paper-with-me

홈 › Papers

AIT Academy: Cultivating the Complete Agent with a Confucian Three-Domain Curriculum

2026-04-20 · Jiaqi Li, Lvyang Zhang, Yang Zhao, Wen Lu, Lidong Zhai arxiv

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.

📄 PDF Abstract BibTeX arXiv:2604.17989

Code (0)

등록된 구현이 없습니다.

Tasks

Code Generation

Similar Papers 제목 키워드 기반

CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

2023-10-26 · Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin 외

Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning. While most of the work has focused on cooperation and collaboration between agents,…

Language ModelingLanguage ModellingLarge Language Model

The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems

2025-04-17 · Lidong Zhai, Zhijie Qiu, Lvyang Zhang, Jiaqi Li 외

This paper proposes the "Academy of Athens" multi-agent seven-layer framework, aimed at systematically addressing challenges in multi-agent systems (MAS) within artificial intelligence (AI) art creation, such as collabor…

Federated LearningMeta-Learning

Google Research Football: A Novel Reinforcement Learning Environment

2019-07-25 · Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając 외

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner. …

Game of Footballreinforcement-learningReinforcement LearningReinforcement Learning (RL)

HistoLens: An LLM-Powered Framework for Multi-Layered Analysis of Historical Texts -- A Case Application of Yantie Lun

2024-11-15 · Yifan Zeng

This paper proposes HistoLens, a multi-layered analysis framework for historical texts based on Large Language Models (LLMs). Using the important Western Han dynasty text "Yantie Lun" as a case study, we demonstrate the …

graph constructionnamed-entity-recognitionNamed Entity Recognition

CultivAgents: Cultivating Relationship-Centered Multi-Agent Systems for Personalized Gardening

2026-05-22 · Yiyang Wang, Moeiini Reilly, Britney Johnson, Kefei Yan 외 arxiv

Gardening is critical to support well-being, cultural continuity, and food autonomy, yet existing digital tools often provide generic advice that overlooks gardeners' skills, local ecologies, seasons, and cultural contex…