The paper introduces a new task called compositional layout understanding, focusing on interpreting complex, multi-layer document and UI layouts that involve hierarchical relationships among visually entangled elements. It presents CoDeLayout, a VQA dataset of about 20,000 real-world layouts annotated with compositional element pairs and design intent, and identifies two main challenges for current vision‑language models: semantic drift between textual metadata and visual content, and structural ambiguity in hierarchical inter‑element relationships. To address these, the authors propose MASON, a post‑training paradigm that combines multimodal alignment and structural perception, achieving a 91.66% accuracy on CoDeLayout and outperforming full‑data direct fine‑tuning with only 30% of the training data.
arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.
By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang
arXiv:2607. 10400v1 Announce Type: cross Abstract: Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc.
By Abhigya Verma, Khyati Mahajan, Amit Kumar Saha, Shruthan Radhakrishna, Sagar Davasam, Vikas Yadav, Sai Rajeswar Mudumba
arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.
By Miguel Lopez-Duran, Elena Marrero, Julian Fierrez, Marta Robledo-Moreno, Ruben Vera-Rodriguez, Daniel DeAlcala, Aythami Morales, Ruben Tolosana, Oscar Delgado, Alvaro Ortigosa, Javier Ortega-Garcia
arXiv:2606. 03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text.
By Luis Palacios, Lorenzo Basile, Diego Doimo, Alberto Cazzaniga
Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents. Although Vision-Language Models (VLMs) have shown remarkable performance in text-vision tasks, their robustness and transferability to different document domains remains underexplored.