State-of-Art-Reviewing: A Radical Proposal to Improve Scientific
  Publication
  Peer review forms the backbone of modern scientific manuscript evaluation.
But after two hundred and eighty-nine years of egalitarian service to the
scientific community, does this protocol remain fit for purpose in 2020? In
this work, we answer this question in the negative (strong reject, high
confidence) and propose instead State-Of-the-Art Review (SOAR), a neoteric
reviewing pipeline that serves as a 'plug-and-play' replacement for peer
review. At the heart of our approach is an interpretation of the review process
as a multi-objective, massively distributed and extremely-high-latency
optimisation, which we scalarise and solve efficiently for PAC and CMT-optimal
solutions. We make the following contributions: (1) We propose a highly
scalable, fully automatic methodology for review, drawing inspiration from
best-practices from premier computer vision and machine learning conferences;
(2) We explore several instantiations of our approach and demonstrate that SOAR
can be used to both review prints and pre-review pre-prints; (3) We wander
listlessly in vain search of catharsis from our latest rounds of savage CVPR
rejections.
