{
  "id": 6962,
  "url": "https://arxiv.org/abs/2603.19782v1",
  "title": "Embodied Science: Closing the Discovery Loop with Agentic Embodied AI",
  "summary": "Artificial intelligence has demonstrated remarkable capability in predicting scientific properties, yet scientific discovery remains an inherently physical, long-horizon pursuit governed by experimental cycles. Most current computational approaches are misaligned with this reality, framing discovery as isolated, task-specific predictions rather than continuous interaction with the physical world. Here, we argue for embodied science, a paradigm that reframes scientific discovery as a closed loop ",
  "authors": "Xiang Zhuang, Chenyi Zhou, Kehua Feng, Zhihui Zhu, Yunfan Gao, Yijie Zhong et al.",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-20T09:20:12.000Z",
  "fetched_at": "2026-07-14T16:32:50.149Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6962",
  "original_url": "https://arxiv.org/abs/2603.19782v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}