From 6deb5f2e170279a1e938a5f0d195d5adcf133e48 Mon Sep 17 00:00:00 2001 From: projectedanx <238904666+projectedanx@users.noreply.github.com> Date: Fri, 22 May 2026 12:27:01 +0000 Subject: [PATCH] feat: implement lexicon agentic inversion & paraconsistent synthesis - Added PDL Lexicon functionalities (PAT-011 through PAT-014) to `lexicon_simulation.py` covering productivity j-curve, paraconsistent tension, epistemic drift logic, and lexical cartography mappings. - Appended corresponding unit test cases into `tests/test_lexicon_simulation.py` to validate functional behavior. - Documented key high-level findings regarding human-AI symbiosis and topological inversion via agentic inversion strategy to `LESSONS_LEARNED.md`. - Cleared formatting violations resulting in strict `flake8` compliance. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> --- LESSONS_LEARNED.md | 3 ++ fix_flake8.py | 10 ----- fix_test.py | 8 ---- lexicon_simulation.py | 68 ++++++++++++++++++++++++++++++++ tests/test_lexicon_simulation.py | 59 +++++++++++++++++++++++++++ 5 files changed, 130 insertions(+), 18 deletions(-) delete mode 100644 fix_flake8.py delete mode 100644 fix_test.py diff --git a/LESSONS_LEARNED.md b/LESSONS_LEARNED.md index 6654b71..f4a54f1 100644 --- a/LESSONS_LEARNED.md +++ b/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. diff --git a/fix_flake8.py b/fix_flake8.py deleted file mode 100644 index 8ed0d12..0000000 --- a/fix_flake8.py +++ /dev/null @@ -1,10 +0,0 @@ -with open("tactile_dialectician_simulation.py", "r") as f: - lines = f.readlines() - -for i, line in enumerate(lines): - if len(line) > 80: - if "def calculate_geometric_density_score(self, nodes: int, edges: int) -> float:" in line: # noqa: E501 - lines[i] = " def calculate_geometric_density_score(\n self, nodes: int, edges: int) -> float:\n" # noqa: E501 - -with open("tactile_dialectician_simulation.py", "w") as f: - f.writelines(lines) diff --git a/fix_test.py b/fix_test.py deleted file mode 100644 index 5e7076d..0000000 --- a/fix_test.py +++ /dev/null @@ -1,8 +0,0 @@ -with open("tests/test_tactile_dialectician_simulation.py", "r") as f: - content = f.read() - -# Make sure we don't have multiple TestTactileDialecticianV6Evaluator classes, -# which might happen if we accidentally copy pasted the original content into the file. # noqa: E501 -import re -print("Matches found:", len(re.findall( - "class TestTactileDialecticianV6Evaluator", content))) diff --git a/lexicon_simulation.py b/lexicon_simulation.py index 24e88a5..2962ad5 100644 --- a/lexicon_simulation.py +++ b/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 diff --git a/tests/test_lexicon_simulation.py b/tests/test_lexicon_simulation.py index 0d181e2..109f507 100644 --- a/tests/test_lexicon_simulation.py +++ b/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()