VLM-based harmonization of inconsistent annotations across two document layout corpora raises detection F-score from 0.860 to 0.883 and table TEDS from 0.750 to 0.814 while tightening embedding clusters.
mplug-docowl: Modularized multimodal large language model for document understanding
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
CPT is a fine-tuned language model that uses Creative Markup Language representations of professional designs to generate controllable, stylistically coherent, and fully editable design variations.
InstructTable combines instruction-guided pre-training on structural patterns with visual fine-tuning and a template-free synthetic data generator (TME) to reach state-of-the-art table structure recognition on public benchmarks and a new complex-table test set.
DocSeeker improves long-document understanding in MLLMs via a two-stage training process that combines supervised fine-tuning from distilled data with evidence-aware group relative policy optimization and memory-efficient resolution allocation.
citing papers explorer
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Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization
VLM-based harmonization of inconsistent annotations across two document layout corpora raises detection F-score from 0.860 to 0.883 and table TEDS from 0.750 to 0.814 while tightening embedding clusters.
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CPT: Controllable and Editable Design Variations with Language Models
CPT is a fine-tuned language model that uses Creative Markup Language representations of professional designs to generate controllable, stylistically coherent, and fully editable design variations.
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InstructTable: Improving Table Structure Recognition Through Instructions
InstructTable combines instruction-guided pre-training on structural patterns with visual fine-tuning and a template-free synthetic data generator (TME) to reach state-of-the-art table structure recognition on public benchmarks and a new complex-table test set.
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DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding
DocSeeker improves long-document understanding in MLLMs via a two-stage training process that combines supervised fine-tuning from distilled data with evidence-aware group relative policy optimization and memory-efficient resolution allocation.