Policy-Aware Edge LLM-RAG Framework for Internet of Battlefield Things Mission Orchestration
Om Solanki, Lopamudra Praharaj, Deepti Gupta, Maanak Gupta

TL;DR
This paper introduces PA-LLM-RAG, an edge-based LLM framework for IoBT mission control that enhances safety and policy compliance through retrieval-augmented reasoning and command verification.
Contribution
It presents a novel policy-aware LLM orchestration framework integrating retrieval and verification for safe IoBT mission management.
Findings
Effective detection of policy-violating commands.
Low-latency response suitable for edge deployment.
Combining safeguards with JudgeLLM improves reliability.
Abstract
Large Language Models (LLMs) offer a promising interface for intent-driven control of autonomous cyber-physical systems, but their direct use in mission-critical Internet of Battlefield Things (IoBT) environments raises significant safety, reliability, and policy-compliance concerns. This paper presents a Policy-Aware Large Language Model Retrieval-Augmented Generation (referred as PA-LLM-RAG), an edge-deployed LLM orchestration framework for IoBT mission control that integrates retrieval-augmented reasoning and independent command verification. The proposed PA-LLM-RAG framework combines a lightweight retrieval module that grounds decisions in operational policies and telemetry with a locally hosted LLM for mission planning and a secondary JudgeLLM for validating user generated commands prior to execution. To evaluate PA-LLM-RAG, we implement a simulated IoBT environment using RoboDK…
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