Machine individuality: Separating genuine idiosyncrasy from response bias in large language models

Computer Science > Artificial Intelligence [Submitted on 18 Apr 2026] Machine individuality: Separating genuine idiosyncrasy from response bias in large language models Valentin Kriegmair, Dirk U. Wulff As large language models (LLMs) are increasingly integrated into daily life, in roles ranging from high-stakes decision support to companionship, understanding their behavioral dispositions becomes critical. A growing literature uses psychometric inventories and cognitive paradigms to profile LLM dispositions. However, these approaches cannot determine whether behavioral differences reflect stable, stimulus-specific individuality or global response biases and stochastic noise. Here, we apply crossed random-effects models -- widely used in psychometrics to separate systematic effects -- to 74.9 million ratings provided by 10 open-weight LLMs for over 100,000 words across 14 psycholinguistic norms. On average, 16.9% of variance is attributable to stimulus-specific individuality, robustly exceeding a statistical null model. Cross-norm prediction analyses reveal this individuality as a coherent fingerprint, unique to each model. These results identify individual differences among LLMs that cannot be attributed to response biases or stochastic noise. We term these differences machine individuality. Comments: 18 pages, 1 figure. Supporting information included Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.16755 [cs.AI]   (or arXiv:2604.16755v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.16755 Focus to learn more Submission history From: Valentin Kriegmair [view email] [v1] Sat, 18 Apr 2026 00:02:41 UTC (49 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)