DocLayout-VL: A Foundational Model for Hierarchical, Open-set, and Promptable Document Layout Segmentation

Document layout understanding has long relied on bound-ing boxes with non-hierarchical and fixed label spaces. This abstractionfails on irregular regions, discards structural hierarchy, and falls shortof what real-world document tasks demand. We present DocLayout-VL, a foundation model that reframes document layout understandingas promptable open-set hierarchical segmentation. DocLayout-VL pro-duces a hierarchical layout tree, a pixel-accurate mask for each region,and a natural language explanation. Our novel Hierarchical GroundingContextualizer introduces Hierarchical Contextual Attention which mod-els inter-region relationships across hierarchical levels. DocLayout-VLis instruction-tuned on diverse layout tasks and domain-specific ontolo-gies. This enables highly customizable layout understanding via natu-ral language prompts, without any task-specific fine-tuning. To supportfoundation-scale training, we introduce HOPLay-Docs-80M, a large-scale dataset of 2.5M pages, 80M pixel-level masks, 400k open-set labelsand 5M promptable annotations across 12 domains and 58 documenttypes. We also release HOPLay-Docs-Bench, a human-verified evalu-ation suite covering open-set, hierarchical, and promptable layout seg-mentation. DocLayout-VL achieves state-of-the-art results across allbenchmarks, outperforming both specialized layout models and strongmultimodal baselines. Our work establishes a new paradigm for control-lable, semantically grounded layout understanding.