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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}"
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