{
  "id": 16615,
  "url": "https://arxiv.org/abs/2608.01127",
  "title": "MiniWorld: Democratizing the Training of Video World Models from Scratch",
  "summary": "Video world models predict future observations conditioned on historical observations and control signals, enabling long-horizon generation through autoregressive state transitions. Unlike conventional video generation models that primarily capture visual appearance and motion, video world models learn the underlying dynamics governing environment evolution under agent actions, providing a foundation for embodied AI and interactive simulation. Recent progress has largely relied on adapting pretr",
  "authors": "Yian Zhao, Ruochong Zheng, Hongcan Guo, Yu Yan, Jian Zhang, Jie Chen",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-01T20:00:00.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/16615",
  "original_url": "https://arxiv.org/abs/2608.01127",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}