On sampling diluted Spin-Glasses with unbounded interactions

Spin-glasses are natural Gibbs distributions that have been studied in Theoretical CS for many decades. Recently, they have been gaining attention from the community as they emerge naturally in neural computation and learning, network inference, optimisation and other areas.
We study the problem of efficiently sampling from spin-glass distributions when the underlying graph is a typical instance of $G(n,d/n)$, i.e., the random graph on $n$ vertices such that each edge appears independently with probability $d/n$, and $d=\Theta(1)$.
Our focus is on the 2-spin model at inverse temperature $\beta$. We consider this distribution to be one of the most interesting case of spin-glasses, and one of the most challenging to analyse, since its Gaussian couplings give rise to unbounded interaction. We employ the well-known Glauber dynamics to sample from the aforementioned distribution.
We show that for the typical instances of the 2-spin model on $G(n,d/n)$, the mixing time of Glauber dynamics is $O\left(n^{1+\Theta(\frac{1}{\sqrt{d}})}\right)$, for any $\beta\leq \frac{1}{4\sqrt{d}}$.
Our results can also be adapted for the case of spin-glass distributions with bounded interactions. In that respect, we obtain rapid mixing of Glauber dynamics for the Viana-Bray model on $G(n,d/n)$ when $\beta\leq \frac{1}{4\sqrt{d}}$. This improves on the current best bound which is $\beta<\frac{0.18}{\sqrt{d}}$.
We utilise stochastic localisation, and in particular, we build and improve on the scheme introduced in [Liu, Mohanty, Rajaraman and Wu: FOCS 2024]. This is the first time that stochastic localisation is used for diluted spin-glasses, where both degrees and interactions can be unbounded.