{"ID":2829566,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12356","arxiv_id":"2512.12356","title":"Tacit Understanding Game (TUG): Predicting Interpersonal Compatibility","abstract":"Research on relationship quality often relies on lengthy questionnaires or invasive textual corpora, limiting ecological validity and user privacy. We ask whether a sequence of single-word choices made in a playful setting can reveal personality and predict interpersonal compatibility. We introduce the Tacit Understanding Game (TUG), a two-player online word association game. We collect word choice traces, annotate a subset with psychological ground truth scales, and bootstrap a larger synthetic corpus via large language model simulation. TUG demonstrates that minimal, privacy preserving signals can support relationship matching, offering new design space for social platforms.","short_abstract":"Research on relationship quality often relies on lengthy questionnaires or invasive textual corpora, limiting ecological validity and user privacy. We ask whether a sequence of single-word choices made in a playful setting can reveal personality and predict interpersonal compatibility. We introduce the Tacit Understand...","url_abs":"https://arxiv.org/abs/2512.12356","url_pdf":"https://arxiv.org/pdf/2512.12356v1","authors":"[\"Yueshen Li\",\"Krishnaveni Unnikrishnan\",\"Aadya Agrawal\"]","published":"2025-12-13T15:01:32Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Language Model\"]","has_code":false}