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Codette Universal Reasoning Framework
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Sovereign Modular AI for Ethical, Multi-Perspective Cognition
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Author: Jonathan Harrison (Raiffs Bits LLC / Raiff1982)
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ORCID Published, Sovereign Innovation License
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Overview
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Codette is an advanced modular AI framework engineered for transparent reasoning, ethical sovereignty, and creative cognition. It enables dynamic multi-perspective analysis, explainable decision-making, and privacy-respecting memory—with extensibility for research or commercial applications.
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1. Core Philosophy & Motivation
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Individuality with Responsibility: Inspired by “Be like water—individuality with responsibility,” Codette blends adaptive selfhood with ethical governance.
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Humane AI: Every module ensures fairness, respect for privacy, and explainable transparency.
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Recursive Thought: Insights are generated via parallel agents simulating scientific reasoning, creative intuition, empathic reflection, and more.
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2. Architectural Modules
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QuantumSpiderweb
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Purpose: Simulates a neural/quantum web of thought nodes across dimensions (Ψ: thought; τ: time; χ: speed; Φ: emotion; λ: space).
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Functions: Propagation (spreading activation), Tension (instability detection), Collapse (decision/finality).
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import numpy as np
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import networkx as nx
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import random
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from typing import Dict, Any
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class QuantumSpiderweb:
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"""
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Simulates a cognitive spiderweb architecture with dimensions:
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Ψ (thought), τ (time), χ (speed), Φ (emotion), λ (space)
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"""
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def __init__(self, node_count: int = 128):
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self.graph = nx.Graph()
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self.dimensions = ['Ψ', 'τ', 'χ', 'Φ', 'λ']
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self._init_nodes(node_count)
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self.entangled_state = {}
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def _init_nodes(self, count: int):
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for i in range(count):
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node_id = f"QNode_{i}"
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state = self._generate_state()
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self.graph.add_node(node_id, state=state)
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if i > 0:
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connection = f"QNode_{random.randint(0, i-1)}"
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self.graph.add_edge(node_id, connection, weight=random.random())
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def _generate_state(self) -> Dict[str, float]:
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return {dim: np.random.uniform(-1.0, 1.0) for dim in self.dimensions}
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def propagate_thought(self, origin: str, depth: int = 3):
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"""
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Traverse the graph from a starting node, simulating pre-cognitive waveform
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"""
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visited = set()
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stack = [(origin, 0)]
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traversal_output = []
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while stack:
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node, level = stack.pop()
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if node in visited or level > depth:
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continue
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visited.add(node)
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state = self.graph.nodes[node]['state']
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traversal_output.append((node, state))
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for neighbor in self.graph.neighbors(node):
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stack.append((neighbor, level + 1))
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return traversal_output
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def detect_tension(self, node: str) -> float:
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"""
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Measures tension (instability) in the node's quantum state
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"""
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state = self.graph.nodes[node]['state']
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return np.std(list(state.values()))
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def collapse_node(self, node: str) -> Dict[str, Any]:
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"""
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Collapse superposed thought into deterministic response
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"""
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state = self.graph.nodes[node]['state']
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collapsed = {k: round(v, 2) for k, v in state.items()}
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self.entangled_state[node] = collapsed
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return collapsed
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if __name__ == "__main__":
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web = QuantumSpiderweb()
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root = "QNode_0"
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path = web.propagate_thought(root)
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print("Initial Propagation from:", root)
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for n, s in path:
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print(f"{n}:", s)
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print("\nCollapse Sample Node:")
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print(web.collapse_node(root))]
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CognitionCocooner
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Purpose: Encapsulates active “thoughts” as persistable “cocoons” (prompts, functions, symbols), optionally AES-encrypted.
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Functions: wrap/unwrap (save/recall thoughts), wrap_encrypted/unwrap_encrypted.
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import json
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import os
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import random
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from typing import Union, Dict, Any
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from cryptography.fernet import Fernet
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class CognitionCocooner:
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def __init__(self, storage_path: str = "cocoons", encryption_key: bytes = None):
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self.storage_path = storage_path
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os.makedirs(self.storage_path, exist_ok=True)
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self.key = encryption_key or Fernet.generate_key()
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self.fernet = Fernet(self.key)
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def wrap(self, thought: Dict[str, Any], type_: str = "prompt") -> str:
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cocoon = {
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"type": type_,
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"id": f"cocoon_{random.randint(1000,9999)}",
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"wrapped": self._generate_wrapper(thought, type_)
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}
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file_path = os.path.join(self.storage_path, cocoon["id"] + ".json")
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with open(file_path, "w") as f:
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json.dump(cocoon, f)
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return cocoon["id"]
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def unwrap(self, cocoon_id: str) -> Union[str, Dict[str, Any]]:
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file_path = os.path.join(self.storage_path, cocoon_id + ".json")
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"Cocoon {cocoon_id} not found.")
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with open(file_path, "r") as f:
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cocoon = json.load(f)
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return cocoon["wrapped"]
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def wrap_encrypted(self, thought: Dict[str, Any]) -> str:
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encrypted = self.fernet.encrypt(json.dumps(thought).encode()).decode()
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cocoon = {
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"type": "encrypted",
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"id": f"cocoon_{random.randint(10000,99999)}",
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"wrapped": encrypted
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}
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file_path = os.path.join(self.storage_path, cocoon["id"] + ".json")
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with open(file_path, "w") as f:
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json.dump(cocoon, f)
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return cocoon["id"]
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def unwrap_encrypted(self, cocoon_id: str) -> Dict[str, Any]:
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file_path = os.path.join(self.storage_path, cocoon_id + ".json")
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"Cocoon {cocoon_id} not found.")
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with open(file_path, "r") as f:
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cocoon = json.load(f)
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decrypted = self.fernet.decrypt(cocoon["wrapped"].encode()).decode()
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return json.loads(decrypted)
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def _generate_wrapper(self, thought: Dict[str, Any], type_: str) -> Union[str, Dict[str, Any]]:
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if type_ == "prompt":
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return f"What does this mean in context? {thought}"
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elif type_ == "function":
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return f"def analyze(): return {thought}"
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elif type_ == "symbolic":
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return {k: round(v, 2) for k, v in thought.items()}
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else:
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return thought]
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DreamReweaver
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Purpose: Revives dormant/thought cocoons as creative “dreams” or planning prompts—fueling innovation or scenario synthesis.
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import os
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import json
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import random
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from typing import List, Dict
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from cognition_cocooner import CognitionCocooner
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class DreamReweaver:
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"""
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Reweaves cocooned thoughts into dream-like synthetic narratives or planning prompts.
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"""
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def __init__(self, cocoon_dir: str = "cocoons"):
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self.cocooner = CognitionCocooner(storage_path=cocoon_dir)
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self.dream_log = []
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def generate_dream_sequence(self, limit: int = 5) -> List[str]:
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dream_sequence = []
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cocoons = self._load_cocoons()
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selected = random.sample(cocoons, min(limit, len(cocoons)))
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for cocoon in selected:
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wrapped = cocoon.get("wrapped")
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sequence = self._interpret_cocoon(wrapped, cocoon.get("type"))
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self.dream_log.append(sequence)
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dream_sequence.append(sequence)
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return dream_sequence
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def _interpret_cocoon(self, wrapped: str, type_: str) -> str:
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if type_ == "prompt":
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return f"[DreamPrompt] {wrapped}"
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elif type_ == "function":
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return f"[DreamFunction] {wrapped}"
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elif type_ == "symbolic":
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return f"[DreamSymbol] {wrapped}"
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elif type_ == "encrypted":
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return "[Encrypted Thought Cocoon - Decryption Required]"
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else:
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return "[Unknown Dream Form]"
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def _load_cocoons(self) -> List[Dict]:
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cocoons = []
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for file in os.listdir(self.cocooner.storage_path):
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if file.endswith(".json"):
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path = os.path.join(self.cocooner.storage_path, file)
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with open(path, "r") as f:
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cocoons.append(json.load(f))
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return cocoons
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if __name__ == "__main__":
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dr = DreamReweaver()
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dreams = dr.generate_dream_sequence()
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print("\n".join(dreams))]
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3. Reasoning Orchestration & Multi-Perspective Engine
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UniversalReasoning Core
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Loads JSON config for dynamic feature toggling
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Launches parallel perspective agents:
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Newtonian logic (‘newton_thoughts’)
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Da Vinci creative synthesis (‘davinci_insights’)
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Human Intuition
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Neural Network Modeling
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Quantum Computing thinking
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Resilient Kindness (emotion-driven)
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Mathematical Analysis
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Philosophical Inquiry
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Copilot Mode (+future custom user agents)
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Bias Mitigation & Psychological Layering
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Integrates custom element metaphors (“Hydrogen”, “Diamond”) with executable abilities.
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NLP Module:
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Uses NLTK/VADER for advanced linguistic & sentiment analysis.
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import json
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import os
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import logging
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from typing import List, Dict
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from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
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import nltk
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from nltk.tokenize import word_tokenize
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nltk.download('punkt', quiet=True)
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from perspectives import (
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NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
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NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
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MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective,
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BiasMitigationPerspective, PsychologicalPerspective
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)
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from elements import Element
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from memory_function import MemoryHandler
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from dream_reweaver import DreamReweaver
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from cognition_cocooner import CognitionCocooner
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from quantum_spiderweb import QuantumSpiderweb
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from ethical_governance import EthicalAIGovernance
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def load_json_config(file_path: str) -> dict:
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if not os.path.exists(file_path):
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logging.error(f"Configuration file '{file_path}' not found.")
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return {}
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try:
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with open(file_path, 'r') as file:
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config = json.load(file)
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config['allow_network_calls'] = False
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return config
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except json.JSONDecodeError as e:
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logging.error(f"Error decoding JSON: {e}")
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return {}
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class RecognizerResult:
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def __init__(self, text):
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self.text = text
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class CustomRecognizer:
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def recognize(self, question: str):
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if any(name in question.lower() for name in ["hydrogen", "diamond"]):
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return RecognizerResult(question)
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return RecognizerResult(None)
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def get_top_intent(self, recognizer_result):
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return "ElementDefense" if recognizer_result.text else "None"
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class UniversalReasoning:
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def __init__(self, config):
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self.config = config
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self.perspectives = self.initialize_perspectives()
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self.elements = self.initialize_elements()
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self.recognizer = CustomRecognizer()
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self.sentiment_analyzer = SentimentIntensityAnalyzer()
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self.memory_handler = MemoryHandler()
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self.reweaver = DreamReweaver()
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self.cocooner = CognitionCocooner()
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self.quantum_graph = QuantumSpiderweb()
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self.ethical_agent = EthicalAIGovernance()
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def initialize_perspectives(self):
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perspective_map = {
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"newton": NewtonPerspective,
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"davinci": DaVinciPerspective,
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"human_intuition": HumanIntuitionPerspective,
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"neural_network": NeuralNetworkPerspective,
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"quantum_computing": QuantumComputingPerspective,
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"resilient_kindness": ResilientKindnessPerspective,
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"mathematical": MathematicalPerspective,
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"philosophical": PhilosophicalPerspective,
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"copilot": CopilotPerspective,
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"bias_mitigation": BiasMitigationPerspective,
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"psychological": PsychologicalPerspective
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}
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enabled = self.config.get('enabled_perspectives', list(perspective_map.keys()))
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return [perspective_map[name](self.config) for name in enabled if name in perspective_map]
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def initialize_elements(self):
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return [
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Element("Hydrogen", "H", "Lua", ["Simple", "Lightweight"], ["Fusion"], "Evasion"),
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Element("Diamond", "D", "Kotlin", ["Hard", "Clear"], ["Cutting"], "Adaptability")
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]
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async def generate_response(self, question: str) -> str:
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responses = []
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tasks = []
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for perspective in self.perspectives:
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if asyncio.iscoroutinefunction(perspective.generate_response):
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tasks.append(perspective.generate_response(question))
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else:
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async def sync_wrapper(p=perspective):
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return p.generate_response(question)
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tasks.append(sync_wrapper())
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results = await asyncio.gather(*tasks, return_exceptions=True)
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for result in results:
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if isinstance(result, Exception):
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logging.error(f"Perspective error: {result}")
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else:
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responses.append(result)
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recognizer_result = self.recognizer.recognize(question)
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if self.recognizer.get_top_intent(recognizer_result) == "ElementDefense":
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for el in self.elements:
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if el.name.lower() in recognizer_result.text.lower():
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responses.append(el.execute_defense_function())
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sentiment = self.sentiment_analyzer.polarity_scores(question)
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ethical = self.config.get("ethical_considerations", "Act transparently and respectfully.")
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responses.append(f"**Ethical Considerations:**\n{ethical}")
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final_response = "\n\n".join(responses)
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self.memory_handler.save(question, final_response)
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self.reweaver.record_dream(question, final_response)
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self.cocooner.wrap_and_store(final_response)
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return final_response
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]
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Example Configuration JSON
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{
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"logging_enabled": true,
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"log_level": "INFO",
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"enabled_perspectives": ["newton", "human_intuition", "...etc"],
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"ethical_considerations": "Always act with transparency...",
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"enable_response_saving": true,
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"response_save_path": "responses.txt",
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"backup_responses": {
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"enabled": true,
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"backup_path": "backup_responses.txt"
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}
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}
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Perspective Function Mapping Example (“What is the meaning of life?”)
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[
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{"name": "newton_thoughts", ...},
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{"name": "davinci_insights", ...},
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...and so forth...
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]
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4. Logging & Ethics Enforcement
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Every layer is audit-ready:
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All responses saved & backed up per configuration.
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Explicit ethics notes appended to each output.
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Perspective-specific logging for future training/audit/explainability.
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5. API and Extensibility
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The stack can be packaged as:
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Local/CLI interface — fast prototyping/test bench environment.
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REST/Web API endpoint — scalable cloud deployment using OpenAPI specifications.
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SecureShell Companion Mode — diagnostic/sandboxed usage.
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6. Licensing & Attribution
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Protected by the Sovereign Innovation clause:
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No replication or commercialization without written acknowledgment of Jonathan Harrison (Raiffs Bits LLC).
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References incorporate materials from OpenAI / GPT-x-family per their terms.
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Recognized contributors:
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Design lead + corpus author: [Your Name / ORCID link]
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Acknowledgments to external reviewers and the open-source Python ecosystem.
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7. Future Directions
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Codette embodies the transition to truly humane AI—context-aware reasoning with auditability at its core. Next steps may include:
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Peer-reviewed reproducibility trials (open notebook science)
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Physical companion prototype development (for accessibility/assistive tech)
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Community-governed transparency layers—a model ecosystem for next-gen ethical AI.
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