  We present a novel algorithm 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 (exploiting the
SModelS software framework). A combinatorial algorithm identifies the set of
analyses and/or signal regions that maximally violates the SM hypothesis, while
remaining compatible with the entirety of LHC constraints in our database.
Demonstrating our method by running over the experimental results in the
SModelS database, we find as currently best-performing proto-model a top
partner, a light-flavor quark partner, and a lightest neutral new particle with
masses of the order of 1.2 TeV, 700 GeV and 160 GeV, respectively. The
corresponding global p-value for the SM hypothesis is approximately 0.19; by
construction no look-elsewhere effect applies.
