@inproceedings{li-etal-2026-tux,
title = "{TUX}: Measuring Human{--}{AI} Tacit Understanding",
author = "Li, Yueshen and
Min, Hanyi and
Swain, Vedant Das and
Saha, Koustuv",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1090/",
pages = "23083--23103",
ISBN = "979-8-89176-462-0",
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 behavioral 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 associated with person-level characteristics rather than reflecting only 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 TUX provides a measurable behavioral signal of human{--}LLM tacit understanding, while revealing the limits of profile-based conditioning for capturing deeper representational alignment."
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<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 behavioral 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 associated with person-level characteristics rather than reflecting only 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 TUX provides a measurable behavioral signal of human–LLM tacit understanding, while revealing the limits of profile-based conditioning for capturing deeper representational alignment.</abstract>
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%0 Conference Proceedings
%T TUX: Measuring Human–AI Tacit Understanding
%A Li, Yueshen
%A Min, Hanyi
%A Swain, Vedant Das
%A Saha, Koustuv
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-tux
%X 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 behavioral 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 associated with person-level characteristics rather than reflecting only 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 TUX provides a measurable behavioral signal of human–LLM tacit understanding, while revealing the limits of profile-based conditioning for capturing deeper representational alignment.
%U https://aclanthology.org/2026.findings-emnlp.1090/
%P 23083-23103
Markdown (Informal)
[TUX: Measuring Human–AI Tacit Understanding](https://aclanthology.org/2026.findings-emnlp.1090/) (Li et al., Findings 2026)
ACL
- Yueshen Li, Hanyi Min, Vedant Das Swain, and Koustuv Saha. 2026. TUX: Measuring Human–AI Tacit Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 23083–23103, Budapest, Hungary. Association for Computational Linguistics.