[2512.24420] Virasoro Symmetry in Neural Network Field Theories

High Energy Physics - Theory arXiv:2512.24420 (hep-th) [Submitted on 30 Dec 2025 (v1), last revised 2 Apr 2026 (this version, v3)] Title:Virasoro Symmetry in Neural Network Field Theories Authors:Brandon Robinson View a PDF of the paper titled Virasoro Symmetry in Neural Network Field Theories, by Brandon Robinson View PDF HTML (experimental) Abstract:Neural Network Field Theories (NN-FTs) typically describe Generalized Free Fields that lack a local stress-energy tensor in two dimensions, obstructing the realization of Virasoro symmetry. We present the ``Log-Kernel'' (LK) architecture, which enforces local conformal symmetry via a specific rotation-invariant spectral prior $p(k) \propto |k|^{-2}$. We analytically derive the emergence of the Virasoro algebra from the statistics of the neural ensemble. We validate this construction through numerical simulation, computing the central charge $c_{exp} = 0.9958 \pm 0.0196$ (theoretical $c=1$) and confirming the scaling dimensions of vertex operators. Furthermore, we demonstrate that finite-width corrections generate interactions scaling as $1/N$. Finally, we extend the framework to include fermions and boundary conditions, realizing the super-Virasoro algebra. We verify the $\mathcal{N}=1$ super-Virasoro algebra by measuring the supercurrent correlator to $96\%$ accuracy. We further demonstrate conformal boundary conditions on the upper half-plane, achieving 99\% agreement for boundary fermion and boson propagators. Comments: Su...