The Brownian tree is the scaling limit of many random tree models for which the square of the diameter is of the order of the number of vertices. In contrast to this universality, proofs of such convergences commonly rely on model-specific methods. To provide a conceptual understanding of the universality of the Brownian tree, we show that it is uniquely characterized by a uniform self-similar decomposition property. This leads to a general proof scheme for convergences to the Brownian tree that does not require the computation of finite-dimensional limit distributions. […]

Self-repelling walks and processes are stochastic processes that are influenced by their past behaviour, in a way that makes them try to avoid their past trajectory. In this talk, I will first present a toy model for self-repelling random walks introduced by Toth and Werner, which allows to present results and methods that generalise to more complex models. I will then present the « true » self-avoiding walk (TSAW) and state the results from an article by Toth in 1995. Last, I will informally present the « true » self-repelling […]