Before revision:  Moreover, we design a hierarchical model  H-VILA that encodes these layout structures and record a up-to 70\% efficiency boost without hurting prediction accuracy. The experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset covering 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL
After revision:  Moreover, we design a hierarchical model H-VILA that encodes these layout structures and record a up-to 47\% inference time reduction with less than 1.5\% Macro F1 loss for the text classification models. Experiments are conducted on a newly curated evaluation suite, S2-VLUE, with a novel metric measuring VILA awareness and a new dataset covering 19 scientific disciplines with gold annotations . Pre-trained weights, benchmark datasets, and source code will be available at URL