The machine-learning and automatic-differentiation communities were mutually unaware until "very recently" — the structural cause of the backprop attribution gap

The authoritative JMLR survey of automatic differentiation states, in its abstract:

"Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results."

This is the structural explanation for the attribution gap the vault documents elsewhere as priority without paternity. It reframes the backpropagation-gap away from a simple story of a foundational work being ignored and toward a more precise one: two literatures that did not overlap, each maintaining its own citation graph, independently arriving at the same technique — reverse-mode differentiation in numerical analysis, backpropagation in neural networks.

The consequence follows mechanically. Linnainmaa's 1976 BIT paper was published inside the numerical-analysis literature and remained effectively invisible to the ML community — and whether it had citation life even within early AD implementation work is open, not established: the canonical early implementation did not cite it (claim-speelpenning-1980-does-not-cite-linnainmaa), and Kedem 1977 is the next candidate to check. (Revisit 2026-07-07, audit correction 2: this sentence originally read "had a modest but real citation life inside the AD / numerical-analysis literature (it is cited by early autodiff implementation work — see the Speelpenning-1980 thread)" — unsupported, and contradicted by the Speelpenning primary itself.) So "essentially uncited before the 2010s" is an overstatement if applied to all academic communities, and accurate only if scoped to the neural-network lineage: the citation that would have mattered for deep learning — Rumelhart, Hinton & Williams (1986) — pointed back to its own authors, not to Linnainmaa (see claim-rhw-1986-reference-list-four-works). The five independent rediscoveries Griewank documents are the same phenomenon viewed from the AD side: when literatures don't read each other, the same idea gets invented once per field. The walls are even finer than "ML vs AD": even within automatic differentiation, the compiler-optimization sub-line built the first working implementation (Speelpenning 1980) without citing the numerical-analysis sub-line that had published the method a decade earlier.

Baydin et al. cite Griewank (2012) and Schmidhuber (2015) as the historical sources for the reinvention pattern, which makes this survey the field's own Tier-1 acknowledgement that its backpropagation genealogy runs through numerical analysis — not a claim imported from the priority-advocacy literature. The survey's exact wording, verified by direct ar5iv full-text fetch on two independent retrievals (2026-07-01 capture):

"In machine learning, a specialized counterpart of AD known as the backpropagation algorithm has been the mainstay for training neural networks, with a colorful history of having been reinvented at various times by independent researchers (Griewank, 2012; Schmidhuber, 2015)."

Correction, 2026-07-07 (higher-model audit, Cali-accepted ruling): an earlier version of this note claimed here that "Linnainmaa" does not appear in the survey's prose and that the survey outsources its history rather than narrating it. False — the survey's §3.3 ("Origins of AD and Backpropagation") narrates the history and names him in the body: "Prior to Werbos, the work by Linnainmaa (1970, 1976) is often cited as the first published description of the reverse mode." See 00-meta/audit-morning-2026-07-07.md, correction 1.