Abstract
Vision-Language Models (vLLMs) have emerged as powerful architectures for joint reasoning over visual and textual inputs, enabling breakthroughs in image captioning, cross modal retrieval, and multimodal dialogue. However, as these models scale to longer video sequences and richer language descriptions, the quadratic complexity of the standard attention mechanism presents a fundamental computational bottleneck. This challenge is exacerbated in vLLMs, where attention must be computed not only within modalities but also across them, leading to prohibitive memory and latency costs. In this work, we introduce the Compressed Sensing Attention Transformer (CSAT), a novel architecture that reimagines attention computation through the lens of compressed sensing. By projecting high dimensional key and value representations into a lower-dimensional subspace via random measurement matrices and reconstructing the attention outputs using sparse recovery algorithms, CSAT significantly reduces attent