Please note: A rendering error in the initial export caused track desynchronization. This has been corrected in the updated version now available. This updated preprint now includes two extended appendices documenting long-term measurements, comparative power data, and realtime telemetry visualizations. Together, they provide empirical support for the original study's findings and expand on the energetic behavior of the T-Zero resonance field. The core of the approach is a resonance-based field architecture — referred to as the T-Zero field — that enables thermal decoupling and energetic self-structuring in deep neural networks operating under real-world benchmark conditions. This field 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%. Appendix A presents structured energy data over multiple runtime scenarios and measurement systems.Appendix B introduces realtime video documentation, showing synchronized GPU telemetry and task manager data across multiple T-Zero configurations. All values are validated through internal and external monitoring tools. The extended dataset confirms that the observed energy savings and structural coherence are not transient, but stable and reproducible under sustained system load. This release consolidates the original preprint and both appendices under a single version DOI to ensure continuity and traceability. 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. 🔧 Tools Used for Video Editing and Voiceover The video segments in Appendix B were edited using Microsoft Clipchamp.Synthetic voice narration was also generated within Clipchamp using standard AI voice features. Trauth Research® is a registered trademark at the German Patent and Trademark Office (DPMA).