Artificial Intelligence for Tactical Network Troubleshooting
Name
Jaimes_Mendez_Phantom.pdf
Description
Technical Paper
Size
112.24 KB
Format
Adobe PDF
Checksum (MD5)
fe55e92be67839b1e1090978d5bedc7d
Author(s)
Jaimes, Rafael
Mendez, Maximillian
Date Issued
September 12, 2025
Abstract
The tactical network is a key component of most
United States Marine Corps missions. It is critical to expeditiously
stand up a robust communications architecture for both voice
and data transmissions across a variety of classification levels.
However, when there are unforeseen or induced faults in network
configurations, the establishment time can increase by hours
if not days. The research described in this report sought to
determine if a large language model (LLM), when provided
the correct baseline network configurations, would be able to
identify errors in active working network configurations and
reduce network establishment time. A/B testing was conducted to
see whether teams assisted by artificial intelligence (AI) or control
teams with no AI assistance could establish the network faster.
The LLM hosted by NIPRGPT decreased the establishment time
by 50 percent (p <0.05) compared to warfighters unaided by AI.
The results conclude that AI agents such as LLMs can be useful
in providing commanders with a course of action to establish
command, control, communications, and computers (C4) faster.
Description
The Department of the Air Force Artificial Intelligence Accelerator
Subjects
artificial intelligence
network troubleshooting
military networks
large language models
tactical communications
NIPRGPT
MIT Department
Lincoln Laboratory
Persistent DSpace Link
