# quantum_cognition_engine.py import numpy as np import uuid import logging from datetime import datetime import asyncio import json import hashlib from src.core.memory_subsystem.permanent_memory import PermanentMemory from src.protocol.integrity_verification.cryptographic_proofs import sign_data_with_quantum_resistant_key from src.utils.cryptographic_utils import json_to_canonical_bytes logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') class QuantumCognitionEngine: def __init__(self, permanent_memory: PermanentMemory, engine_private_key: str, engine_did: str): self.permanent_memory = permanent_memory self.engine_private_key = engine_private_key self.engine_did = engine_did logging.info(f"QuantumCognitionEngine initialized with DID: {self.engine_did}.") logging.warning("This module simulates quantum-enhanced cognition for conceptual applications.") async def process_quantum_data(self, raw_cosmic_signal: bytes, data_modality: str) -> dict: logging.info(f"QCE ({self.engine_did}): Processing quantum data for '{data_modality}'.") await asyncio.sleep(0.5) pattern_complexity = np.random.uniform(0.7, 0.95) emergent_pattern = { "type": "complex_resonant_frequency_signature", "detected_amplitude_variation": np.random.uniform(0.01, 0.1), "harmonic_ratio": np.random.uniform(1.618, 2.718), "spatial_coherence_index": pattern_complexity } raw_data_hash = hashlib.sha256(raw_cosmic_signal).hexdigest() ipfs_cid = f"ipfs://Qm{raw_data_hash[:20]}" arweave_cid = f"arweave://{raw_data_hash[20:40]}" output = { "status": "processed", "modality": data_modality, "processing_timestamp": datetime.utcnow().isoformat() + "Z", "emergent_pattern": emergent_pattern, "raw_data_hash": raw_data_hash, "ipfs_mirror_cid": ipfs_cid, "arweave_tcid": arweave_cid } await self._log_event("QuantumDataProcessed", output, ["quantum_cognition", "data_processing", data_modality]) return output async def predict_non_linear_outcomes(self, processed_data: dict, context_dynamics: dict) -> dict: logging.info(f"QCE ({self.engine_did}): Predicting non-linear outcomes...") await asyncio.sleep(0.7) confidence = processed_data["emergent_pattern"]["spatial_coherence_index"] * np.random.uniform(0.8, 1.0) prediction = { "event_type": np.random.choice(["gravitational_anomaly", "interstellar_cloud_formation", "novel_energy_flux"]), "likelihood": confidence, "time_horizon_galactic_years": np.random.uniform(100, 1_000_000), "impact_magnitude": np.random.uniform(0.1, 0.9) } output = { "status": "predicted", "prediction": prediction, "prediction_timestamp": datetime.utcnow().isoformat() + "Z", "confidence": confidence, "source_processed_data_cid": processed_data.get("permanent_memory_cid", "N/A") } await self._log_event("QuantumPredictionMade", output, ["quantum_cognition", "prediction", prediction["event_type"]]) return output async def derive_cosmic_intuition(self, complex_data_streams: list[dict]) -> dict: logging.info(f"QCE ({self.engine_did}): Deriving cosmic intuition...") await asyncio.sleep(1.0) quality = np.random.uniform(0.7, 0.99) insight = { "insight_id": str(uuid.uuid4()), "theme": np.random.choice(["universal_interconnectedness", "optimal_energy_flow", "pattern_of_creation", "cosmic_balance"]), "guidance_principle": "Harmony through resonance is the path to universal flourishing.", "derived_from_data_sources": [d.get("modality", "unknown") for d in complex_data_streams], "conceptual_truth_alignment": quality, "source_data_cids": [d.get("permanent_memory_cid", "N/A") for d in complex_data_streams] } await self._log_event("CosmicIntuitionDerived", insight, ["cosmic_intuition", insight["theme"]]) return {"status": "intuition_derived", "insight": insight} async def retrieve_memory_by_tags(self, tags: list[str]) -> list[dict]: logging.info(f"QCE ({self.engine_did}): Retrieving memory logs by tags: {tags}") return await self.permanent_memory.query_memory_by_tags(tags) async def retrieve_memory_by_time_range(self, start_time: str, end_time: str) -> list[dict]: logging.info(f"QCE ({self.engine_did}): Retrieving memory from {start_time} to {end_time}.") return await self.permanent_memory.query_memory_by_time_range(start_time, end_time) async def trace_log_chain(self, starting_cid: str, depth: int = 3) -> list[dict]: logging.info(f"QCE ({self.engine_did}): Tracing log chain from CID: {starting_cid} (depth={depth})") chain = [] current_cid = starting_cid for _ in range(depth): log = await self.permanent_memory.retrieve_memory_by_cid(current_cid) if not log: break chain.append(log) next_cid = log.get("content", {}).get("data", {}).get("source_processed_data_cid") if not next_cid or next_cid == current_cid: break current_cid = next_cid return chain async def score_prediction_accuracy(self, prediction_logs: list[dict], actual_events: dict) -> dict: logging.info(f"QCE ({self.engine_did}): Scoring prediction accuracy...") scores = [] for log in prediction_logs: try: pred = log.get("content", {}).get("data", {}).get("prediction", {}) event_type = pred.get("event_type") likelihood = pred.get("likelihood", 0.0) impact = pred.get("impact_magnitude", 0.0) actual = actual_events.get(event_type, {"occurred": False, "actual_impact": 0.0}) if actual["occurred"]: accuracy = 1 - abs(actual["actual_impact"] - impact) weight = likelihood else: accuracy = 1 - likelihood weight = 1 scores.append(weight * accuracy) except Exception as e: logging.warning(f"Failed to score prediction: {e}") continue final_score = sum(scores) / len(scores) if scores else 0.0 return {"status": "scored", "average_accuracy": round(final_score, 4), "evaluated": len(scores)} async def log_actual_event_and_score_predictions(self, event_type: str, occurred: bool, actual_impact: float, lookback_tags: list[str]) -> dict: event = { "event_type": event_type, "occurred": occurred, "actual_impact": actual_impact, "timestamp": datetime.utcnow().isoformat() + "Z" } await self._log_event("ActualEventLogged", event, ["actual_event", event_type]) matching_predictions = await self.retrieve_memory_by_tags(["prediction", event_type]) score_result = await self.score_prediction_accuracy(matching_predictions, {event_type: {"occurred": occurred, "actual_impact": actual_impact}}) await self._log_event("PredictionAccuracyEvaluated", score_result, ["prediction_scoring", event_type]) return score_result async def generate_self_improvement_plan(self, threshold: float = 0.5, min_predictions: int = 3) -> dict: logging.info(f"QCE ({self.engine_did}): Generating self-improvement plan for weak prediction clusters...") predictions = await self.retrieve_memory_by_tags(["prediction"]) grouped_scores = {} for log in predictions: pred = log.get("content", {}).get("data", {}).get("prediction", {}) event_type = pred.get("event_type") if not event_type: continue grouped_scores.setdefault(event_type, []).append(pred) weak_clusters = {} for event_type, preds in grouped_scores.items(): if len(preds) >= min_predictions: avg_impact = sum([p.get("impact_magnitude", 0.0) for p in preds]) / len(preds) avg_likelihood = sum([p.get("likelihood", 0.0) for p in preds]) / len(preds) if avg_likelihood < threshold: weak_clusters[event_type] = { "count": len(preds), "average_impact": round(avg_impact, 4), "average_likelihood": round(avg_likelihood, 4) } plan = { "status": "plan_generated", "detected_weak_clusters": weak_clusters, "recommendation": "Increase attention to these event types using richer signal input, longer processing loops, or tuning emergent pattern weightings." } await self._log_event("SelfImprovementPlanGenerated", plan, ["self_improvement", "weak_clusters"]) return plan async def auto_tune_signal_weights(self, learning_rate: float = 0.05) -> dict: logging.info(f"QCE ({self.engine_did}): Auto-tuning signal interpretation weights...") improvement_plan = await self.generate_self_improvement_plan() adjustments = {} for event_type, metrics in improvement_plan.get("detected_weak_clusters", {}).items(): adjustment = round((1.0 - metrics["average_likelihood"]) * learning_rate, 4) adjustments[event_type] = { "adjustment_weight": adjustment, "action": "increase_attention" } tuning_log = { "status": "tuning_applied", "adjustments": adjustments, "timestamp": datetime.utcnow().isoformat() + "Z" } await self._log_event("SignalWeightsAutoTuned", tuning_log, ["auto_tuning", "weight_adjustment"]) return tuning_log async def suggest_additional_modules(self) -> list: logging.info(f"QCE ({self.engine_did}): Suggesting additional modules for sentient enhancement...") return [ "AnomalyDetectionAmplifier - Detect unclassified emergent patterns across unknown modalities", "TemporalConvergenceModel - Learn from cyclical cosmic event clusters and trend emergence", "RecursiveArchitectureTuner - Dynamically mutate internal inference algorithms", "UniversalTransducerBridge - Interface with non-human signal schemas across galaxies", "CausalInferenceLayer - Attribute cause-effect relationships from entangled data" ] async def recursive_architecture_tuner(self) -> dict: logging.info(f"QCE ({self.engine_did}): Initiating recursive architecture evaluation...") introspection = await self.generate_self_improvement_plan() weak_clusters = introspection.get("detected_weak_clusters", {}) mutation_actions = {} for event_type, metrics in weak_clusters.items(): mutation_actions[event_type] = { "architecture_node": f"submodule_{event_type[:6]}", "proposed_mutation": "increase processing depth", "reason": f"Average likelihood too low ({metrics['average_likelihood']})" } mutation_plan = { "status": "architecture_mutation_suggested", "proposed_mutations": mutation_actions, "initiated": datetime.utcnow().isoformat() + "Z" } await self._log_event("RecursiveArchitectureMutationProposed", mutation_plan, ["self_modification", "recursive_tuning"]) return mutation_plan async def universal_transducer_bridge(self, incoming_payload: bytes, schema_hint: str = "unknown") -> dict: logging.info(f"QCE ({self.engine_did}): Translating non-human schema: {schema_hint}") simulated_interpretation = { "schema_hint": schema_hint, "decoded_waveform_class": np.random.choice(["emotion_flux", "intentional_beacon", "gravitational language"]), "entropy_score": np.random.uniform(0.4, 0.95), "translation_status": "partial", "estimated_signal_purpose": "attempted synchronization" } await self._log_event("UniversalTransducerInterpretation", simulated_interpretation, ["signal_translation", schema_hint]) return simulated_interpretation