LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks

ArXi:2603.04818v2 Announce Type: replace Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritize forecasting accuracy without providing operationally interpretable early warnings. This paper proposes an evidence-grounded framework that jointly performs supply chain bottleneck prediction and faithful natural-language risk explanation by coupling a Temporal Graph Attention Network (TGAT) with a structured large language model (LLM) reasoning module.