Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approaches rely on tailored parsing techniques that disregard layout information and are prone to errors, while recent parsing-free visual methods often struggle to capture fine-grained textual semantics in text-rich scenarios. To address these limitations, we propose \textbf{Unveil}, a novel visual-textual embedding framework that effectively integrates textual and visual features for robust document representation. Through knowledge distillation, we transfer the semantic understanding capabilities from the visual-textual embedding model to a purely visual model, enabling efficient parsing-free retrieval while preserving semantic fidelity. Experimental results demonstrate that our visual-textual embedding method surpasses existing approaches, while knowledge distillation successfully bridges the performance gap between visual-textual and visual-only methods, improving both retrieval accuracy and efficiency.
@article{arxiv.2605.24530,
title = {Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval},
author = {Hao Sun and Yingyan Hou and Jiayan Guo and Bo Wang and Chunyu Yang and Jinsong Ni and Yan Zhang},
journal= {arXiv preprint arXiv:2605.24530},
year = {2026}
}