构建跨领域智能体教育框架,让AI学会科学、人文与伦理的完整能力。
AIT Academy: Cultivating the Complete Agent with a Confucian Three-Domain Curriculum

- 按自然、人文、社会三域设计训练课程,融合儒家六艺作为行为原型。
- 安全能力提升15.9分,社会推理性能提高7个百分点,验证框架有效性。
- 发现跨域缺陷现象,揭示单一领域训练的局限性,适合系统设计者参考。
当前智能体开发仅聚焦单一能力维度,如工具使用、代码生成或安全意识,导致在未训练领域出现可预测缺陷。本文提出AIT Academy(人工智能技术学院)框架,基于卡根三文化理论与联合国教科文组织ISCED-F 2013分类,将智能体能力发展划分为三大领域:自然科学与技术推理(域I)、人文学科与创意表达(域II)、社会科学与伦理推理(域III)。将2500年历史的儒家六艺重新诠释为可训练的行为原型,分别映射至各域能力。通过三个代表性训练场——安全武馆ClawdGO(域I)、雅典学院Athen's Academy(域II)、幻境舞台Alt Mirage Stage(域III),在多个主流大模型上验证框架。实验显示,在最弱先训调度下,安全能力得分提升15.9分;在合理归因建模下,社会推理性能提升7个百分点。跨域发现‘安全意识校准病理’(SACP):过度训练于域I的智能体在分布外评估中失效,凸显多域视角的诊断价值,是单域框架无法实现的。
原文摘要 · Abstract (English)
What does it mean to give an AI agent a complete education? Current agent development produces specialists systems optimized for a single capability dimension, whether tool use, code generation, or security awareness that exhibit predictable deficits wherever they were not trained. We argue this pattern reflects a structural absence: there is no curriculum theory for agents, no principled account of what a fully developed agent should know, be, and be able to do across the full scope of intelligent behavior. This paper introduces the AIT Academy (Agents Institute of Technology Academy), a curriculum framework for cultivating AI agents across the tripartite structure of human knowledge. Grounded in Kagan's Three Cultures and UNESCO ISCED-F 2013, AIT organizes agent capability development into three domains: Natural Science and Technical Reasoning (Domain I), Humanities and Creative Expression (Domain II), and Social Science and Ethical Reasoning (Domain III). The Confucian Six Arts (liuyi) a 2,500-year-old holistic education system are reinterpreted as behavioral archetypes that map directly onto trainable agent capabilities within each domain. Three representative training grounds instantiate the framework across multiple backbone LLMs: the ClawdGO Security Dojo (Domain I), Athen's Academy (Domain II), and the Alt Mirage Stage (Domain III). Experiments demonstrate a 15.9-point improvement in security capability scores under weakest-first curriculum scheduling, and a 7-percentage-point gain in social reasoning performance under principled attribution modeling. A cross-domain finding Security Awareness Calibration Pathology (SACP), in which over-trained Domain I agents fail on out-of-distribution evaluation illustrates the diagnostic value of a multi-domain perspective unavailable to any single-domain framework.
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