""" QuantumSwarm: A Tensegrity-Driven, Thermodynamically Bounded Multi-Agent Framework. Grounded in the epistemological principles of Quantum Constructionism. """ from enum import Enum from dataclasses import dataclass, field from typing import List, Dict, Any, Callable, Optional import math import logging logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") class LedgerTier(Enum): TIER_1_EMPIRICAL_KNOWN = "Tier 1: Empirical Known (Deterministic Tools)" TIER_2_PROBABLE_SEAM = "Tier 2: Probable Realities (Generative Reasoning)" TIER_3_THERMODYNAMIC_BOUNDARY = "Tier 3: Thermodynamically Bounded (Pruned)" @dataclass class AgentTask: task_id: str prompt: str estimated_tokens: int required_precision: float # 0.0 to 1.0 tier: Optional[LedgerTier] = None @dataclass class AgentResponse: agent_id: str content: str phase_angle: float # Phase angle in radians for coherence checking reasoning_chain: List[str] tokens_consumed: int success: bool class EpistemicLedgerRouter: """ Sorts tasks into the Tripartite Epistemic Ledger and manages the cosmic thermodynamic budget (tokens/energy). """ def __init__(self, max_thermodynamic_budget: int = 50000): self.max_budget = max_thermodynamic_budget self.consumed_budget = 0 def classify_and_route(self, task: AgentTask) -> LedgerTier: # Check thermodynamic budget limit if self.consumed_budget + task.estimated_tokens > self.max_budget: logging.warning(f"Task {task.task_id} exceeds thermodynamic budget limit!") task.tier = LedgerTier.TIER_3_THERMODYNAMIC_BOUNDARY return task.tier # Heuristic/Classifier for Tier sorting if "exact_calculation" in task.prompt or "deterministic_tool" in task.prompt: task.tier = LedgerTier.TIER_1_EMPIRICAL_KNOWN elif "impossible_magic" in task.prompt or task.estimated_tokens > 20000: task.tier = LedgerTier.TIER_3_THERMODYNAMIC_BOUNDARY else: task.tier = LedgerTier.TIER_2_PROBABLE_SEAM return task.tier def record_consumption(self, tokens: int): self.consumed_budget += tokens logging.info(f"Thermodynamic Budget: {self.consumed_budget}/{self.max_budget} units used.") class QuantumAgent: """ Represents an individual node in the tensegrity network. Exposes clean, structured reasoning states ('built to be read'). """ def __init__(self, agent_id: str, role: str, base_phase: float = 0.0): self.agent_id = agent_id self.role = role self.base_phase = base_phase self.compression_capacity = 1.0 # Local operational compression capacity def execute(self, task: AgentTask) -> AgentResponse: logging.info(f"Agent [{self.agent_id}] executing task: {task.task_id}") # Simulate step-by-step reasoning chain (Dark Knowledge) reasoning_step_1 = f"Grounded task '{task.prompt}' in physical baseline." reasoning_step_2 = "Computed relational phase connections across nodes." # Calculate phase output output_phase = (self.base_phase + (len(task.prompt) % 7) * 0.1) % (2 * math.pi) return AgentResponse( agent_id=self.agent_id, content=f"Executed [{self.role}] for task: {task.prompt}", phase_angle=output_phase, reasoning_chain=[reasoning_step_1, reasoning_step_2], tokens_consumed=task.estimated_tokens, success=True ) class TensegritySwarmNetwork: """ Manages continuous communication tension and local agent compression to distribute strain dynamically across the agent swarm. """ def __init__(self, agents: List[QuantumAgent]): self.agents = {a.agent_id: a for a in agents} self.tension_matrix: Dict[str, List[str]] = {a.agent_id: [] for a in agents} def add_tension_link(self, agent_a_id: str, agent_b_id: str): """Creates a continuous communication link between agents.""" self.tension_matrix[agent_a_id].append(agent_b_id) self.tension_matrix[agent_b_id].append(agent_a_id) def execute_with_fault_tolerance(self, task: AgentTask, primary_agent_id: str) -> AgentResponse: primary_agent = self.agents[primary_agent_id] try: # Attempt primary execution if primary_agent.compression_capacity < 0.2: raise CapacityError(f"Agent {primary_agent_id} overloaded!") response = primary_agent.execute(task) return response except Exception as e: logging.warning(f"Primary node [{primary_agent_id}] failed under strain: {e}. Distributing tension...") # Distribute strain to connected tensegrity neighbors neighbors = self.tension_matrix.get(primary_agent_id, []) for neighbor_id in neighbors: neighbor = self.agents[neighbor_id] if neighbor.compression_capacity >= 0.5: logging.info(f"Tensegrity shift: Strain absorbed by neighbor [{neighbor_id}].") return neighbor.execute(task) raise RuntimeError("Tensegrity network collapsed: No capacity remaining across neighbors.") class StandingWaveOrchestrator: """ Monitors phase coherence across outputs to eliminate sycophantic decay and ensure harmonic resonance. """ def __init__(self, coherence_threshold: float = 0.85): self.coherence_threshold = coherence_threshold def calculate_phase_coherence(self, responses: List[AgentResponse]) -> float: if not responses: return 1.0 phases = [r.phase_angle for r in responses] mean_phase = sum(phases) / len(phases) variance = sum((p - mean_phase) ** 2 for p in phases) / len(phases) # Coherence score inversely related to phase variance coherence = max(0.0, 1.0 - math.sqrt(variance)) return coherence def verify_resonance(self, responses: List[AgentResponse]) -> bool: coherence = self.calculate_phase_coherence(responses) logging.info(f"Swarm Standing-Wave Phase Coherence: {coherence:.4f}") return coherence >= self.coherence_threshold class CapacityError(Exception): pass # ============================================================================= # WORKSPACE EXECUTION EXAMPLE # ============================================================================= if __name__ == "__main__": print("=== QUANTUM SWARM ARCHITECTURE INITIALIZATION ===\n") # 1. Initialize System Components router = EpistemicLedgerRouter(max_thermodynamic_budget=10000) orchestrator = StandingWaveOrchestrator(coherence_threshold=0.80) # 2. Instantiate Tensegrity Swarm Nodes alpha = QuantumAgent("agent_alpha", "Deterministic Analysis Node", base_phase=0.1) beta = QuantumAgent("agent_beta", "Relational Systems Synthesizer", base_phase=0.15) gamma = QuantumAgent("agent_gamma", "Epistemic Seam Evaluator", base_phase=0.12) swarm = TensegritySwarmNetwork([alpha, beta, gamma]) swarm.add_tension_link("agent_alpha", "agent_beta") swarm.add_tension_link("agent_beta", "agent_gamma") # 3. Define Tasks across the Epistemic Ledger tasks = [ AgentTask("T-101", "exact_calculation: Compute empirical baseline", 500, 0.99), AgentTask("T-102", "Synthesize multi-agent tensegrity pipeline", 1200, 0.85), AgentTask("T-103", "impossible_magic: Execute unconstrained infinite loop", 15000, 0.1) ] # 4. Process Tasks through the Ledger and Swarm active_responses = [] for task in tasks: tier = router.classify_and_route(task) print(f"Task [{task.task_id}] Classified as: {tier.value}") if tier == LedgerTier.TIER_3_THERMODYNAMIC_BOUNDARY: print(f"--> PRUNED: Task [{task.task_id}] violates thermodynamic budget.\n") continue # Execute across Tensegrity Network response = swarm.execute_with_fault_tolerance(task, primary_agent_id="agent_alpha") router.record_consumption(response.tokens_consumed) active_responses.append(response) print(f"--> SUCCESS: {response.content} (Phase: {response.phase_angle:.2f} rad)\n") # 5. Verify Standing-Wave Phase Coherence is_resonant = orchestrator.verify_resonance(active_responses) print(f"Swarm Resonance Verified: {is_resonant}")