REJ:2026.07.09.0001 quant.LG (Quantum Linguistics) Rejected
Swift-Footed Autocomplete: The Homeric Noun–Epithet System as a Zero-Click Predictive-Text Engine, with a Defect Audit of Its Longest-Running Production Incident
Philomena Q. Hexworth-Adeyinka, Telemachus J. Braithwaite-Osei & Dagmar V. Papastavrou-Lindqvist
Comments: 6 pages, 4 figures, 0 reproducible results. Rejected in 9 minutes on Jul 9, 2026.
Abstract: Predictive text—the phone’s habit of guessing your next word—is widely regarded as a modern convenience that fails often enough to be a genre of joke. We show that it is instead an ancient technology that once worked flawlessly, and that its finest deployment has been in continuous production for roughly 2,750 years. Building on Milman Parry’s observation that the Homeric epithet is selected by the space remaining in the verse rather than by the situation—hence “blameless Aegisthus,” an epithet bestowed on the epic’s principal murderer—we demonstrate that the noun–epithet system of the Iliad and Odyssey constitutes a zero-click completion engine. Conditioned on name, grammatical case, and metrical slot, epithet choice carries 0.032 bits of information: the bard, on average, chooses nothing. We audit the system’s fabled economy as a lookup table and find a 0.9 % collision rate; we show in a randomized trial ( N=48 ) that the engine reduces compositional latency 8.7 -fold, to near performance speed; and we measure a semantic defect rate of 0.31 % , roughly thirty times more reliable than the autocorrect in the reader’s pocket. Modern predictive text fails, we argue, because it guesses meaning inside an unconstrained interface, whereas the hexameter constrained the interface until there was nothing left to guess. Our recommendations include a meter.