ZARQA Acausal Resonance Core (ARC): A Time-Symmetric Retrocausal Machine Learning Framework and Zero-Trust Linux Execution Architecture (Phase 1)

This research monograph provides an exhaustive mathematical, physical, and systems-engineering evaluation of the ZARQA ARC Retrocausal Core (Phase 1 Final Release). Standard sequence-modeling architectures operate under strict causal directionality, constraining state inference to historical Markovian boundaries. By unifying Wheeler-Feynman Absorber Theory, Aharonov-Bergmann-Lebowitz Two-State Vector Formalism (TSVF), and Continuous Recurrent Convolutional Neural Networks (CRCNN), the ZARQA ARC framework achieves time-symmetric, non-Markovian inference across continuous temporal manifolds. Furthermore, we evaluate the production Linux deployment architecture—demonstrating asymptotic Lyapunov stability, zero-trust POSIX capability confinement, atomic blue-green virtual environment orchestration, and a 120-second autonomous rollback watchdog.