  We present a novel approach to solving combinatorial assignment problems in
particle physics without the need to introduce prior knowledge or assumptions
about the particles' decay. The correct assignment of decay products to parent
particles is achieved in a model-agnostic fashion by introducing a novel neural
network architecture, Passwd-ABC, which combines a custom layer based on
attention mechanisms and dual autoencoders. We demonstrate how the network,
trained purely on background events in an unsupervised setting, is capable of
reconstructing correctly hypothetical new particles regardless of their mass,
decay multiplicity and substructure, and produces simultaneously an anomaly
score that can be used to efficiently suppress the background. This model
allows to extend the suite of searches for localized excesses to include
non-resonant particle pair production where the reconstruction of the two
resonant masses is thwarted by combinatorics.
