Optimizing National Security Strategies through LLM-Driven Artificial Intelligence Integration
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Abstract (arXiv)
As artificial intelligence and machine learning continue to advance, we must understand their strategic importance in national security. This paper focuses on unique AI applications in the military, emphasizes strategic imperatives for success, and aims to rekindle excitement about AI's role in national security. We will examine the United States progress in AI and ML from a military standpoint, discuss the importance of securing these technologies from adversaries, and explore the challenges and risks associated with their integration. Finally, we will highlight the strategic significance of AI to national security and a set of strategic imperatives for military leaders and policymakers
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 0 of its 22 references that are in the map. First posted on arXiv 7 May 2023.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 4.
6 papers.
- 2023Towards Knowledge-driven Autonomous Drivingcited by 29
- 2025Agentic AI for autonomous anomaly management in complex systemscited by 4
- 2025On the Performance of an Explainable Language Model on PubMedQAcited by 2
- 2025Semantic Scheduling for LLM Inferencecited by 1
- 2025Cross-Lingual Stability and Bias in Instruction-Tuned Language Models for Humanitarian NLPcited by 1
- 2024Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantificationcited by 0
- 2025Cross-Lingual Stability and Bias in Instruction-Tuned Language Models for Humanitarian NLP26 Oct 2025
- 2025Agentic AI for autonomous anomaly management in complex systems21 Jul 2025
- 2025Semantic Scheduling for LLM Inference13 Jun 2025
- 2025On the Performance of an Explainable Language Model on PubMedQA7 Apr 2025
- 2024Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification20 Mar 2024
- 2023Towards Knowledge-driven Autonomous Driving7 Dec 2023