AI systems (Scraper and LLM operational regimes)
Periodic Entropy Pulsing (PEP) framework
Baseline continuous operation
Sustained informational throughput and catastrophic collapse events
The Periodic Entropy Pulsing framework significantly improves informational throughput and prevents catastrophic collapse 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
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Samir Baladi
Ronin Institute
Renaissance Services (United States)
Renaissance University
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Samir Baladi (Mon,) studied this question.
www.synapsesocial.com/papers/69e07dfe2f7e8953b7cbeffd — DOI: https://doi.org/10.5281/zenodo.19547862