Building on advances in the Large Language Model (LLM), this paper explores the application of the Visual Language Model (VLM), specifically a fine-tuned LLaMa 3.2 variant, to identify neutrino interactions in pixelated detector data from high-energy physics (HEP) experiments. We compare this model with state-of-the-art convolutional neural network (CNN) architectures similar to those used in the NOvA and DUNE experiments, which achieved high efficiency and purity in classifying electron and muon neutrino events. Considering both classification performance and interpretability of model predictions, we find that the VLM outperforms the CNN, offers greater flexibility in incorporating auxiliary text or semantic information, and provides more interpretable inference-based predictions. This study highlights the potential of the VLM as a universal backbone for physical event classification due to its high performance, interpretability, and generalizability, opening new avenues for integrating multimodal inference in experimental neutrino physics.
