Low-Rank Adaptation/ LoRA

Definition

Low-Rank Adaptation is fine-tuning technique that updates small rank-decomposition matrices instead of all model weights. In defense applications, it adapts foundation models to doctrine, platforms, units, or coalition terminology without retraining the base model. The hard part is adapter sprawl, unsafe merges, and provenance confusion across mission packages, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a modular update package that matches KhanBMS plug-and-play doctrine, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

adapter fine-tuning method

Operational value

Adapts foundation models to doctrine, platforms, units, or coalition terminology without retraining the base model

Primary risk

Adapter sprawl, unsafe merges, and provenance confusion across mission packages

KhanBMS role

A modular update package that matches KhanBMS plug-and-play doctrine

Related terms

• Parameter-Efficient Fine-Tuning (PEFT)
  Family of methods that customize large models by training a small fraction of parameters.
• Sovereign AI Models (SAI)
  Models trained, hosted, and governed under national or coalition control rather than foreign commercial dependency.
• Secure Model Provenance
  Cryptographic and procedural evidence tracking where a model, adapter, dataset, or artifact came from.
• MLOps for Defense (MLOps-D)
  Lifecycle practices for building, testing, approving, deploying, monitoring, and updating military AI.