  We present a novel approach to identify potential dispersed signals of new
physics in the slew of published LHC results. It employs a random walk
algorithm to introduce sets of new particles, dubbed "proto-models", which are
tested against simplified-model results from ATLAS and CMS searches for new
physics by exploiting the SModelS software framework. A combinatorial algorithm
identifies the set of analyses and/or signal regions that maximally violates
the Standard Model hypothesis, while remaining compatible with the entirety of
LHC constraints in our database. Crucial to the method is the ability to
construct a reliable likelihood in proto-model space; we explain the various
approximations which are needed depending on the information available from the
experiments, and how they impact the whole procedure.
