  Thimble regularization as a solution to the sign problem has been
successfully put at work for a few toy models. Given the non trivial nature of
the method (also from the algorithmic point of view) it is compelling to
provide evidence that it works for realistic models. A Chiral Random Matrix
theory has been studied in detail. The known analytical solution shows that the
model is non-trivial as for the sign problem (in particular, phase quenched
results can be very far away from the exact solution). This study gave us the
chance to address a couple of key issues: how many thimbles contribute to the
solution of a realistic problem? Can one devise algorithms which are robust as
for staying on the correct manifold? The obvious step forward consists of
applications to gauge theories.
