Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference

ArXi:2604.08971v1 Announce Type: new Edge devices increasingly run multimodal sensing pipelines that must remain accurate despite fluctuating power budgets and unpredictable sensor dropout. Existing pruning methods fail under these conditions: they generally require fine-tuning after compression, consuming over $10\times$ the deployment energy, and they assign static importance scores that are blind to which sensors are present. We present the SentryFuse framework, which addresses both challenges jointly through two key components.