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arXiv AI By Yueshen Li, Hanyi Min, Vedant Das Swain, Koustuv Saha

TUX: Measuring Human--AI Tacit Understanding

Read the original on arXiv AI →

The paper introduces TUX, a Tacit Understanding Index that measures how similarly humans and large language models (LLMs) place concepts along subjective spectra in a task inspired by the game Wavelength. Using 241 human participants and 200 profile-conditioned LLM agents across four models, the study finds that human–agent pairs with similar traits achieve higher TUX scores, indicating that tacit alignment is linked to person-level characteristics. Regression analyses show that richer predictor sets—including individual traits, decision-making styles, and confidence—improve the explainability of TUX beyond simple trait-distance baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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