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Battlespace management requires rapid processing of large amounts of data to facilitate informed decision-making. Large Language Models (LLMs) have demonstrated near or above human performance on a wide range of cognitive tasks. Current LLMs are unreliable, have poor explainability, and are prone to bias and hallucinations. As such they are unsuitable for many defense applications, but their abilities can be studied with future, improved LLMs in mind. Specifically, LLM capabilities to synthesize defense-relevant data and make decisions in a combat environment have been largely unexplored. The battlefield information and tactics engine (BITE) uses LLMs as observers and decision-makers in a military environment. A multiplayer video game focusing on modern mechanized combat, Squad by Offworld Industries Ltd., is used as an operating environment due to its moderate realism levels and focus on audio communication between players. BITE is tasked with ingesting tactical data, providing summaries of the current situation, and giving order to a squad of human players. The present work aims to qualitatively assess the suitability of BITE, and LLMs in general, for use in battlespace management systems. Shortcomings are identified in the areas of spatial awareness, decision-making time, and reliability. However, BITE exhibits instances of competent leadership and demonstrates a generalized understanding of modern mechanized combat. While current LLMs are currently deeply unsuitable for combat environments, BITE and similar approaches show promise in wargaming and training applications.
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