""" Zero-Waste Residual Cognitive Action Engine (ZW-RCAE) ==================================================== Implements the exact Mathematical Utility Formulation: U_i(E) = \Delta I_i^{(v)}(E) + \sum_{j \in E} S_{ij}^{+} - \lambda_R R_i(E) - \lambda_C C_i - \lambda_L L_i - \lambda_K K_i - \lambda_F F_i Execution Condition: Execute step i if and only if: U_i(E) > \tau Zero-Waste Artifact Guarantee: Every executed output is projected into an immutable holographic micro-residual: A_i = < \Phi_h, \Gamma_p, \Omega_v > - \Phi_h: Quantum-Hash Fingerprint - \Gamma_p: Causal Directed Acyclic Provenance - \Omega_v: Epistemic Validity Envelope Dead-Work Quantum Annihilation: Eliminates redundant work, marginal gain <= 0, or zero downstream consumers prior to compute. """ from __future__ import annotations import hashlib import time import json from typing import Any, Dict, List, Set, Optional class HolographicResidualArtifact: def __init__( self, artifact_id: str, data: Any, ancestors: List[str], validity_envelope: Dict[str, Any] ): self.artifact_id = artifact_id self.data = data self.timestamp = time.time() self.ancestors = ancestors # \Gamma_p: Causal Directed Acyclic Provenance self.validity_envelope = validity_envelope # \Omega_v: Epistemic Validity Envelope self.downstream_consumers: Set[str] = set() # \Phi_h: Quantum-Hash Fingerprint raw_repr = f"{artifact_id}:{ancestors}:{json.dumps(validity_envelope, sort_keys=True)}" self.fingerprint = hashlib.sha256(raw_repr.encode('utf-8')).hexdigest() def register_consumer(self, consumer_id: str): self.downstream_consumers.add(consumer_id) def is_valid(self, current_context: Dict[str, Any]) -> bool: """Check if current context falls inside \Omega_v.""" for k, v in self.validity_envelope.items(): if current_context.get(k) != v: return False return True class ZeroWasteCognitiveActionEngine: def __init__( self, tau: float = 0.25, lambda_R: float = 0.15, lambda_C: float = 0.10, lambda_L: float = 0.05, lambda_K: float = 0.05, lambda_F: float = 0.08 ): self.tau = tau self.lambda_R = lambda_R self.lambda_C = lambda_C self.lambda_L = lambda_L self.lambda_K = lambda_K self.lambda_F = lambda_F # Invariant Artifact Substrate (Storage for reusable residuals) self.artifact_store: Dict[str, HolographicResidualArtifact] = {} self.fingerprint_index: Dict[str, str] = {} self.execution_history: List[Dict[str, Any]] = [] def compute_utility( self, delta_I: float, synergy_sum: float, redundancy_R: float, compute_cost_C: float, latency_L: float, epistemic_complexity_K: float, friction_F: float ) -> float: """ Calculates: U_i(E) = \Delta I_i^{(v)}(E) + \sum_{j \in E} S_{ij}^{+} - \lambda_R R_i(E) - \lambda_C C_i - \lambda_L L_i - \lambda_K K_i - \lambda_F F_i """ penalty = ( self.lambda_R * redundancy_R + self.lambda_C * compute_cost_C + self.lambda_L * latency_L + self.lambda_K * epistemic_complexity_K + self.lambda_F * friction_F ) return (delta_I + synergy_sum) - penalty def should_execute( self, task_id: str, ancestors: List[str], validity_envelope: Dict[str, Any], delta_I: float, synergy_sum: float, redundancy_R: float, compute_cost_C: float, latency_L: float, epistemic_complexity_K: float, friction_F: float, expected_downstream_consumers: int = 1 ) -> tuple[bool, float, Optional[str]]: """ Evaluates dead-work condition & execution threshold. Returns: (allow_execution, utility_value, reason) """ # 1. Dead-work check: If no downstream consumer, instant annihilation if expected_downstream_consumers <= 0: return False, 0.0, "ANNIHILATED: Zero downstream consumers (deg_out = 0)" # 2. Check fingerprint collision (Exact reusable artifact already exists) raw_repr = f"{task_id}:{ancestors}:{json.dumps(validity_envelope, sort_keys=True)}" proposed_fp = hashlib.sha256(raw_repr.encode('utf-8')).hexdigest() if proposed_fp in self.fingerprint_index: cached_id = self.fingerprint_index[proposed_fp] cached_art = self.artifact_store[cached_id] if cached_art.is_valid(validity_envelope): return False, 0.0, f"CACHED_REUSE: Reusable artifact {cached_id} matches \Phi_h" # 3. Calculate exact Utility U_i(E) U_i = self.compute_utility( delta_I=delta_I, synergy_sum=synergy_sum, redundancy_R=redundancy_R, compute_cost_C=compute_cost_C, latency_L=latency_L, epistemic_complexity_K=epistemic_complexity_K, friction_F=friction_F ) # 4. Gating threshold: Execute only when U_i(E) > \tau if U_i <= self.tau: return False, U_i, f"ANNIHILATED: Marginal gain U_i({U_i:.4f}) <= tau({self.tau})" return True, U_i, "EXECUTED: Utility strictly exceeds tau" def register_execution_output( self, task_id: str, result_data: Any, ancestors: List[str], validity_envelope: Dict[str, Any] ) -> HolographicResidualArtifact: """Projects output into immutable holographic residual artifact.""" artifact = HolographicResidualArtifact( artifact_id=task_id, data=result_data, ancestors=ancestors, validity_envelope=validity_envelope ) self.artifact_store[task_id] = artifact self.fingerprint_index[artifact.fingerprint] = task_id return artifact if __name__ == "__main__": print("=" * 80) print("TESTING ZERO-WASTE RESIDUAL COGNITIVE ACTION ENGINE (ZW-RCAE)") print("=" * 80) engine = ZeroWasteCognitiveActionEngine(tau=0.20) # Test 1: High utility action (Should execute) exec_1, u_1, reason_1 = engine.should_execute( task_id="opt_step_01", ancestors=["root"], validity_envelope={"cuda_arch": "sm_86", "dtype": "int4"}, delta_I=0.85, synergy_sum=0.15, redundancy_R=0.0, compute_cost_C=0.1, latency_L=0.05, epistemic_complexity_K=0.1, friction_F=0.05, expected_downstream_consumers=2 ) print(f"Task 1 -> Execute: {exec_1} | Utility: {u_1:.4f} | Rationale: {reason_1}") if exec_1: engine.register_execution_output( task_id="opt_step_01", result_data={"weights_nibble": "0x5A"}, ancestors=["root"], validity_envelope={"cuda_arch": "sm_86", "dtype": "int4"} ) # Test 2: Dead-work / Redundant action (Should be annihilated) exec_2, u_2, reason_2 = engine.should_execute( task_id="opt_step_01", ancestors=["root"], validity_envelope={"cuda_arch": "sm_86", "dtype": "int4"}, delta_I=0.85, synergy_sum=0.15, redundancy_R=0.9, compute_cost_C=0.8, latency_L=0.5, epistemic_complexity_K=0.5, friction_F=0.4, expected_downstream_consumers=1 ) print(f"Task 2 (Duplicate) -> Execute: {exec_2} | Utility: {u_2:.4f} | Rationale: {reason_2}") # Test 3: Zero downstream consumers (Should be annihilated instantly) exec_3, u_3, reason_3 = engine.should_execute( task_id="dangling_eval", ancestors=["opt_step_01"], validity_envelope={"cuda_arch": "sm_86"}, delta_I=0.9, synergy_sum=0.5, redundancy_R=0.0, compute_cost_C=0.1, latency_L=0.01, epistemic_complexity_K=0.01, friction_F=0.01, expected_downstream_consumers=0 ) print(f"Task 3 (Zero Consumers) -> Execute: {exec_3} | Utility: {u_3:.4f} | Rationale: {reason_3}") print("\n[+] Zero-Waste Gating Invariant Engine Verified Successfully!")