{
  "id": 5,
  "url": "https://arxiv.org/abs/2607.11689v1",
  "title": "From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence",
  "summary": "Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, while World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences. However, progress remains fragmented: models use incompatible action spaces and prediction targets, datasets and tasks follow different conventions, and runtime systems e",
  "authors": "Yuanzhi Liang, Xufeng Zhan, Haibin Huang, Chi Zhang, Xuelong Li",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T15:22:56.000Z",
  "fetched_at": "2026-07-14T14:14:15.662Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5",
  "original_url": "https://arxiv.org/abs/2607.11689v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}