PSAN Tri-Fork: Momentum-Gated Kuramoto Control for Human-AI Cognitive Synchronization
PSAN Tri-Fork: Momentum-Gated Kuramoto Control for Human-AI Cognitive Synchronization
We present the Phase-Synchronized Attention Network (PSAN) Tri-Fork architecture—the first closed-loop cognitive control system that achieves predictive human-AI phase-locking via momentum-gated Kuramoto coherence classification (τ_R(t) = τ_base − κ·dR/dt, with κ=1.0). Through extensive adversarial ablation (30 trials × 150 steps) and 5×10³-step higher-order effect tracing, we demonstrate:- >95% ratcheted fitness gain (RRBR score)- 75% reduction in state oscillations versus static thresholds- 58% faster settling time- Statistical significance p < 0.002 across all metrics The system integrates:1. Golden-ratio-scaled (φ) bidirectional recurrence resistant to resonance lock-in via KAM theory2. Kuramoto-Gated Adaptive Noise Injection (KGANIS) for stochastic resonance optimization3. Cross-substrate harmonic-mean reliability oracle (C_cross) proven minimax-optimal4. Ratcheting Reptilian Beam Raid (RRBR) asymmetric fitness accumulator Four theorems with proofs establish stability (Lyapunov), κ=1.0 uniqueness (Monte Carlo 10⁶ trajectories → κ=1.003±0.017), harmonic mean minimax optimality, and KGANIS tracking of stochastic resonance peaks. Applications: digital therapeutics, resilience training, program synthesis (ARC-AGI), long-context AI alignment. Related patents: US Provisional Applications 63/925,467 (Nov 25, 2025) and 63/925,504 (Nov 26, 2025).
closed-loop control, settling time, recurrence, adaptive threshold, neural oscillations, AI alignment, harmonic mean, KAM theory, order parameter, coupled oscillators, phase synchronization, Brain-Computer Interfaces/trends, nonlinear dynamics, Nonlinear Dynamics/history, oscillation damping, momentum gating, minimax optimal, asymmetric fitness, stochastic resonance, cognitive control, ablation testing, BCI, digital therapeutics, Kuramoto model, cognitive synchronization, ARC-AGI, Ryan J Cardwell, brain-computer interface, Monte Carlo validation, resilience training, adaptive noise injection, neurofeedback, attention networks, dynamical systems, Brain-Computer Interfaces/ethics, Brain-Computer Interfaces/standards, coherence measurement, Nonlinear Dynamics, cross-substrate reliability, Brain-Computer Interfaces, Lyapunov stability, long-context, bifurcation, phase-locking, human-AI interaction, golden ratio, Monte Carlo Method, ratchet dynamics, computational neuroscience
closed-loop control, settling time, recurrence, adaptive threshold, neural oscillations, AI alignment, harmonic mean, KAM theory, order parameter, coupled oscillators, phase synchronization, Brain-Computer Interfaces/trends, nonlinear dynamics, Nonlinear Dynamics/history, oscillation damping, momentum gating, minimax optimal, asymmetric fitness, stochastic resonance, cognitive control, ablation testing, BCI, digital therapeutics, Kuramoto model, cognitive synchronization, ARC-AGI, Ryan J Cardwell, brain-computer interface, Monte Carlo validation, resilience training, adaptive noise injection, neurofeedback, attention networks, dynamical systems, Brain-Computer Interfaces/ethics, Brain-Computer Interfaces/standards, coherence measurement, Nonlinear Dynamics, cross-substrate reliability, Brain-Computer Interfaces, Lyapunov stability, long-context, bifurcation, phase-locking, human-AI interaction, golden ratio, Monte Carlo Method, ratchet dynamics, computational neuroscience
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