In this paper, we present Aria-UI, a novel large-scale multimodal model for digital agents that automate tasks by directly manipulating GUIs on diverse platforms. Aria-UI addresses the challenge of linking language instructions to target elements by adopting a purely vision approach, without relying on HTML or AXTree inputs. It adapts to heterogeneous plan instructions via a scalable data pipeline that generates diverse and high-quality instruction samples, and enhances context-aware inference by integrating mixed text and text-image task histories to handle dynamic context during task execution. Experimental results show that Aria-UI achieves state-of-the-art performance on both offline and online agent benchmarks, outperforming existing vision-only and AXTree-based models. All training data and model checkpoints are publicly available.
