迈向人工智能代理可靠性的科学

This research introduces a method for quantifying the gap between capability and reliability in AI agents. By analyzing the performance of different types of AI agents in specific tasks, significant discrepancies between their capabilities and actual reliability are revealed. Such gaps can lead to AI agents failing to perform tasks as expected in critical application scenarios. Factors influencing the reliability of AI agents are also explored, including algorithm design, quality of training data, and environmental changes. Finally, directions for future research are proposed to enhance the reliability of AI agents, ensuring their effectiveness and safety in practical applications.