Figure 1
The distribution of publication venues for the reviewed papers.
Table 1
Articles Analyzed in the Literature Review.
| TYPE | COUNT | PAPERS |
|---|---|---|
| Survey | 8 | Andreoni et al. (2024), Geist et al. (2024), Liu G. et al. (2024), Stebbins et al. (2024), Feffer et al. (2024), Moy and Gradon (2023), Huang et al. (2025), Mikhailov (2023) |
| Proposition | 4 | Black et al. (2024), Oniani et al. (2023), Lee et al. (2025), Tian et al. (2025) |
| Overview | 3 | Brearcliffe et al. (2023), Kelly and Smith (2024), Rashid et al. (2023) |
| Application | 10 | Hua et al. (2024), Goecks and Waytowich (2024), Chuang and Cheng (2022), Liu X. et al. (2024), Liu T. et al. (2025), Barzyk et al., (2024), Lee et al. (2023), Ruiz and Sell (2024), Rivera et al (2024), Lin et al. (2024) |
| Review | 2 | Zarrar and Kakar (2024), Caballero and Jenkins (2025) |
| Other | 2 | Marcellino et al. (2023), Beauchamp-Mustafaga et al. (2024) |
| Total | 29 |
Table 2
GenAI Application (Type A) and Proposition (Type P) Papers.
| PAPER | TYPE | TOPIC | KEY CONTRIBUTION |
|---|---|---|---|
| Lin et al. (2024) | A | Decision making and decision support | Historic battle analysis with LLM multiagent simulations |
| Hua et al. (2024) | A | Decision making and decision support | Historic strategic international conflict analysis with LLM multiagent simulations |
| Goecks and Waytowich (2024) | A | Decision making and decision support | Enhancing course of action (COA) generation with LLMs |
| Rivera et al. (2024) | A | Decision making and decision support | Examining escalation risks associated with LLM use in decision making |
| Chuang and Cheng (2022) | A | Decision support | Conversational AI systems for military training using intent detection and response generation techniques |
| Liu X. et al. (2024) | A | Information extraction and fine-tuning | LLM approach for extracting military equipment entities from unstructured text to build a military knowledge base |
| Barzyk et al. (2024) | A | Cybersecurity | GenAI methodology for automating data tagging in military zero trust architecture cybersecurity frameworks |
| Lee et al. (2023) | A | Decision making and decision support | Proposes Deep AI Military Staff (DAMS), a multi-agent AI system for battlefield decision- making and introduces Multi-Agent Collaboration Architecture to enhance situational awareness |
| Ruiz & Sell (2024) | A | Information extraction and fine-tuning | Presents TRACLM, a fine-tuned Large Language Model (LLM) developed for the U.S. Army to improve AI-driven decision-making and intelligence analysis. |
| Liu T. et al. (2025) | A | Cybersecurity | The use of GenAI for enhancing cross-layer covert communication in military networks. |
| Black et al. (2024) | P | Strategic advantage | Proposes a strategic framework that positions GenAI within the broader context of military competition |
| Oniani et al. (2023) | P | Ethical principles | Proposes ethical cross-domain principles for transparency, value-alignment and accountability |
| Lee et al. (2025) | P | Collaboration | Proposes a system-level architecture using FL as a collaborative framework for LLM training for allied nations. |
| Tian et al. (2025) | P | Unmanned systems | Explores the integration of LLMs and LVMs into UAVs |
Figure 2
The known or estimated (uncertain) parameter counts for a selection of top models from 2023 to 2025 (Cardillo, 2025, Farabet & Warkentin 2025, LLM stats, 2025, Luukkonen et al., 2024, Ali et al. 2024, Martins et al., 2024, Martins et al., 2025).