Command-agent: Reconstructing warfare simulation and command decision-making using large language models

War rehearsals have become increasingly important in national security due to the growing complexity of international affairs. However, traditional rehearsal methods, such as military chess simulations, are inefficient and inflexible, with particularly pronounced limitations in command and decision-making. The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control. To address this challenge, an intelligent warfare simulation framework named Command-Agent is proposed, which deeply integrates large language models (LLMs) with digital twin battlefields. By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion, the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe–Orient–Decide–Act (OODA) feedback loop. Within the Command-Agent framework, a multi-model collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs. By combining specialized models such as DeepSeek-R1 and MCTool, the limitations of single-model capabilities are overcome. MCTool is a lightweight execution model fine-tuned for military Function Calling tasks. The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs. Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent. Leveraging the multi-model collaborative architecture, during red-blue UAV confrontations involving 2 to 8 UAVs, the integrated score is improved by an average of 41.8% compared to the single-agent system (MCTool), accompanied by a 161.8% optimization in the battle loss ratio. Furthermore, when compared with multi-agent systems lacking the knowledge base, the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%. In comparison with the general model (Qwen2.5–7B), the fine-tuned MCTool leads by 5% in execution efficiency. Therefore, the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.