  High-multiplicity signatures at particle colliders can arise in Standard
Model processes and beyond. With such signatures, difficulties often arise from
the large dimensionality of the kinematic space. For final states containing a
single type of particle signature, this results in a combinatorial problem that
hides underlying kinematic information. We explore using a neural network that
includes a Lorentz Layer to extract high-dimensional correlations. We use the
case of squark decays in $R$-Parity-violating Supersymmetry as a benchmark,
comparing the performance to that of classical methods. With this approach, we
demonstrate significant improvement over traditional methods.
