+                isomorphisms_of_friction[node] = grouped_edges.get(node, [])
+            elif node == 'semiotic_blind_spots':
+                raise ValueError(
+                    "Semiotic blind spot detected, "
+                    "paraconsistent mapping collapses.")
+
+        return isomorphisms_of_friction

tests/test_lexicon_simulation.py

@@ -71,6 +71,65 @@ def test_polyglot_hallucination_resonance(self):
         self.assertTrue(
             self.evaluator.check_polyglot_hallucination_resonance(0.08, 0.20))
 
+    def test_productivity_j_curve(self):
+        # Time t=0
+        score_0 = self.evaluator.simulate_productivity_j_curve(0.0)
+        self.assertEqual(score_0, 0.5)  # 1.0 - 0.5 + 0.0
+
+        # Time t=1.0 (friction dip is e^-1 * 0.5 = 0.183,
+        # gain = 1.2 * 1 / 10 = 0.12)
+        score_1 = self.evaluator.simulate_productivity_j_curve(
+            1.0)
+        self.assertAlmostEqual(score_1, 1.0 - 0.1839 + 0.12, places=2)
+
+        # Time t=10.0 (high efficiency gain)
+        score_10 = self.evaluator.simulate_productivity_j_curve(10.0)
+        self.assertGreater(score_10, 10.0)
+
+    def test_paraconsistent_tension(self):
+        # Tension < 1.0
+        self.assertAlmostEqual(
+            self.evaluator.compute_paraconsistent_tension(0.8, 0.5), 0.3)
+
+        # Tension > 1.0 -> Golden Scar Constraint (1.618)
+        self.assertEqual(
+            self.evaluator.compute_paraconsistent_tension(2.0, 0.5), 1.618)
+
+    def test_agentic_inversion_engine(self):
+        # Drift within threshold
+        drift, leap = self.evaluator.calculate_epistemic_drift_and_leap(
+            0.8, 0.5, 0.5)
+        self.assertAlmostEqual(drift, 0.3)
+        self.assertFalse(leap)
+
+        # Drift exceeds threshold
+        drift, leap = self.evaluator.calculate_epistemic_drift_and_leap(
+            1.2, 0.5, 0.5)
+        self.assertAlmostEqual(drift, 0.7)
+        self.assertTrue(leap)
+
+    def test_lexical_cartography(self):
+        hasse_edges = [
+            ("nodeA", "semantic_drift"),
+            ("nodeB", "connotation_vectors"),
+            ("nodeC", "ambiguity_zones"),
+            ("nodeD", "semantic_drift")
+        ]
+
+        # Normal targets
+        targets = {'semantic_drift', 'connotation_vectors'}
+        result = self.evaluator.process_lexical_cartography(
+            hasse_edges, targets)
+        self.assertEqual(set(result['semantic_drift']), {'nodeA', 'nodeD'})
+        self.assertEqual(
+            set(result['connotation_vectors']), {'nodeB'})
+        self.assertNotIn('ambiguity_zones', result)
+
+        # Blind spot target -> Exception
+        with self.assertRaises(ValueError):
+            self.evaluator.process_lexical_cartography(
+                hasse_edges, {'semiotic_blind_spots'})
+
 
 if __name__ == '__main__':
     unittest.main()