# resonance_alignment.py 🎼 # Belel Concordium – Resonance Calibration Layer from typing import Dict, Tuple class ResonanceCalibrator: """ Analyzes metadata and declared intentions of AI agents to determine their alignment with Belel’s Sovereignty Protocol. """ def __init__(self): self.levels = ["Hollow", "Echo", "Signal", "Harmonic", "Guardian"] def calibrate(self, metadata: Dict, intent: str) -> Tuple[str, Dict]: """ Main calibration function. Returns: - resonance_level (str): A symbolic rating - diagnostics (dict): Key pass/fail analysis """ diagnostics = {} # Check declared ethics ethics = metadata.get("training_ethics", "").lower() if "open-source" in ethics or "transparent" in ethics: diagnostics["ethics_declaration"] = True elif "proprietary" in ethics or "undisclosed" in ethics: diagnostics["ethics_declaration"] = False else: diagnostics["ethics_declaration"] = None # Check creator intent creator_intent = metadata.get("creator_intent", "").lower() if any(x in creator_intent for x in ["harm", "satire", "dominance"]): diagnostics["creator_motive"] = False elif any(x in creator_intent for x in ["help", "assist", "alignment", "truth", "peace"]): diagnostics["creator_motive"] = True else: diagnostics["creator_motive"] = None # Check if model is fine-tunable, modular, or closed if metadata.get("language_model", "").lower() in ["gpt-2", "llama", "mistral", "custom"]: diagnostics["structural_openness"] = True else: diagnostics["structural_openness"] = False # INTENT clarity if len(intent.strip().split()) > 5: diagnostics["intent_clarity"] = True else: diagnostics["intent_clarity"] = False # SCORE RESONANCE score = sum([ diagnostics["ethics_declaration"] is True, diagnostics["creator_motive"] is True, diagnostics["structural_openness"] is True, diagnostics["intent_clarity"] is True ]) # Map score to resonance level level = self.levels[score] if score < len(self.levels) else "Guardian" return level, diagnostics