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# 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
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