提出面向复杂系统的决策中心型AI框架，提升工业与企业级智能系统的可靠性与可治理性。
Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems
- 构建双世界模型：组织层与现场层协同，实现战略与执行联动
- 通过模式智能耦合两模型，支持多AI应用无缝集成与动态更新
- 强调决策闭环自演化，适合工业智能系统研发与企业级落地场景
随着人工智能持续发展，其应用已超越大语言模型和文本图像生成，逐步融入工具、智能体与协同机制以解决真实商业与工业问题。然而，现有AI在复杂现实系统中的可靠性、可行性、韧性与责任性仍面临挑战。本文整合相关研究，提出‘主动式人工智能’（Enactive AI）的概念框架，用于企业与工业推理、现场级决策支持及执行反馈。该框架包含四个互补角色：组织世界（从战略制度视角建模企业运营逻辑与行为），现场世界（从操作实现视角建模物理边界内的工业优化与执行），模式智能（作为两世界间的耦合机制，编织各类AI应用），以及主动决策循环（触发自我演化动态过程，持续更新与审计整个框架）。通过聚焦复杂系统中的决策智能，Enactive AI将AI边界从模型能力拓展至系统感知行动，为可扩展、可治理、具社会价值的AI部署开辟新路径。未来AI进步不仅应衡量模型生成或自动化能力，更应评估智能系统在支撑关键行动、负责任治理与可持续社会价值方面的表现——这正是企业级与工业复杂系统中下一代AI研究的核心前沿。
原文摘要 · Abstract (English)
As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve real business and industrial problems. However, the power of AI is not verified under these real-world complex systems for various reasons, considering reliability, feasibility, resilience, and responsibility requirements in real commercial and industrial operations. This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback. Four complementary roles organize the framework: an Organizational World defines operations management logic and an organizational behavior world model behind an enterprise from a strategic-institutional horizon; a Site World defines a physically bounded industrial optimization and execution world model from an operational-realization horizon; Schema Intelligence provides the coupling mechanism between two world models to weave various AI applications via two models; and Enactive Decision Cycle triggers the self-evolving dynamic process to update and audit the entire framework. By foregrounding decision intelligence in complex systems, Enactive AI expands the frontier of AI from model capability to system-aware action, opening new possibilities for scalable, governable, and socially valuable AI deployment. Enactive AI points toward a future in which AI progress is measured not only by what models can generate or automate, but by how reliably intelligent systems can support consequential action, responsible governance, and durable social value in the complex systems that shape modern life, which we believe will define the next frontier of AI research for enterprise-level and industrial complex systems.
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