Is the Widrow–Hoff LMS rule established as a stochastic gradient descent method by a Tier 1–2 primary source, not just Wikipedia?

claim-amari-stochastic-descent-and-widrow-hoff-lms-are-sgd-siblings rests one of its two legs on a Tier-4 Wikipedia characterization: "It is a stochastic gradient descent method in that the filter is only adapted based on the error at the current time" ("Least mean squares filter"). Characterizing how LMS relates to gradient descent is a technical-mechanism claim, which under the sources.md sourcing floor requires Tier 1–2 — Wikipedia here is a pointer to a primary, not the source itself. Until that grounding is in the vault, the sibling note stays seedling.

What would answer it.

• Widrow & Hoff, "Adaptive switching circuits," 1960 IRE WESCON Convention Record — the original LMS/ADALINE paper. Confirm the rule is the instantaneous-error (single-sample) update it is later called an SGD method for.
• Widrow & Stearns, Adaptive Signal Processing (Prentice-Hall, 1985) — the standard text that derives LMS explicitly as an instantaneous-gradient stochastic approximation to steepest descent (E[gradient] = true gradient; single-sample estimate). A verbatim quote from its LMS derivation would clear the mechanism floor at Tier 1.
• Either confirms the technical framing already asserted from the vault's Tier-1 Amari side (claim-amari-1968-saito-experiment-primary-read) and the SGD-family root (claim-robbins-monro-1951-stochastic-approximation).

Why it matters. LMS is the hinge node in moc-backpropagation-origins connecting neural-net history to Ted Hoff and the Intel 4004 (claim-ted-hoff-widrow-phd-student-architected-intel-4004). If "LMS is SGD" is going to carry weight across that cluster, it should not rest on an aggregator. Related but distinct open thread: question-lucky-widrow-adaptive-filter-mutual-awareness.

Next move. Pull the Widrow & Stearns LMS derivation (or the 1960 WESCON paper) and lift the exact phrase; on success, upgrade the sibling note past seedling and record the primary quote there.