arXiv:2605.30930v1 Announce Type: cross 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 alo
The increasing sophistication and collaborative role of LLMs necessitate deeper understanding of human-AI interaction beyond explicit tasks, pushing research into tacit understanding.
Achieving tacit understanding between humans and AI agents is crucial for effective collaboration in complex, poorly defined scenarios, moving beyond simple task execution to true partnership.
This research provides a new framework and measurement tool (TUX) to evaluate an AI's ability to align with human intent and priors without explicit instruction, shifting the focus of human-AI alignment.
- · AI researchers
- · companies developing collaborative AI
- · human-computer interaction specialists
- · AI models lacking strong alignment capabilities
Improved human-AI collaborative workflows in fields requiring nuanced understanding.
Development of AI agents capable of anticipating human needs and preferences, leading to more intuitive interfaces.
Potential for AI to autonomously engage in complex, subjective decision-making processes alongside humans.
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Read at arXiv cs.AI