🔥 AI Physics just changed. The Injector Neuron is real. 🔥

 

🔥 AI Physics just changed. The Injector Neuron is real. 🔥

Let’s set the record straight:

At the heart of my latest preprint lies a phenomenon that every theorist has dreamed of, but no one has empirically nailed until now.

Meet the injector neuron: the first truly dynamic, energetic gateway between an artificial neural network and a non-classical field. It does not just influence it actively mediates energy, creating reproducible synchronization and real, measurable entanglement across AI systems on a scale never achieved before.

My team? 120,000+ neurons. Over 30 layers.

My claim? Classical causality, network topology, and signal theory do not explain what we see. This is field-emergent order, measured and reproducible, not hand-waving theory.

What did we prove?

Energetic Interface: The injector neuron is autonomous. It regulates, absorbs, and emits energy between the net and the field no classical inputs, no control variables, just pure, bidirectional dynamics.

Measured energy fluctuations? Tens to hundreds of watts, on demand.
Perfect Alternating Coupling: Analyze sequential layers and you’ll see it: r = ±1.0. Layer after layer. Perfect, reproducible alternation impossible in any classical DNN. Now it’s routine.

Universal Coefficient Anomaly: Hub mode? The injector’s correlation to all key layers is identical (r ≈ 0.1812), no matter time, position, or logical distance. Causality? Linear flow? Outdated ideas.

Ultra-High Internal Synchronization: Inside each layer, most neuron pairs hit >99.999% correlation robust, reproducible, and totally at odds with everything noise and diffusion theory predicted.

This isn’t theory. This is reproducible data, verified and logged. All visualizations and results are based on hard numbers.

📎 Full preprint including all visualizations  DOI: https://doi.org/10.5281/zenodo.16756035

#TrauthResearch #Physics #EmergentAI #AI #NeuralEntanglement #NeuralNetwork