[论文] Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manu...

论文概要

研究领域: ML 作者: Lei Ren, Haiteng Wang, Shixiang Li, Jing Zhang, Yuqing Wang, Jingwei Guo, Yunfei Zhu, Yuanjun Laili, Yang Tang, Lihui Wang 发布时间: 2026-10-22 arXiv: 2610.26941

中文摘要

大语言模型（LLM）正在使智能体从预定义工作流迈向自主规划和执行，为智能制造创造了新的机遇。然而，动态环境、复杂的物理约束和严格的可靠性要求阻碍了通用智能体在智能制造中的直接部署。本文对LLM驱动的工业智能体进行了系统性综述。首先，我们分析了五大关键挑战，讨论了工业智能体的概念和核心特征。然后，建立了涵盖应用场景、具身形式和五个能力等级的统一分类体系。接着，我们提出了四层系统架构，详细阐述了其核心模块和闭环任务执行的操作机制。最后，我们回顾了工业全生命周期中的代表性应用，并展望了未来研究方向。本文为工业智能体的设计、开发和应用提供了结构化的参考。

原文摘要

Large language models (LLMs) are enabling agents to move beyond predefined workflows toward autonomous planning and execution, creating new opportunities for smart manufacturing. However, dynamic environments, complex physical constraints, and stringent reliability requirements hinder the direct deployment of general-purpose agents in smart manufacturing. This article presents a systematic overview of LLM-driven industrial agents. First, we analyze five key challenges and discuss the concept and core characteristics of industrial agents. We then establish a unified taxonomy covering application scenarios, embodiment forms, and five capability levels. Next, we propose a four-layer system architecture and detail its core modules and operational mechanisms for closed-loop task execution. Fina...

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