Closed-Form Reviewer Matching — Conflict Exclusion, Fit Gate, Load Balancing

Abstract

When a manuscript enters peer review, an editor must find reviewers who actually know the subject, who have no conflict of interest, and who are not already overloaded. We do this in closed form, in the spirit of Reverse Synthetic Networks (RSN): the corpus's own statistics synthesise the match. A reviewer's expertise — their field, declared keywords, and the text of their own authored work — is a TF-IDF vector; the manuscript is another; fit is their cosine, refined by field/keyword overlap and balanced by current review load. No trained model, no gradient, no embedding service, no per-reviewer profile learning. On a controlled benchmark of 30 reviewer pools with known expertise ground truth, the engine puts a correct expert in the top slot 99.8% of the time (nDCG@5 = 0.980 ± 0.008, MRR 0.999), against 0.52 for a field-matching baseline and 0.08 for random. Three properties make it safe to act on automatically: (1) conflicts are detected automatically from the collaboration record — the manuscript's authors, editor-declared conflicts, anyone who has co-authored with an author, and anyone sharing a lab with the author team are excluded with 100% reliability (while a clean expert is still found 100% of the time); (2) a fit gate returns an empty list for a manuscript with no in-Commons expert — the explicit signal to source reviewers externally — suppressing 100% of no-expert cases at 0 false positives; and (3) a load signal spreads assignments (peak load 7.4 → 6.3, dispersion 0.48 → 0.30) with negligible loss of fit. Matching against a 5,000-reviewer pool takes 131 ms on one CPU core. By design the engine serves the Commons — its Fellows and Patrons; for external reviewers it returns nothing, and the editor sources them by hand.