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This paper applies the cavity method—a technique central to analyzing disordered systems and neural networks—to permutation models on Cayley trees, linking statistical mechanics with representation theory via Schur functions. By deriving instability criteria for paramagnetic solutions in random tensor network contexts, it advances theoretical frameworks relevant to quantum many-body physics and AI-inspired optimization landscapes.
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Cavity method for permutation models on Cayley trees

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arXiv:2606.17751v1 Announce Type: new Abstract: Motivated by permutation statistical models arising in random tensor networks, we study permutation models on a Cayley tree whose variables take values in the symmetric group $\Sn$. The pair interaction is assumed to depend only on the cycle type of the relative permutation. Then the Boltzmann weight is written as a class function on $\Sn$. This property diagonalizes the edge convolution operator in irreducible representation sectors. As a result, the linear stability of the uniform paramagnetic cavity solution is controlled by the character eigenvalue ratios. For cycle-factorized weights, these eigenvalues can be expressed as specializations of Schur functions. We derive the instability criteria and also verify their validity by comparison with direct numerical iterations of the cavity equation.

Abstract:Motivated by permutation statistical models arising in random tensor networks, we study permutation models on a Cayley tree whose variables take values in the symmetric group $\Sn$. The pair interaction is assumed to depend only on the cycle type of the relative permutation. Then the Boltzmann weight is written as a class function on $\Sn$. This property diagonalizes the edge convolution operator in irreducible representation sectors. As a result, the linear stability of the uniform paramagnetic cavity solution is controlled by the character eigenvalue ratios. For cycle-factorized weights, these eigenvalues can be expressed as specializations of Schur functions. We derive the instability criteria and also verify their validity by comparison with direct numerical iterations of the cavity equation.

Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn)

arXiv:2606.17751 [quant-ph]

(or arXiv:2606.17751v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2606.17751 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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