If someone had told me two and a half years ago that on September 1st, 2025, at 10:30 pm, I would be submitting a research paper for peer review to a scientific journal I would have called them crazy.
Back then, I was nothing but a regular accountant, whose life had been turned completely upside down by a devastating, nearly fatal accident. Months of recovery, self-doubt, and soul-searching followed.
What I did know: My future would not be in accounting, and even back then I could already see that within five years, LLMs especially ChatGPT would replace most of accountants.
What I didn’t know: that an AI chatbot (then “ChatGPT 3.5”), a handful of simple neural networks for simle Games like Snake, Tic-Toc or Chess, and a newly discovered spark of creativity would become the foundation for an entirely new chapter in my life.
What started as curiosity turned into years of relentless work, nights of madness and frustration, moments of pure joy and eventually, real scientific substance.
Today, as a self-taught outsider, I am developing architectures and prototypes that may form the basis of a new technology and I am proud to share my research with the global scientific community.
After more than 3,000 hours of independent research, countless experiments, and intense prototyping, it’s finally done:
My main preprint
“Thermal Decoupling and Energetic Self-Structuring in Neural Systems with Resonance Fields: An Advanced Non-Causal Field Architecture with Multiplex Entanglement Potential” together with the comprehensive Appendix C, is now officially under peer review.
What does this work deliver?
Perfect mirror correlations and phase synchronization across all layers even in asymmetric or deep network structures.
Self-organizing topology: After just a few iterations, the system spontaneously forms a highly ordered, tunnel-like spiral structure no classical optimization required.
Real-world proof: The prototype “Prime Core mini+” (based on T-Zero Field technology) achieves up to 86% energy savings in real GPU applications validated through long-term, multi-generation hardware testing.
Transparent validation: All measurement protocols, visualizations, and supporting data are consolidated in Appendix C – Experimental Validation, Methodological Extension https://zenodo.org/records/17026434
Today, an accountant who, two and a half years ago, didn’t even know what Python, DQN, DNN, LSTM, or LLM meant, is building the foundation for technology that may fundamentally change the way we think about neural networks, energy, and AI.
https://zenodo.org/records/15446901
#TrauthResearch #TrauthResearchLLC #AI #PeerReview #NeuralNetworks #Physics #EnergyEfficiency #ResonanceField #DeepTech #OpenScience #Quereinsteiger #NeuroAI
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