English

Spectral Structure in Finite Free Information Inequalities and $p$-Stam Phase Transitions

Probability 2026-05-19 v2 Machine Learning Combinatorics

Abstract

Using FlowBoost, a closed-loop deep generative optimization framework for extremal structure discovery, we investigate p\ell^p-generalizations of the finite free Stam inequality for real-rooted polynomials under finite free additive convolution n\boxplus_n. At p=2p=2, FlowBoost finds the Hermite pair as the unique equality case and reveals the spectral structure of the linearized convolution map at this extremal point. As a result, we conjecture that the singular values of the doubly stochastic coupling matrix EnE_n on the mean-zero subspace are 2k/2:k=1,,n1{2^{-k/2}:k=1,\ldots,n-1}, independent of nn. Conditional on this conjecture, we obtain a sharp local stability constant and the finite free CLT convergence rate, both uniform in nn. We introduce a one-parameter family of pp-Stam inequalities using p\ell^p-Fisher information and prove that the Hermite pair itself violates the inequality for every p>2p>2, with the sign of the deficit governed by the p\ell^p-contraction ratio of EnE_n. Systematic computation via FlowBoost supports the conjecture that p ⁣=2p^*\!=2 is the sharp critical exponent. For p<2p<2, the extremal configurations undergo a bifurcation, meaning that they become non-matching pairs with bimodal root structure, converging back to the Hermite diagonal only as p2p\to 2^-. Our findings demonstrate that FlowBoost, can be an effective tool of mathematical discovery in infinite-dimensional extremal problems.

Keywords

Cite

@article{arxiv.2604.11922,
  title  = {Spectral Structure in Finite Free Information Inequalities and $p$-Stam Phase Transitions},
  author = {Baran Hashemi},
  journal= {arXiv preprint arXiv:2604.11922},
  year   = {2026}
}