English

Spectral Separation and Eigenvalue Labelling for Polynomial Tensor Representations of General Linear Groups

Representation Theory 2026-05-11 v5 Group Theory Number Theory

Abstract

Let q=pfq=p^f be a prime power, HGLd(q)H \leq \mathrm{GL}_d(q) a subgroup containing a genuine Singer cycle ss of order qd1q^d-1, and WW an FqH\mathbb{F}_q H-module whose scalar extension restricts to an untwisted polynomial tensor representation L(λ(t))\bigotimes L(\lambda^{(t)}) of the algebraic group GLd\mathrm{GL}_d. If the total polynomial degree satisfies K<q1K < q-1, we prove that distinct weights give distinct eigenvalues of ss on WFqFqdW \otimes_{\mathbb{F}_q} \mathbb{F}_{q^d}. The proof relies on an elementary base-qq injectivity lemma: bounded digit vectors determine distinct residues modulo qd1q^d-1. Consequently, when the tensor product is multiplicity-free for the diagonal torus, the Singer cycle has a simple spectrum. We also provide a shifted exponent formula for situations where Singer eigenvalue data undergo qq-Frobenius shifts, proving separation of distinct shifted digit vectors under the same bound K<q1K<q-1. These results provide a uniform spectral explanation for eigenvalue separation in bounded-degree polynomial tensor representations. Motivated by this, we formulate a conditional rewriting framework that uses compatible base-qq eigenvalue labelling to reduce the reconstruction of the natural action to a functor-specific inversion problem. Finally, the viability of this framework is explicitly demonstrated through computational experiments, prominently featuring a non-trivial, full algebraic reconstruction of the natural action from a strictly multiplicity-free, genuine tensor product representation.

Keywords

Cite

@article{arxiv.2512.00263,
  title  = {Spectral Separation and Eigenvalue Labelling for Polynomial Tensor Representations of General Linear Groups},
  author = {Dang Vo Phuc},
  journal= {arXiv preprint arXiv:2512.00263},
  year   = {2026}
}

Comments

32 pages. In this version, the exposition has been improved and the theoretical framework has been tightened. A non-trivial, genuine tensor-product example has also been added to Section 7 to illustrate the algebraic reconstruction step. The author welcomes any comments and suggestions for improvement

R2 v1 2026-07-01T08:00:26.637Z