No independent source corroborates Tieman & Markou (2026)'s 'generative-process diversity' finding as of 2026-11-01

ai-safetycorrelated-errorsalgorithmic-monoculturesource-corroborationsingle-source-capgenerative-process-diversity

A dedicated search — a direct re-check of the arXiv record, general web search under several query formulations, and a check of the two candidate papers the open question itself named as worth checking — located no independent (different author group, different venue) test, replication, or citation of Tieman & Markou's generative-process-diversity finding as of 2026-11-01. The arXiv record for 2609.03422 shows exactly one version, filed by one of the paper's own two authors ("Submission history From: Evan Markou [view email] [v1] Thu, 3 Sep 2026..."), with no journal-ref or errata. The paper's only further public record is a NeurIPS 2026 listing that states its own status plainly — "Poster in Workshop: The Third Workshop on Agents in the Wild..." — authored by the same two people. A workshop poster by a paper's own authors is not outside engagement, and workshop acceptance carries a lighter review bar than the main track.

Of the two candidates the open question named, neither corroborates. Artificial Hivemind (Jiang, Chai, Li, Liu, Fok, Dziri, Tsvetkov, Sap, Albalak & Choi; NeurIPS 2025 Datasets & Benchmarks, Oral) is genuinely independent, peer-reviewed prior work — and is the kind of semantic-similarity assessment Tieman & Markou's own abstract positions their contribution against ("Assessments of language-model populations using semantic similarity demonstrate limited semantic diversity"), though the abstract names no specific paper — but it measures a different construct: semantic homogeneity of model outputs on open-ended queries ("a pronounced Artificial Hivemind effect... characterized by... inter-model homogeneity, where different models produce strikingly similar outputs"), not generative-process diversity against benchmark correlated failure. It is background and contrast, not a test of the −0.216 finding. William F. Bradley's "LLMs and the Madness of Crowds" (arXiv 2411.01539) studies correlated wrong-answer agreement across 37 LLMs — methodologically adjacent to the base phenomenon, claim-correlated-errors-in-llms-exceed-chance-and-rise-with-accuracy — but predates Tieman & Markou by nearly two years and, on an abstract-level read, does not measure anything resembling compression-distance-based generative-process diversity.

This is the same shape the vault has recorded for other single-source-capped preprints: Hu 2026, RIKER, Hexels. The open question stays open: the NeurIPS listing's linked OpenReview page and project page were not opened this session and could in principle carry an outside critique or attempted reproduction.