TUX: Measuring Human--AI Tacit Understanding

• URL: http://arxiv.org/abs/2605.30930v1
• Date: Fri, 29 May 2026 07:19:58 GMT
• Title: TUX: Measuring Human--AI Tacit Understanding
• Abstract summary: Tacit Understanding Index (TUX) is a measure of similarity between human and agent judgments.<n>We find that nearest human--agent pairs in trait space achieve significantly higher TUX.<n> Regression analyses show that TUX becomes more explainable as predictor sets become richer.
• Score: 10.755611801171995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) increasingly act as collaborative partners, human--AI alignment is often evaluated through explicit task success, accuracy, or reward optimization. Yet many collaborative settings depend on tacit understanding: whether an agent can align with a human's evaluative stance or representational priors without clear objectives, communication, or feedback. To study this capacity, we develop a spectrum-placement task inspired by the social party game Wavelength, in which humans and agents independently place concepts along subjective spectra. We operationalize the Tacit Understanding Index (TUX) as a pairwise measure of similarity between human and agent judgments, and evaluate it with 241 human participants and 200 profile-conditioned LLM agents across four models. We find that nearest human--agent pairs in trait space achieve significantly higher TUX, suggesting that tacit alignment is structured by person-level characteristics rather than random similarity. Regression analyses show that TUX becomes more explainable as predictor sets become richer, with individual traits, decision-making styles, and confidence improving over aggregate trait-distance baselines. These findings suggest that tacit understanding between humans and LLMs is measurable, while revealing the limits of profile-based conditioning for capturing deeper representational alignment.

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