The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that many recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment. The MAPF and PR problems are similar: a solution to a MAPF instance is a set of blocked cells in x-y-t space, while a solution to the corresponding PR instance is a set of blocked cells in x-y-z space. We show how to use this similarity to apply several recently developed MAPF algorithms to the PR problem, and discuss their performance on abstract PR instances. We also discuss further research necessary to tackle real-world pipe-routing instances of interest to industry today. This opens up a new direction of industrial relevance for the MAPF research community.
DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching
Peking University, Beijing, China（北京大学）；Georgia Institute of Technology, Atlanta, United States（佐治亚理工学院）；Southeast University, Location, Country（东南大学）；Tsinghua University（清华大学）
A self-learning framework for adaptive free-form pipe routing in aeroengines
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University（浙江大学流体动力与机电系统国家重点实验室）；Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province, Zhejiang University（浙江大学浙江省设计工程与数字孪生工程研究中心）
Design for Manufacturing: A Manufacturability Knowledge-Integrated Reinforcement Learning Framework for Free-Form Pipe Routing in Aeroengines
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University（浙江大学流体动力与机电系统国家重点实验室）；Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province, Zhejiang University（浙江省设计工程与数字孪生工程研究中心）；Zhejiang Changxing Heliang Intelligent Equipment Co., Ltd.（浙江长兴鹤浪智能装备有限公司）
ARMOR-MAD: Adaptive Routing for Heterogeneous Multi-Agent Debate in Large Language Model Reasoning
School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；School of Artificial Intelligence, Shenzhen Technology University（深圳技术大学人工智能学院）
An Agentic Framework with LLMs for Solving Complex Vehicle Routing Problems
School of Computing and Information Systems, Singapore Management University（计算与信息系统学院，新加坡管理大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
Dynamic Mixed-Precision Routing for Efficient Multi-step LLM Interaction
University of Arizona（亚利桑那大学）；University of Pittsburgh（匹兹堡大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Central Florida（佛罗里达中央大学）
Bi-Level Routing and Sparse Spatial Attention based Multi-View BEV 3D Object Detection for Autonomous Driving
Stuart Weitzman School of Design University of Pennsylvania（宾夕法尼亚大学斯图尔特·韦茨曼设计学院）；Courant Institute of Mathematical Science New York University（纽约大学库朗数学科学研究所）
MemRouter: Memory-as-Embedding Routing for Long-Term Conversational Agents
University of Central Florida（佛罗里达大学中央分校）；University of Rochester（罗切斯特大学）；University of Illinois at Chicago（伊利诺伊大学香槟分校）；Case Western Reserve University（凯斯西储大学）
