Towards AI-Assisted Generation of Military Training Scenarios

• URL: http://arxiv.org/abs/2511.07690v1
• Date: Wed, 12 Nov 2025 01:11:50 GMT
• Title: Towards AI-Assisted Generation of Military Training Scenarios
• Authors: Soham Hans, Volkan Ustun, Benjamin Nye, James Sterrett, Matthew Green,
• Abstract summary: This paper introduces a multi-agent, multi-modal reasoning framework that leverages Large Language Models (LLMs) to generate critical training artifacts.<n>We structure our framework by decomposing scenario generation into a hierarchy of subproblems, and for each one, defining the role of the AI tool.<n>Our results demonstrate the potential of LLM-driven multi-agent systems to generate coherent, nuanced documents.
• Score: 3.491673646235722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving expert-level performance in simulation-based training relies on the creation of complex, adaptable scenarios, a traditionally laborious and resource intensive process. Although prior research explored scenario generation for military training, pre-LLM AI tools struggled to generate sufficiently complex or adaptable scenarios. This paper introduces a multi-agent, multi-modal reasoning framework that leverages Large Language Models (LLMs) to generate critical training artifacts, such as Operations Orders (OPORDs). We structure our framework by decomposing scenario generation into a hierarchy of subproblems, and for each one, defining the role of the AI tool: (1) generating options for a human author to select from, (2) producing a candidate product for human approval or modification, or (3) generating textual artifacts fully automatically. Our framework employs specialized LLM-based agents to address distinct subproblems. Each agent receives input from preceding subproblem agents, integrating both text-based scenario details and visual information (e.g., map features, unit positions and applies specialized reasoning to produce appropriate outputs. Subsequent agents process these outputs sequentially, preserving logical consistency and ensuring accurate document generation. This multi-agent strategy overcomes the limitations of basic prompting or single-agent approaches when tackling such highly complex tasks. We validate our framework through a proof-of-concept that generates the scheme of maneuver and movement section of an OPORD while estimating map positions and movements as a precursor demonstrating its feasibility and accuracy. Our results demonstrate the potential of LLM-driven multi-agent systems to generate coherent, nuanced documents and adapt dynamically to changing conditions, advancing automation in scenario generation for military training.

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