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EP188: [Agent-World] AI Building Its Own Training Worlds
Title: Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Source: http://arxiv.org/abs/2604.18292v1
Summary:
Agent-World provides a foundational framework for scaling general agent intelligence by autonomously synthesizing thousands of real-world environment themes and tasks for continuous self-evolution. The co-evolution of agent policies and environments enables models to identify and bridge capability gaps, allowing smaller models to consistently outperform larger proprietary baselines.
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By Yun WuTitle: Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Source: http://arxiv.org/abs/2604.18292v1
Summary:
Agent-World provides a foundational framework for scaling general agent intelligence by autonomously synthesizing thousands of real-world environment themes and tasks for continuous self-evolution. The co-evolution of agent policies and environments enables models to identify and bridge capability gaps, allowing smaller models to consistently outperform larger proprietary baselines.