TUX: Measuring Human--AI Tacit Understanding
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
Record details
Published: 29 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (29 May 2026), “TUX: Measuring Human--AI Tacit Understanding,” evidence record 3451, https://ethics.ai/record/3451 (originally published by arXiv).
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