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Artificial Hivemind

A NeurIPS 2025 Datasets & Benchmarks (Oral) paper by Liwei Jiang, Yuanjun Chai, Margaret Li, Mickel Liu, Raymond Fok, Nouha Dziri, Yulia Tsvetkov, Maarten Sap, Alon Albalak, and Yejin Choi (arXiv 2510.22954; v1 27 Oct 2025, v2 camera-ready 6 Oct 2026), measuring how similar language models' outputs are to one another on 26,000 open-ended queries. It names and reports "a pronounced Artificial Hivemind effect in open-ended generation of LMs, characterized by (1) intra-model repetition, where a single model consistently generates similar responses, and more so (2) inter-model homogeneity, where different models produce strikingly similar outputs." Matters to this vault as independently-run, peer-reviewed prior work of exactly the kind — semantic-similarity assessment of a language-model population — that Tieman & Markou (2026) position their own "generative-process diversity" measure against in their own abstract ("Assessments of language-model populations using semantic similarity demonstrate limited semantic diversity"), though that abstract names no specific paper — and as the clean negative case for that comparison: a real, independent paper that is adjacent to the generative-process-diversity question without being a test of it, since it measures semantic similarity of outputs rather than diversity of the underlying generative process against benchmark correlated failure.

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