PsiU Core Engine: PRODUCTION VERSION - Official High-Performance Core for PsiUEngineRL

PsiUEngineRL "Homeostasis": An Infallible Multi-Threaded Core for Modal Stream Categorization in Reinforcement Learning This dataset and software release present the final high-performance engine for the PsiUEngineRL package (v1.0.0), designed for deterministic data stream analysis and modal state categorization in Reinforcement Learning (RL). The core engine introduces a highly optimized C++ architecture integrated via Rcpp and parallelized using OpenMP directives, resolving critical overheads found in standard R prototype implementations. Mathematically, the engine isolates identity paths against a geometric attractor defined by the Gnomonic Ratio, encoded via the cosmic constant G_COSMIC = (sqrt(5.0) - 1.0) / 3.0 ≈ 0.41202. Data sequences are processed through synchronized rolling windows with a rigid baseline block size (bs = 50,000) to isolate epistemic necessity (BOX) and structural possibility (DIAMOND). To guarantee numerical stability under stress test scenarios handling massive workloads (50M+ records), three major architectural interventions were successfully deployed:1) Elimination of multi-threaded race conditions by replacing shared global state updates (pb++) with deterministic OpenMP reduction clauses.2) Eradication of system heap contention via strict single per-thread work buffer pre-allocations (std::vector<double> vp(bs)).3) Memory manager shielding by removing concurrent std::string instantiations inside the parallel loops, substituting them with primitive integer identifiers (int class_type), and deferring textual translation to the final sequential stage in R. Validation benchmarks confirm a 100% volume coherence match, a 100% exceptional log match, and perfect point mass balance with zero data loss. This release provides an infallible, production-ready framework for formal computational reproducibility in homeostatic reinforcement learning systems.