Consider the minimum spanning tree of the complete graph with $n$ vertices, when edges are assigned independent random weights. We show that, when this tree is endowed with the graph distance renormalized by $n^{1/3}$ and with the uniform measure on its vertices, the resulting space converges in distribution as $n\to\infty$ to a random limiting metric space in the Gromov--Hausdorff--Prokhorov topology. We show that this space is a random binary $\mathbb R$-tree and has Minkowski dimension $3$ almost surely. In particular, its law is mutually singular with that of the Brownian continuum random tree or any rescaled version thereof. Our approach relies on a coupling between the minimum spanning tree problem and the Erdös--Rényi random graph, and the explicit description of its scaling limit in the so-called critical window.
In the recursive lamination of the disk, one tries to add chords one after an other at random; a chord is kept and inserted if it does not intersect any of the previously inserted ones. Curien and Le Gall [Ann. Probab., vol. 39, pp. 2224--2270, 2011] have proved that the set of chords converges to a limit triangulation of the disk encoded by a continuous process $\mathscr M$. Based on a new approach resembling ideas from the so-called contraction method in function spaces, we prove that, when properly rescaled, the planar dual of the discrete lamination converges almost surely in the Gromov--Hausdorff sense to a limit real tree $\mathscr T$, which is encoded by $\mathscr M$. This is one of the first natural limit real trees which is identified and does not come from the excursion of a Lévy process.
We provide simplified proofs for the asymptotic distribution of the number of cuts required to cut down a Galton--Watson tree with critical, finite-variance offspring distribution, conditioned to have total progeny $n$. Our proof is based on a coupling which yields a precise, non-asymptotic distributional result for the case of uniformly random rooted labeled trees (or, equivalently, Poisson Galton--Watson trees conditioned on their size). Our approach also provides a new, random reversible transformation between Brownian excursion and Brownian bridge.



