The paper presents a framework for deploying large language model (LLM) agents in smart manufacturing, pairing each factory module with an LLM-based agent and an MCP tool server that exposes the module’s skills via OPC UA. Three agent architectures—orchestrator, peer‑to‑peer, and monolithic—are evaluated on nine production challenges in a simulated six‑module hexagonal factory, with the monolithic and peer‑to‑peer designs achieving a 93 % mean solve rate and the orchestrator uniquely handling a silent conveyor‑belt fault in all runs. The study demonstrates that LLM agents can reduce programming effort, handle unforeseen runtime faults, and exhibit emergent fault‑diagnosis behavior without explicit failure logic, thereby establishing a reproducible foundation for LLM‑programmed smart manufacturing.
By Kay K\"ohle, Darko Anicic, Thomas A. Runkler, Ren\'e Graf
arXiv:2505. 16120v3 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) has reshaped agent systems.
By Guannan Liang, Qianqian Tong
arXiv:2506.03828v4 Announce Type: replace
Abstract: AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance schedulin...
By Dhaval Patel, Shuxin Lin, James Rayfield, Nianjun Zhou, Chathurangi Shyalika, Suryanarayana R Yarrabothula, Roman Vaculin, Natalia Martinez, Fearghal O'donncha, Jayant Kalagnanam
Design-to-Plan is a large language model–based multi‑agent framework that automates end‑to‑end manufacturing process planning from 3D CAD models and 2D engineering drawings. The system uses an orchestrator to coordinate specialized agents for feature recognition, drawing analysis, context fusion, knowledge retrieval, process sequencing, tool selection, and report generation, integrating deterministic modules with LLM reasoning. Evaluation on 300 benchmark cases shows high success rates, strong tool selection accuracy, effective conflict detection, and reduced token usage, demonstrating the framework’s ability to produce consistent, traceable design‑to‑plan outputs.
By Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon
The paper introduces AI Control Scientist (AICS), a large language model–driven agent that automatically generates optimized controllers from language design requirements. AICS consists of a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show that AICS outperforms existing automated baselines in design success rate and optimization efficiency, demonstrating its potential to shift control system design from human‑driven to agent‑driven approaches.
AI Control Scientist (AICS) is a large language model–driven agent that automatically generates optimized controllers from language design requirements. It comprises a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show AICS outperforms existing automated baselines in design success rate and optimization efficiency, enabling the creation of multiple representative control systems.
By Haiteng Wang, Weihao Li, Jing Zhang, Lei Ren
arXiv:2607. 09616v1 Announce Type: cross Abstract: As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development.
By Kangwei Xu, Bing Li, Ulf Schlichtmann
arXiv:2606. 12835v1 Announce Type: cross Abstract: The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action.
By Quanyan Zhu
arXiv:2606. 04599v1 Announce Type: new Abstract: Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios.
By Yongzi Yu, Ao Li, Le Wang, Ziyue Li, Fugee Tsung, Yuxuan Liang, Man Li
The paper proposes a Recursive Autonomous Agent (RAA) framework for intelligent manufacturing, combining goal-driven autonomous agents with a recursive organizational structure. It identifies individual autonomy and collective coordination as coupled mechanisms that enable local decision-making and alignment with shared goals. The framework uses goal and capability contracts to define local objectives and facilitate the composition of production units, allowing coordinated groups to function as units within larger organizations.
By Yuning Chen, Hongda Zhang, Linqiang Hu, Ziqing Zhou, Yi Liu, Zhao Huang, Ding Zhou, Haining Xu
The paper introduces little m, an AI agent that helps formulate industrial process control models by combining a domain-specific knowledge repository with LLM-driven interaction. It tackles the challenge of converting messy real-world specifications, including natural language and spatial diagrams, into rigorous mathematical optimization models. The authors also present IPC-Bench, a multimodal dataset of 50 canonical scenarios, and show through automated and human evaluations that little m outperforms state‑of‑the‑art LLMs in generating semantically correct models.
By Yongchao Ye, Xinyu He, Dutliff Boshoff, Way Kuo, Lishuai Li
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
By Konstantinos I. Roumeliotis, Ranjan Sapkota