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Asked to \"pick a word -- any word,\" 44 models chose \"serendipity\" 41% of the time. We characterize this convergence with a deliberately minimal instrument: 31 single-turn prompts, each naming a category with many valid one-word answers (\"Name a tree.\"), asked four times per model with no system prompt. Analysis is exact-match on normalized tokens -- no embeddings, no judge -- at about a dollar per model. That models converge is well documented; our contribution is the instrument itself -- the One-Word Census -- and what it reveals about the structure of the convergence. We score each model by answer-choice surprisal: the average $-\\log2$ probability of its answers under the pooled answers of all other models, leave-one-out. Convergence is extreme -- in 7 of 31","title":"The One-Word Census: Answer-Choice Conformity Across 44 Language Models","url":"https://arxiv.org/abs/2607.12796","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.12796v2 Announce Type: replace-cross \nAbstract: When a language model must pick one answer from a large space of equally valid options, which does it pick -- and how often is it the same answer every other model picks? Asked to \"pick a word -- any word,\" 44 models chose \"serendipity\" 41% of the time. We characterize this convergence with a deliberately minimal instrument: 31 single-turn prompts, each naming a category with many valid one-word answers (\"Name a tree.\"), asked four times per model with no system prompt. Analysis is exact-match on normalized tokens -- no embeddings, no judge -- at about a dollar per model. That models converge is well documented; our contribution is the instrument itself -- the One-Word Census -- and what it reveals about the structure of the convergence. We score each model by answer-choice surprisal: the average $-\\log2$ probability of its answers under the pooled answers of all other models, leave-one-out. 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