TopoLLM: LLM-driven adaptive tool learning for real-time emergency network topology planning

Communication infrastructure is often among the first casualties in natural or human-induced disasters, severely impairing the coordination and efficiency of rescue operations. Rapid deployment of Unmanned Aerial Vehicles (UAVs) and satellite systems has thus become essential for establishing robust communication links to support rescue-critical tasks. However, existing emergency communication networks rely heavily on domain expertise for topology design, thereby suffering from issues such as inefficient resource allocation and network congestion, among others. To address these challenges, we present TopoLLM, a framework that leverages Large Language Models (LLMs) for tool-driven optimization of emergency network topologies. This framework effectively combines the reasoning capabilities of the LLM with TopoTool, a domain-specific optimization toolkit engineered for high-precision and load-balanced network planning in disaster scenarios. Guided by an adaptive tool-selection mechanism, TopoLLM autonomously generates resilient topologies and allocates resources intelligently, reducing the need for extensive human interventions. Experimental evaluations on simulated disaster scenarios verify that TopoLLM can rapidly generate high-accuracy and robust topologies, achieving notable performance improvements compared with existing approaches.