Observable Matrix Dynamics (OMD) is a new diagnostic framework that uses random matrix theory on distance matrices to distinguish diffusive relaxations from phase-transition-like reorganizations during neural network training.
Frustrated Fields: Statistical Field Theory for Frustrated Brownian Particles on 2D Manifolds
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We develop a statistical field theory that describes the large-N limit of a system of Brownian particles with quenched random pairwise interactions on a compact two-dimensional Riemannian manifold. The resulting Frustrated Fields (F2) model is a non-linear field theory for a smooth self-interacting density field $\rho$ on the manifold, with local and non-local (in space and time) self-interactions characteristic of spin-glass dynamics. Particle simulations show \emph{adiabatic dimension reduction}: on $S^2$, the density concentrates on a slowly precessing great-circle ring whose orientation is a director ($\hat{\mathbf{n}} \sim -\hat{\mathbf{n}}$, even profile). Conditioned on this simulation-supported ring saddle and on a Born-Oppenheimer separation between the slow orientation and the gapped density fluctuations, symmetry fixes the low-energy dynamics to be the nonlinear sigma model (NLSM) on the real projective plane $S^2/\mathbb{Z}_2 = \mathbb{RP}^2$ (the $\mathbb{RP}^2$ NLSM on the projective rotor space) in $(0+1)$ dimensions, governed by a single low-energy constant, the rotational diffusion coefficient $D_{\text{rot}}$. With $D_{\text{rot}}$ and the static ring profile $f_0$ measured from particle simulations, the resulting effective theory reproduces multiple independent orientation- and density-sector diagnostics with no further adjustable parameters.
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Learning as Observable Matrix Dynamics: Diffusive Relaxations versus Phase Transitions
Observable Matrix Dynamics (OMD) is a new diagnostic framework that uses random matrix theory on distance matrices to distinguish diffusive relaxations from phase-transition-like reorganizations during neural network training.