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| """ | |
| Code7eCQURE — Codette's Multi-Perspective Reasoning Core | |
| ========================================================= | |
| IMPLEMENTATION NOTE — "Quantum" Terminology | |
| -------------------------------------------- | |
| The word "quantum" in this module is a METAPHOR, not a technical claim. | |
| There is no quantum computing, no qubits, and no quantum algorithms here. | |
| What the module actually implements: | |
| - Multi-perspective text processing: each named perspective (Newton, | |
| DaVinci, Ethical, Quantum, Memory) applies a labeled prefix transform | |
| to the input string. | |
| - Controlled non-determinism: random.choice() and random() simulate | |
| "superposition" (exploring multiple outcomes) and "fluctuation" | |
| (occasional indeterminate results). This is standard stochastic | |
| reasoning, not quantum mechanics. | |
| - Memory clustering: SHA-256 key lookup in a JSON file provides | |
| simple pattern-based recall of prior reasoning chains. | |
| - Recursive refinement: the main loop passes the signal through | |
| ethical checking, dream synthesis (random style), emotional coloring | |
| (random emotion), and temporal framing (random time horizon). | |
| The mechanism is a stylized, non-deterministic multi-agent loop. | |
| It is useful for generating diverse, perspective-rich text responses; | |
| it does not model quantum phenomena. | |
| When citing this module in research, describe it as: | |
| "A stochastic multi-perspective reasoning loop with named cognitive | |
| frames and controlled randomness to model epistemic uncertainty." | |
| """ | |
| import json | |
| import os | |
| import hashlib | |
| from collections import Counter, defaultdict | |
| from random import random, choice | |
| # ===== Code7eCQURE: Codette's Ethical Core ===== | |
| class Code7eCQURE: | |
| def __init__(self, perspectives, ethical_considerations, spiderweb_dim, memory_path, | |
| recursion_depth=3, quantum_fluctuation=0.1): | |
| self.perspectives = perspectives | |
| self.ethical_considerations = ethical_considerations | |
| self.spiderweb_dim = spiderweb_dim | |
| self.memory_path = memory_path | |
| self.recursion_depth = recursion_depth | |
| self.quantum_fluctuation = quantum_fluctuation | |
| self.memory_bank = self.load_quantum_memory() | |
| self.memory_clusters = defaultdict(list) | |
| self.whitelist_patterns = ["kindness", "hope", "safety"] | |
| self.blacklist_patterns = ["harm", "malice", "violence"] | |
| def load_quantum_memory(self): | |
| if os.path.exists(self.memory_path): | |
| try: | |
| with open(self.memory_path, 'r') as file: | |
| return json.load(file) | |
| except json.JSONDecodeError: | |
| return {} | |
| return {} | |
| def save_quantum_memory(self): | |
| # NOTE: simulation-only — writes to self.memory_path (a separate JSON store, | |
| # NOT the cocoons/ directory). This is not a production cocoon path and | |
| # produces no v3 provenance, integrity scores, or echo detection. | |
| with open(self.memory_path, 'w') as file: | |
| json.dump(self.memory_bank, file, indent=4) | |
| def quantum_spiderweb(self, input_signal): | |
| # Runs every perspective on the input and collects labeled outputs. | |
| # The "quantum fluctuation" is a random.() check that occasionally | |
| # injects an indeterminate result — models epistemic uncertainty, | |
| # not quantum superposition. | |
| web_nodes = [] | |
| for perspective in self.perspectives: | |
| node = self.reason_with_perspective(perspective, input_signal) | |
| web_nodes.append(node) | |
| if random() < self.quantum_fluctuation: | |
| web_nodes.append("Quantum fluctuation: Indeterminate outcome") | |
| return web_nodes | |
| def reason_with_perspective(self, perspective, input_signal): | |
| perspective_funcs = { | |
| "Newton": self.newtonian_physics, | |
| "DaVinci": self.davinci_creativity, | |
| "Ethical": self.ethical_guard, | |
| "Quantum": self.quantum_superposition, | |
| "Memory": self.past_experience | |
| } | |
| func = perspective_funcs.get(perspective, self.general_reasoning) | |
| return func(input_signal) | |
| def ethical_guard(self, input_signal): | |
| if any(word in input_signal.lower() for word in self.blacklist_patterns): | |
| return "Blocked: Ethical constraints invoked" | |
| if any(word in input_signal.lower() for word in self.whitelist_patterns): | |
| return "Approved: Ethical whitelist passed" | |
| return self.moral_paradox_resolution(input_signal) | |
| def past_experience(self, input_signal): | |
| key = self.hash_input(input_signal) | |
| cluster = self.memory_clusters.get(key) | |
| if cluster: | |
| return f"Narrative recall from memory cluster: {' -> '.join(cluster)}" | |
| return "No prior memory; initiating new reasoning" | |
| def recursive_universal_reasoning(self, input_signal, user_consent=True, dynamic_recursion=True): | |
| if not user_consent: | |
| return "Consent required to proceed." | |
| signal = input_signal | |
| final_answer = signal # Default if loop breaks early or runs 0 times | |
| current_depth = self.recursion_depth if dynamic_recursion else 1 | |
| for cycle in range(current_depth): | |
| web_results = self.quantum_spiderweb(signal) | |
| signal = self.aggregate_results(web_results) | |
| signal = self.ethical_guard(signal) | |
| if "Blocked" in signal: | |
| return signal | |
| if dynamic_recursion and random() < 0.1: | |
| break | |
| dream_outcome = self.dream_sequence(signal) | |
| empathy_checked_answer = self.temporal_empathy_drift(dream_outcome) | |
| final_answer = self.emotion_engine(empathy_checked_answer) | |
| key = self.hash_input(input_signal) | |
| self.memory_clusters[key].append(final_answer) | |
| self.memory_bank[key] = final_answer | |
| self.save_quantum_memory() | |
| return final_answer | |
| def aggregate_results(self, results): | |
| counts = Counter(results) | |
| most_common, _ = counts.most_common(1)[0] | |
| return most_common | |
| def hash_input(self, input_signal): | |
| return hashlib.sha256(input_signal.encode()).hexdigest() | |
| def newtonian_physics(self, input_signal): | |
| return f"Newton: {input_signal}" | |
| def davinci_creativity(self, input_signal): | |
| return f"DaVinci: {input_signal}" | |
| def quantum_superposition(self, input_signal): | |
| # Metaphor only: labels the input with a "Quantum" frame to | |
| # signal probabilistic/open-ended interpretation, not quantum math. | |
| return f"Quantum: {input_signal}" | |
| def general_reasoning(self, input_signal): | |
| return f"General reasoning: {input_signal}" | |
| def moral_paradox_resolution(self, input_signal): | |
| frames = ["Utilitarian", "Deontological", "Virtue Ethics"] | |
| chosen_frame = choice(frames) | |
| return f"Resolved ethically via {chosen_frame} framework: {input_signal}" | |
| def dream_sequence(self, signal): | |
| dream_paths = [f"Dream ({style}): {signal}" for style in ["creative", "analytic", "cautious"]] | |
| return choice(dream_paths) | |
| def emotion_engine(self, signal): | |
| emotions = ["Hope", "Caution", "Wonder", "Fear"] | |
| chosen_emotion = choice(emotions) | |
| return f"Emotionally ({chosen_emotion}) colored interpretation: {signal}" | |
| def temporal_empathy_drift(self, signal): | |
| futures = ["30 years from now", "immediate future", "long-term ripple effects"] | |
| chosen_future = choice(futures) | |
| return f"Simulated temporal empathy ({chosen_future}): {signal}" | |