A new arXiv paper introduces Qwen-AgentWorld, a novel framework that leverages language-based world models to simulate environments for training general-purpose AI agents. By using large language models to predict environmental states and outcomes, the research aims to significantly enhance the decision-making and adaptability of autonomous systems across diverse tasks.