The cross-vendor citation-selection agreement behind citation-popularity bias collapses under recursion when candidates are AI-generated, even as concentration on real papers survives

chatgptllmcitation-metricsmatthew-effectbibliometricsalgorithmic-monoculture

Within the same twelve-round recursive citation-selection benchmark as the dilution finding, Alemohammad, Zhang, Tang, Qin, Mai, Abbasi, Baraniuk & Wang separately tested whether the "citation monoculture" — the paper's term for the near-identical, within- and across-vendor citation preferences it establishes at round 0 — persists once most of the candidate pool is AI-generated rather than real. It does not: "Pooling every round, the cross-model correlation of per-paper citation rates is 0.68 among seeds and 0.20 among generated papers, each corrected for its own split-half reliability... The models simply do not share it. Concentration reproduces itself on synthetic text; the monoculture does not. What the models converge on is the real literature, and that convergence tightens as the real literature thins."

This qualifies the concentration finding in an important direction: whatever compounds under recursion is not a uniform, cross-vendor-shared popularity signal growing stronger together. It is a shared preference for a shrinking set of already-established real papers, alongside increasingly idiosyncratic, per-model preferences over the AI-generated content surrounding them. The Matthew-effect shape — credit compounding toward what is already credited — survives and intensifies specifically for pre-existing literature; it does not straightforwardly generalize into a stronger or more unified bias toward whatever an LLM itself most recently produced. This bears on, without resolving, whether popularity bias compounds across trained model generations: even within a single fixed-model recursion, the "monoculture" that makes the bias look like one shared phenomenon across vendors is itself fragile once the inputs stop being real, independently-authored work.