ReaDS-KG: An LLM-Knowledge Graph Framework for Reasoned Decision Support in Dynamic Safety-Critical Domains

Safety-critical domains such as military operations, border security, and search-and-rescue must operate under uncertainty, severe time pressure, and continuously changing conditions. In these settings, decision-support systems must not only provide accurate recommendations but also make the underlying reasoning explicit and auditable. This paper introduces ReaDS-KG (Reasoned Decision Support over Knowledge Graphs), an LLM-Knowledge Graph framework that delivers reasoned rather than purely predictive support. ReaDS-KG represents domain knowledge, assets, constraints, and causal dependencies in an ontology-driven knowledge graph, and uses a large language model to (i) translate natural-language questions into Cypher queries, (ii) orchestrate graph-based reasoning over causal structures, and (iii) return narrative answers with explicit justifications grounded in the graph. The framework follows a five-stage pipeline: ontology design, data-to-KG transformation, causal enrichment, LLM-mediated querying, and scenariobased evaluation. To demonstrate its applicability, we instantiate ReaDS-KG in a synthetic brigade-level operational scenario and pose twenty decision-oriented questions, covering feasibility, mobility, sustainment, command-and-control robustness, and risk. We then compare an LLM+KG agent powered by ReaDS-KG to ten active-duty officers using an eight-dimensional scoring rubric. The agent achieves decision-support quality comparable to field-grade officers and clearly above junior officers, while responding at machine response speed and providing transparent reasoning chains. These results suggest that ReaDS-KG can function as a quasi-expert, explainable staff assistant in dynamic safety-critical domains, and the architecture is readily transferable to other safety-critical settings that share similar uncertainty and causal-reasoning requirements, such as border management and disaster response.