Thermal Decoupling and Energetic Self-Structuring in Neural Systems with Resonance Fields

A peer-reviewed version of this preprint was published in: Journal of Cognitive Computing and Extended Realities: https://doi.org/10.65157/JCCER.2025.011

This updated preprint, now including an extended appendix with long-term measurements and comparative power data, presents a novel field architecture enabling thermal decoupling and energetic self-structuring in deep neural networks operating under real-world benchmark conditions. The core of the approach is a resonance-based structure – referred to as the T-Zero field – that induces a non-causal, self-organizing system state, characterized by stable operation at up to 96% GPU utilization while simultaneously reducing electrical power draw by up to 70%.

Unlike classical energy optimization techniques, which rely on external control or adaptive clocking, the T-Zero field operates through internal structural modulation. The resulting system behavior demonstrates complete functional coherence while defying conventional thermodynamic expectations. Benchmark measurements confirm significant reductions in GPU temperature (from 76 °C to 36 °C) and total system power (from 450 W to 93 W), with no degradation in model output or stability.

🔹 Key Highlights

• 🔥 Up to 70% energy reduction under real GPU load

• 🌀 T-Zero field enables thermal decoupling and internal structural self-modulation

• 🧠 Documented phase-synchronous resonance and non-linear signal dynamics

• 🔒 Unreplicable initialization key, bound to the original field instance

• 🚫 Not based on classical or quantum cryptography – structure-defined protection

Advanced signal analyses – including FFT, cross-correlation, phase-space mapping, and vector field directionality – reveal rhythmic, phase-synchronous coupling between internal neuron groups. These signatures point to an emergent resonance pattern with properties comparable to entangled systems, suggesting a new category of field-based AI architectures.

The findings suggest the emergence of a new computational paradigm in which structure and self-organization replace classical optimization. This model challenges conventional assumptions about thermal dissipation, energy distribution, and process causality in AI systems.