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The directed landscape is a random directed metric on the plane that arises as the scaling limit of classical metric models in the KPZ universality class. Typical pairs of points in the directed landscape are connected by a unique geodesic.…

Probability · Mathematics 2023-06-23 Duncan Dauvergne

The directed landscape is a prominent model of random geometry which is believed to be the universal scaling limit of all planar random geometries in the Kardar-Parisi-Zhang universality class. It comes equipped with a few different natural…

Probability · Mathematics 2024-04-04 Manan Bhatia

We show that the directed landscape is the unique coupling of the KPZ fixed point from all initial conditions satisfying three natural properties: independent increments, monotonicity, and shift commutativity. Equivalently, we show that the…

Probability · Mathematics 2025-08-12 Duncan Dauvergne , Lingfu Zhang

We study maximal length collections of disjoint paths, or 'disjoint optimizers', in the directed landscape. We show that disjoint optimizers always exist, and that their lengths can be used to construct an extended directed landscape. The…

Probability · Mathematics 2022-06-16 Duncan Dauvergne , Lingfu Zhang

A key task in the study of networked systems is to derive local and global properties that impact connectivity, synchronizability, and robustness; computing shortest paths or geodesics yields measures of network connectivity that can…

Social and Information Networks · Computer Science 2025-03-05 Sahil Loomba , Nick S. Jones

We consider the limiting fluctuations of the geodesic in the directed landscape, conditioning on its length going to infinity. It was shown in \cite{Liu22b,Ganguly-Hegde-Zhang23} that when the directed landscape $\mathcal{L}(0,0;0,1) = L$…

Probability · Mathematics 2026-03-26 Zhipeng Liu , Chen Ma , Tejaswi Tripathi

Within the Kardar-Parisi-Zhang universality class, the space-time Airy sheet is conjectured to be the canonical scaling limit for last passage percolation models. In recent work arXiv:1812.00309 of Dauvergne, Ortmann, and Vir\'ag, this…

Probability · Mathematics 2021-08-26 Erik Bates , Shirshendu Ganguly , Alan Hammond

We prove that two half-space models in the KPZ universality class, exponential last-passage percolation and a family of Poisson-avoiding metrics generalizing colored TASEP, converge to a common scaling limit. This scaling limit is the…

Probability · Mathematics 2026-05-11 Duncan Dauvergne , Lingfu Zhang

Geodesic coalescence, or the tendency of geodesics to merge together, is a hallmark phenomenon observed in a variety of planar random geometries involving a random distortion of the Euclidean metric. As a result of this, the union of…

Probability · Mathematics 2024-04-03 Manan Bhatia

Consider the restriction of the directed landscape $\mathcal L(x, s; y, t)$ to a set of the form $\{x_1, \dots, x_k\} \times \{s_0\} \times \mathbb R \times \{t_0\}$. We show that on any such set, the directed landscape is given by a last…

Probability · Mathematics 2021-10-29 Duncan Dauvergne

A basic question about the directed landscape is how much of it can be reconstructed simply by knowing the shapes of its geodesics. We prove that the directed landscape can be reconstructed from the shapes of its semi-infinite geodesics. In…

Probability · Mathematics 2025-08-05 Mustazee Rahman , Balint Virag

The directed landscape, the central object in the Kardar-Parisi-Zhang universality class, is shown to be the scaling limit of various models by Dauvergne and Vir\'ag (2022) and Dauvergne, Ortmann and Vir\'ag (2018). In his study of…

Probability · Mathematics 2025-03-13 Daniel Spivak

Random geometric networks consist of 1) a set of nodes embedded randomly in a bounded domain $\mathcal{V} \subseteq \mathbb{R}^d$ and 2) links formed probabilistically according to a function of mutual Euclidean separation. We quantify how…

Social and Information Networks · Computer Science 2016-11-17 Alexander P. Kartun-Giles , Orestis Georgiou , Carl P. Dettmann

Let $P$ be a set of $n\geq 3$ points in general position in the plane. The edge disjointness graph $D(P)$ of $P$ is the graph whose vertices are all the closed straight line segments with endpoints in $P$, two of which are adjacent in…

Combinatorics · Mathematics 2023-06-22 J. Leaños , Christophe Ndjatchi , L. M. Ríos-Castro

A directed network connecting a set A to a set B is a digraph containing an a-b path for each a in A and b in B. Vertices in the directed network not in A or B are called Steiner points. We show that in a finitely compact metric space in…

Metric Geometry · Mathematics 2008-10-10 Konrad J Swanepoel

It is believed that for metric-like models in the KPZ class the following property holds: with probability one, starting from any point, there are at most two semi-infinite geodesics with the same direction that do not coalesce. Until now,…

Probability · Mathematics 2024-01-31 Ofer Busani

For the directed landscape, the putative universal space-time scaling limit object in the (1+1) dimensional Kardar-Parisi-Zhang (KPZ) universality class, consider the geodesic tree -- the tree formed by the coalescing semi-infinite…

Probability · Mathematics 2025-04-18 Riddhipratim Basu , Manan Bhatia

When the value $L$ of the directed landscape at a point $(\mathbf{p};\mathbf{q})$ is sufficiently large, the geodesic from $\mathbf{p}$ to $\mathbf{q}$ is rigid and its location fluctuates of order $L^{-1/4}$ around its expectation. We…

Probability · Mathematics 2022-07-13 Zhipeng Liu

We establish fundamental properties of infinite geodesics and competition interfaces in the directed landscape. We construct infinite geodesics in the directed landscape, establish their uniqueness and coalescence, and define Busemann…

Probability · Mathematics 2025-07-08 Mustazee Rahman , Balint Virag

We show that the directed landscape is a black noise in the sense of Tsirelson and Vershik. As a corollary, we show that for any microscopic system in which the height profile converges in law to the directed landscape, the driving noise is…

Probability · Mathematics 2025-04-08 Zoe Himwich , Shalin Parekh
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