{
  "id": 3451,
  "url": "https://arxiv.org/abs/2605.30930v1",
  "title": "TUX: Measuring Human--AI Tacit Understanding",
  "summary": "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 ",
  "authors": "Yueshen Li, Hanyi Min, Vedant Das Swain, Koustuv Saha",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-29T07:19:58.000Z",
  "fetched_at": "2026-07-14T16:30:14.372Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3451",
  "original_url": "https://arxiv.org/abs/2605.30930v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}