ARES-KG: An LLM-Knowledge Graph Framework for Reasoned Military Decision Making in Operational Support for Real-Time Causal Foresight

Abstract

Modern military decision-making demands real-time integration of structured knowledge, causal reasoning, and dynamic operational context. We introduce ARES-KG (Actionable & Reasoned Edge Support over Knowledge Graphs), a hybrid decision-support framework whose central contribution is the integration of explicit causal graph semantics—relations such as BLOCKS_ROUTE, LIMITS_MOBILITY_OF, and DELAYS, elevated to first-class queryable edges—into an LLM–KG pipeline. This design lets commanders trace multi-order operational effects (e.g., bridge destruction → mobility degradation → resupply delay → mission slippage) and answer counterfactual queries through deterministic graph traversal rather than free-form LLM speculation. Three supporting elements operationalize the central claim: a closed-loop NL→Cypher interaction layer that makes the causal layer accessible at staff tempo with full auditability; a lightweight ontology-driven CSV→Neo4j architecture suitable for hybrid edge-cloud deployment; and an eight-dimensional human–AI evaluation rubric in which one dimension, Causal Foresight, directly tests the central claim. In a synthetic brigade-level case study, ARES-KG generated transparent, explainable reasoning chains and exposed hidden multi-hop dependencies across sustainment, maneuver, fires, ISR, and C2. In a small-scale study with 10 active-duty officers spanning ranks from second Lieutenant to Colonel, an ARES-KG–enabled LLM achieved a Decision Support Score in the upper band of the sample—at the field-grade rank-group mean and above every junior officer—while producing answers in seconds rather than minutes. ARES-KG thus represents a concrete step toward next-generation human–AI collaborative command systems that augment, rather than replace, expert judgment under operational time pressure.