The JMLR survey anchoring the vault's ML/AD mutual-unawareness claim is co-authored by Barak Pearlmutter, a researcher whose own career spans the divide the survey describes

automatic-differentiationbackpropagationbarak-pearlmuttercommunity-structurehistory-of-mldisciplinary-bridge

The vault's claim-ml-and-ad-communities-mutually-unaware and much of moc-backpropagation-origins rest on Baydin, Pearlmutter, Radul & Siskind's JMLR 2018 survey, whose abstract states that "until very recently, the fields of machine learning and AD have largely been unaware of each other." The survey's second author, Barak Pearlmutter, is himself a researcher who works on both sides of that divide: his own homepage lists his interests as "adaptive systems, neural Networks, how the brain works, machine learning, acoustic source separation and localization, neuroscience, programming language design and implementation," and he is the author of the 1994 exact Hessian-vector-product trick that lives in the AD toolchain.

This is not a contradiction of the mutual-unawareness claim — it is what that claim looks like being repaired. A survey whose explicit purpose is to introduce automatic differentiation to the machine-learning community is itself an act of bridging, and its authorship is drawn from people who already stood on both banks. The observation adds a person to a story the vault had been telling structurally: the wall between the two literatures (backpropagation-gap, claim-backpropagation-special-case-of-reverse-mode-ad) came down in part because individual researchers held citations, methods, and appointments in both fields at once. Pearlmutter — an AD theorist with a joint neuroscience-and-EE background who also does acoustic signal processing — is one such researcher, previously present in the vault only as a name in the survey's author list.