提出CAT框架，评估智能体AI任务与目标的对齐程度
Creative Adversarial Testing (CAT): A Novel Framework for Evaluating Goal-Oriented Agentic AI Systems
- 用对抗性测试模拟任务与目标的冲突场景
- 在模拟数据中发现任务偏离目标的深层问题
- 适合开发智能体AI系统的工程师和研究者
智能体AI代表生成式AI能力的范式转变。尽管这类系统潜力巨大，现有评估方法仍主要关注其识别合适智能体、工具和参数的能力，而忽视了任务与整体目标之间的对齐性评估。本文提出创意对抗测试（Creative Adversarial Testing, CAT）框架，旨在捕捉并分析智能体AI任务与其预期目标间的复杂关系。通过基于亚马逊Alexa+语音服务的合成交互数据进行大规模仿真验证，该框架在保护用户隐私的前提下，全面测试边界情况与失效模式。结果表明，CAT框架能提供前所未有的目标-任务对齐洞察，显著提升智能体AI系统的优化与开发效率。
原文摘要 · Abstract (English)
Agentic AI represents a paradigm shift in enhancing the capabilities of generative AI models. While these systems demonstrate immense potential and power, current evaluation techniques primarily focus on assessing their efficacy in identifying appropriate agents, tools, and parameters. However, a critical gap exists in evaluating the alignment between an Agentic AI system's tasks and its overarching goals. This paper introduces the Creative Adversarial Testing (CAT) framework, a novel approach designed to capture and analyze the complex relationship between Agentic AI tasks and the system's intended objectives. We validate the CAT framework through extensive simulation using synthetic interaction data modeled after Alexa+ audio services, a sophisticated Agentic AI system that shapes the user experience for millions of users globally. This synthetic data approach enables comprehensive testing of edge cases and failure modes while protecting user privacy. Our results demonstrate that the CAT framework provides unprecedented insights into goal-task alignment, enabling more effective optimization and development of Agentic AI systems.
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