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

ArXi:2604.16755v2 Announce Type: new As large language models (LLMs) are increasingly integrated into daily life, in roles ranging from high-stakes decision 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.