NKAT-Ultimate-Unification

We present the first deep-learning verification of the Non-Commutative Kolmogorov–Arnold Theory (NKAT), a candidate for ultimate unification of quantum fields, gravity and information.Using a GPU-accelerated spectral-triple simulator with kernel-adapting neural networks, we drive the spectral-dimension error on 64³ lattices down to 4.34×10−54.34\times10^{-5}4.34×10−5.We show that (i) Moyal and κ\kappaκ-Minkowski star products satisfy Jacobi consistency and yield indistinguishable physics, (ii) the θ-running remains finite and smooth over 20 orders of energy, and (iii) the Connes distance reproduces an almost flat emergent metric.Compactifying eleven-dimensional M-theory on a seven-dimensional Calabi–Yau manifold reproduces the NKAT parameter set within <0.1%<0.1\%<0.1%.The resulting predictions—γ-ray time-delay ≤ 1.6×10−191.6\times10^{-19}1.6×10−19 s (CTA), vacuum birefringence ≤ 10−1310^{-13}10−13 rad km−1^{-1}−1 (PVLAS-II) and atomic-interferometer phase shift Δϕ∼10−7Δ\phi\sim10^{-7}Δϕ∼10−7 rad (MAGIS-100)—are testable with current or near-future experiments.All code, data and trained checkpoints (47 MB) are released under CC-BY-4.0.Our results demonstrate that modern machine-learning pipelines can rigorously validate non-perturbative quantum-gravity candidates and deliver concrete experimental targets.