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
The article reviews how large language models (LLMs) enable industrial agents to move beyond predefined workflows toward autonomous planning and execution in smart manufacturing. It identifies five key challenges, defines the concept and core characteristics of industrial agents, and presents a unified taxonomy covering application scenarios, embodiment forms, and capability levels. The authors propose a four‑layer system architecture with core modules for closed‑loop task execution, review representative applications across the industrial lifecycle, and outline future research directions.
By Lei Ren, Haiteng Wang, Shixiang Li, Jing Zhang, Yuqing Wang, Jingwei Guo, Yunfei Zhu, Yuanjun Laili, Yang Tang, Lihui Wang
The paper introduces a multi‑agent framework that lets large language models (LLMs) perform controlled experiments using scientific simulation models, specifically for pharmaceutical process design. Given a user query and baseline configuration, the system builds a structured task, designs and runs comparative simulations, interprets outcomes, and generates evidence‑based recommendations for optimizing process parameters. By integrating high‑fidelity simulations with LLMs, the approach yields more specific, actionable outputs and improves user‑rated correctness and helpfulness compared to language‑only reasoning.
By Yuchen Xia, Michael Weyrich, Nasser Jazdi, Johannes St\"umpfle, Johannes Sigel, Akshay Narla, Gavin K. Reynolds, Anna Jawor-Baczynska, Pol Llopart
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:2608. 14579v1 Announce Type: new Abstract: Logic synthesis optimization poses significant challenges due to exponentially growing search spaces, sparse reward signals, and diverse logic structures.
By Rui Yang
arXiv:2606. 26758v1 Announce Type: new Abstract: High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their development heavily relies on manual tuning by domain experts.
By Yaochen Han, Ke Fan, Hongxu Jiang, Wanqi Xu, Weiyu Xie, Runhua Zhang, Chenhui Zhu, Yixiang Zhang