Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
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
Record details
Published: 20 April 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (20 April 2026), “Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence,” evidence record 5603, https://ethics.ai/record/5603 (originally published by arXiv).
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