  We report on the asymptotic behaviour of a new model of random walk, we term
the bindweed model, evolving in a random environment on an infinite multiplexed
tree. The term \textit{multiplexed} means that the model can be viewed as a
nearest neighbours random walk on a tree whose vertices carry an internal
degree of freedom from the finite set $\{1,...,d\}$, for some integer $d$. The
consequence of the internal degree of freedom is an enhancement of the tree
graph structure induced by the replacement of ordinary edges by multi-edges,
indexed by the set $\{1,...,d\}\times\{1,...,d\}$. This indexing conveys the
information on the internal degree of freedom of the vertices contiguous to
each edge. The term \textit{random environment} means that the jumping rates
for the random walk are a family of edge-indexed random variables, independent
of the natural filtration generated by the random variables entering in the
definition of the random walk; their joint distribution depends on the index of
each component of the multi-edges. We study the large time asymptotic behaviour
of this random walk and classify it with respect to positive recurrence or
transience in terms of a specific parameter of the probability distribution of
the jump rates. This classifying parameter is shown to coincide with the
critical value of a matrix-valued multiplicative cascade on the ordinary tree
(\textit{i.e.} the one without internal degrees of freedom attached to the
vertices) having the same vertex set as the state space of the random walk.
Only results are presented here since the detailed proofs will appear
elsewhere.
