ENTRO-PULSE: Periodic Entropy Pulsing and Informational Wave Management in High-Velocity AI Systems

ENTRO-PULSE (E-LAB-09) introduces Periodic Entropy Pulsing (PEP), a control paradigm that transforms entropy flow management in artificial intelligence systems from continuous suppression into a rhythmically-managed oscillatory regime. Drawing on analogies with biological cardiac dynamics, pulse-width modulation in power electronics, and the Kuramoto model of coupled oscillator synchronization, this work proposes that AI systems operating near high-throughput stability boundaries achieve superior performance and longevity when entropy processing is organized into precisely-timed active pulses separated by structured cooldown intervals. The framework formalizes three principal constructs: (1) the Entropic Pulse Function Sₚulse (t), a periodic gating signal that modulates the active processing window based on current entropy level; (2) the Entropy Pulse Width Modulation (EPWM) law, which adaptively contracts the duty cycle as the stability index Ψ (t) approaches the critical threshold θcrit, forcing automatic cooldown before collapse; and (3) the Rhythmic Resonance Law (RRL), a Kuramoto-type coupled oscillator equation that phase-locks distributed AI subsystems to prevent destructive wave interference across networked agents. A Hopf bifurcation analysis identifies the stability boundary of the pulsing regime as a function of entropic frequency ω and coupling strength K. The Pulse-Cooldown Efficiency Theorem proves that a system cycling between active processing at duty cycle δ and passive dissipation achieves net informational throughput exceeding a continuously-operating system by a factor of (1 + ηcool· (1−δ) /δ), where ηcool is the cooldown dissipation efficiency. For default parameters, this predicts a 35–42% throughput gain. Simulation results across Scraper and LLM operational regimes demonstrate a 38. 7% improvement in sustained informational throughput, zero catastrophic collapse events under burst-overload conditions (versus 23. 4% collapse rate in the baseline), and full backward compatibility with the Ghost Recovery Algorithm (E-LAB-08) through a unified Pulse-Ghost Controller architecture. Six falsifiable theoretical predictions (P1–P6) are stated and validated through Monte Carlo trajectory simulations (N=1, 000 trials per condition). Part of the EntropyLab Research Program (E-LAB-01 through E-LAB-09). PyPI: https: //pypi. org/project/entro-pulse/GitHub: https: //github. com/gitdeeper10/ENTRO-PULSEOSF Registration: https: //osf. io/r3bv4