Injecting pre-computed layout priors from RT-DETR into VLM prompts raises markdown F1 from 0.37 to 0.92 on a 10k-page OOD benchmark and cuts infinite-loop failures across domains.
LayoutLM: Pre-training of Text and Layout for Document Image Under- standing
7 Pith papers cite this work. Polarity classification is still indexing.
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Multimodal LLMs process code as images to achieve up to 8x token compression, with visual cues like syntax highlighting aiding tasks and clone detection remaining resilient or even improving under compression.
Introduces a benchmark dataset for data snapshot extraction focused on semantically meaningful analytical artifacts in institutional documents and shows open-source layout models struggle to generalize from academic benchmarks.
Nougat applies a visual transformer to convert academic PDFs into markup language while accurately handling mathematical content on a new scientific document dataset.
Systematic ablation of TrOCR fine-tuning for medieval HTR finds that freezing up to three encoder or six decoder layers does not significantly harm accuracy and that removing CLAHE contrast normalization yields comparable 7.84% CER on the Cortonese manuscript.
Presents RT-DocLayout, a 33M-parameter end-to-end model extending RT-DETR that unifies layout classification, detection, segmentation, and reading-order prediction at 132.1 FPS with claimed SOTA results on public benchmarks.
Proposes Modality-Aware Credit Assignment (MoCA) with blindfolded-reasoning proxy to reward perception fidelity separately from reasoning in VLMs.
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Nougat: Neural Optical Understanding for Academic Documents
Nougat applies a visual transformer to convert academic PDFs into markup language while accurately handling mathematical content on a new scientific document dataset.