{
  "id": 5603,
  "url": "https://arxiv.org/abs/2604.18292v1",
  "title": "Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence",
  "summary": "Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services, but training robust agents remains limited by the lack of realistic environments and principled mechanisms for life-long learning. In this paper, we present \\textbf{Agent-World}, a self-evolving training arena for advan",
  "authors": "Guanting Dong, Junting Lu, Junjie Huang, Wanjun Zhong, Longxiang Liu, Shijue Huang et al.",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-20T14:01:10.000Z",
  "fetched_at": "2026-07-14T16:31:53.165Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5603",
  "original_url": "https://arxiv.org/abs/2604.18292v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}