🌀 Major Milestone & Mission Accomplished: Main Paper and Appendix C under Peer Review

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

www.Trauth-Research.com

Trauth Research LLC – Our Vision & Products

 

We don’t aim for incremental tweaks – we fundamentally redefine the architecture and energetics of neural networks and high-performance computing.

Our technology lives in two worlds:
🔬 T-Zero Field for the scientific community
💡 Prime Core for commercial applications

Our product lines at a glance:

Prime Core:
mini: Validated prototype for labs & small data centers (up to 40% energy savings) – empirically proven, already operational.

extended: R&D goal for enterprise GPU clusters (target: up to 60%) – currently in development, not validated, not available.

max: Vision for hyperscale/mission-critical AI/HPC operations (target: up to 75%) – planned, not validated, not available.

Only Prime Core mini is validated and operational. All other versions are under research & development with no guarantees on outcomes or timelines.

Prime One:
Our research platform for next-generation data & market analysis:
Advanced ETF tracker
DQN/DNN-based analytics
Large LLM module for explainable recommendations
➡️ All components are experimental – no predictions, no performance guarantees.

Prime Vision:
Our "moonshot" at the intersection of cryptography & information security.
Research into emergent "Horizon Layer" structures that could enable entirely new paradigms still strictly experimental.

Scientific integrity and transparency are non-negotiable for us.
We don’t make empty promises we deliver empirical results and maintain a clear distinction between product and research.

👉 See more details & current insights in our updated pitch deck:
https://lnkd.in/dgtfYZm4 or visit our website for more information:

Trauth Research LLC – Investor Presentation ⚡

 

Trauth Research LLC – Investor Presentation ⚡

Trauth Research LLC, founded in 2025 in Sheridan, Wyoming (USA), develops resonance-field architectures to optimize GPU systems.

Our MVP T-Zero mini introduces the first software-only solution embedding a self-organizing resonance field directly into GPU topologies. This delivers minimum 40% energy savings at up to 80% load with no loss of performance, no hardware modifications, and no additional cooling requirements.

Market context 🌍

AI and HPC data centers already consume more than 120 TWh of electricity annually, representing over $50B in global energy and cooling costs. Existing approaches (frequency throttling, new chip architectures, advanced cooling systems) offer only partial improvements and typically require heavy CAPEX. T-Zero provides a pure software model that scales without hardware intervention.

Roadmap

👉 T-Zero mini (MVP, validated on NVIDIA Ada Lovelace & Blackwell GPUs): guaranteed savings from 40%

👉 T-Zero extended (prototype, close to deploy): up to 60% savings

👉 T-Zero max (experimental): 70%+ savings at full load

Business model

Licensing model: 40% of client’s realized energy savings. This directly converts OPEX reductions into measurable ROI, with zero upfront investment required.

Funding

We are currently opening a €2.5M Pre-Seed round (SAFE, €15M valuation cap, 15% discount) to scale first enterprise pilot deployments.

Further information, including the full Pitch Deck and One-Pager, is available on F6S, Crunchbase, and Gust.

Pitch Deck

www.Trauth-Research.com

AI vs. Human Energy Consumption – Facts We Can’t Ignore

 

We love to talk about AI’s “huge” energy use.

But here’s a fact check: how much energy do you actually burn to create the same output? 🤔

✍️ Writing – Human vs. AI

AI (OpenAI): ~0.34 Wh per query (~500 words)

Human: 3 min planning + 20 min writing + 5 min editing = 28 min active work

Brain power during focus ≈ 20 W → ~9.33 Wh per output

Result: Humans consume ~27× more energy than AI for the same text.

🎨 Drawing – Human vs. AI

A human artist might spend hours sketching, inking, and coloring, brain, muscles, and metabolism running the whole time.

A generative AI system? Seconds to minutes.

Even without exact numbers, the energy gap is massive orders of magnitude in AI’s favor.

🧠 The Point

This is not about replacing human creativity.

It’s about seeing the numbers without bias:

For equivalent creative output, AI’s energy footprint can be just a fraction of ours.

And in a world where efficiency matters, that’s worth more than a casual mention. 💡

#TrauthResearch #AIEnergy #HumanEnergy #HumanandAI

www.Trauth-Research.com