arXiv:2610.32696v1 Announce Type: new Abstract: Accurate early-stage prediction of cascading failure severity is essential for assessing the vulnerability of power transmission networks. However, existing data-driven approaches often rely on network-specific representations, limiting their generalizability across power system topologies. This work investigates the complementary capabilities of Large Language Models (LLMs) and Graph Neural Networks (GNNs) for cascading failure severity prediction. First, standalone LLM predictors are developed using a LoRA-fine-tuned LLaMA-3-8B-Instruct model with varying degrees of topological information. A hybrid Graph-LLM fusion framework is then proposed, integrating a Topology-Aware Graph Attention Transformer (TAGAT) with the LLM through confidence-gated fusion. TAGAT captures structural dependencies among transmission lines, while the LLM extracts representations from early-stage power-flow observations and aggregated cascade descriptors. The frameworks are evaluated on 30,000 simulated cascading failure scenarios for each of the IEEE 118-bus and IEEE 300-bus systems. Using only the first four cascade steps, the hybrid framework achieves weighted F1 scores of 0.94 and 0.92, respectively. In cross-system transfer from IEEE 118-bus to IEEE 300-bus, it achieves 0.72 zero-shot accuracy, improving to 0.83 with limited target-system calibration, outperforming standalone models and conventional baselines. These results show the potential of combining graph-based structural learning with foundation models to improve cascading failure severity prediction and cross-system transferability.