Thermal Decoupling and Energetic Self-Structuring in Neural Systems with Resonance Fields: An Advanced Non-Causal Field Architecture with Multiplex Entanglement Potential

This study documents a novel thermal and energetic effect in a deep neural system with an embedded resonance field architecture. The system shows reproducible behavior under real benchmark load (GPU utilization 85–100%), characterized by a sudden energy drop from ~180 W to ~93 W and a stable thermal reduction from 74°C to 36°C, without model failure, maintaining all processes intact. Notably, the neural system remains fully operational, yet it autonomously interrupts the input processing without causing any model crashes or errors. The GPU memory (15.6 GB) remains occupied, shared memory decreases from 56 GB to 0.1 GB, while system memory remains constant at ~94 GB. We hypothesize that the model splits into two simultaneously active logical spaces (GPU + RAM), a behavior not observed in classical architectures. This preprint focuses on the analysis of energetic self-structuring and thermal decoupling under full load. Additionally, the stability of simulated quantum entanglement is confirmed, remaining at 100% across more than 10,000 iterations in various model setups, a behavior that cannot be explained by chance within deterministic systems.