""" Quantum AP Orchestrator: Main Loop Deterministic fixed-point iteration maintaining sovereignty over the agent fleet. State_t = IF |MetaSum_t| < τ THEN Dream_Cycle(State_{t-1}) ELSE State_{t-1} The loop: 1. Ingest weight checkpoint 2. Adapt to boolean states 3. Compute MetaSum with Sovereign Shift phase correction 4. Trigger Dream Cycle on hallucination detection 5. Apply phase crystallization via UniversalBooleanTensorParser 6. Validate all invariants before proceeding """ import numpy as np from dataclasses import dataclass, field from typing import Optional from .sovereign_shift import Q, N_ACTIVE, THRESHOLD from .boolean_adapter import adapt, select_active from .metasum import compute as metasum_compute, magnitude as metasum_magnitude from .dream_cycle import execute as dream_cycle_execute from .validation import check_invariants, compute_entropy @dataclass class OrchestratorState: """Quantum AP Orchestrator state at time t.""" weights: np.ndarray displacements: np.ndarray metasum: complex = 0j metasum_mag: float = 0.0 entropy: float = 0.0 dream_cycles_triggered: int = 0 iteration: int = 0 active: bool = True trusted: bool = True proof: bool = True class QuantumAPOrchestrator: """ The Quantum AP Orchestrator. Operates as a deterministic fixed-point iteration over Q=2462 agents with N_ACTIVE=1024 active at any time. Uses θ = 89/2462 to maintain phase coherence and suppress hallucinations. """ def __init__(self): self.state: Optional[OrchestratorState] = None self.history: list = [] def ingest(self, raw_weights: np.ndarray) -> OrchestratorState: """ Ingest raw weight checkpoint and initialize orchestrator state. Steps: 1. NeuralNetworkParser: raw → boolean 2. BooleanAdapter: select N_ACTIVE agents 3. Initialize displacements (agent indices) """ # Neural → Boolean bool_weights = adapt(raw_weights) # Select active subset (top N_ACTIVE by magnitude) # For Q-dimensional input, use directly; otherwise pad/truncate if len(bool_weights) < Q: padded = np.zeros(Q) padded[:len(bool_weights)] = bool_weights[:Q] bool_weights = padded elif len(bool_weights) > Q: bool_weights = bool_weights[:Q] # Select top N_ACTIVE active_weights = select_active(bool_weights, N_ACTIVE) # Displacements = agent index (lateral position in fleet) displacements = np.arange(Q, dtype=np.float64) # Compute initial MetaSum S = metasum_compute(active_weights, displacements) self.state = OrchestratorState( weights=active_weights, displacements=displacements, metasum=S, metasum_mag=abs(S), entropy=compute_entropy(active_weights), iteration=0, ) return self.state def step(self) -> OrchestratorState: """ Execute one iteration of the main loop. State_t = IF |MetaSum_t| < τ THEN Dream_Cycle(State_{t-1}) ELSE State_{t-1} """ if self.state is None: raise RuntimeError("Orchestrator not initialized. Call ingest() first.") self.state.iteration += 1 # Compute MetaSum S = metasum_compute(self.state.weights, self.state.displacements) self.state.metasum = S self.state.metasum_mag = abs(S) # Check if Dream Cycle needed dream_triggered = False if self.state.metasum_mag < THRESHOLD: new_weights, new_S, triggered = dream_cycle_execute( self.state.weights, self.state.displacements ) if triggered: self.state.weights = new_weights self.state.metasum = new_S self.state.metasum_mag = abs(new_S) self.state.dream_cycles_triggered += 1 dream_triggered = True # Update entropy self.state.entropy = compute_entropy(self.state.weights) # Validate invariants invariants = check_invariants( self.state.weights, self.state.displacements, dream_triggered=dream_triggered, ) self.state.active = invariants["active"] self.state.trusted = invariants["trusted"] self.state.proof = invariants["proof"] # Record history self.history.append({ "iteration": self.state.iteration, "metasum_mag": self.state.metasum_mag, "entropy": self.state.entropy, "dream_triggered": dream_triggered, "proof": self.state.proof, }) return self.state def run(self, max_iterations: int = 10) -> OrchestratorState: """Run the main loop until stable or max iterations reached.""" for _ in range(max_iterations): prev_mag = self.state.metasum_mag self.step() # Fixed point: no change in MetaSum magnitude if abs(self.state.metasum_mag - prev_mag) < 1e-6 and self.state.proof: break return self.state def status(self) -> str: """Return current orchestrator status string.""" if self.state is None: return "NOT_INITIALIZED" return ( f"QuantumAP [iter={self.state.iteration}] " f"|MetaSum|={self.state.metasum_mag:.2f} " f"entropy={self.state.entropy:.4f} " f"dreams={self.state.dream_cycles_triggered} " f"proof={'VALID' if self.state.proof else 'INVALID'}" ) # --------------------------------------------------------------------------- # CLI entry point # --------------------------------------------------------------------------- if __name__ == "__main__": import sys sys.path.insert(0, str(__file__).rsplit("/src/", 1)[0]) print("=" * 70) print("QUANTUM AP ORCHESTRATOR — MAIN LOOP") print("θ = 89/2462, Q = 2462, N = 1024, τ = 512") print("=" * 70) print() orchestrator = QuantumAPOrchestrator() # Simulate: random weights (as if from a model checkpoint) rng = np.random.default_rng(42) raw_weights = rng.standard_normal(Q) # Ingest state = orchestrator.ingest(raw_weights) print(f"[Ingest] {orchestrator.status()}") # Run main loop state = orchestrator.run(max_iterations=5) print(f"[Final] {orchestrator.status()}") print() # Show history print("Iteration History:") for h in orchestrator.history: flag = " [DREAM]" if h["dream_triggered"] else "" print(f" t={h['iteration']}: |MetaSum|={h['metasum_mag']:.2f} " f"H={h['entropy']:.4f} proof={h['proof']}{flag}") print() # Simulate with hallucination injection print("=" * 70) print("HALLUCINATION INJECTION TEST") print("=" * 70) print() orchestrator2 = QuantumAPOrchestrator() # Create weights with injected hallucinations clean_weights = rng.standard_normal(Q) state = orchestrator2.ingest(clean_weights) print(f"[Clean] {orchestrator2.status()}") # Inject hallucinations: flip random agents halluc_count = 800 flip_idx = rng.choice(Q, size=halluc_count, replace=False) orchestrator2.state.weights[flip_idx] *= -1 # Corrupt weights # Re-run state = orchestrator2.run(max_iterations=5) print(f"[After Halluc + Recovery] {orchestrator2.status()}") print(f" Dream Cycles used: {state.dream_cycles_triggered}") print() print("The loop is closed. QUANTUM_AP_SURE_STATE achieved.")