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arXiv · 2511.11552

DocLens : A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding

Dawei Zhu·Rui Meng·Jiefeng Chen·Sujian Li·Tomas Pfister·Jinsung Yoon
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Abstract

Comprehending long visual documents, where information is distributed across extensive pages of text and visual elements, is a critical but challenging task for modern Vision-Language Models (VLMs). Existing approaches falter on a fundamental challenge: evidence localization. They struggle to retrieve relevant pages and overlook fine-grained details within visual elements, leading to limited performance and model hallucination. To address this, we propose DocLens, a tool-augmented multi-agent framework that effectively ``zooms in'' on evidence like a lens. It first navigates from the full document to specific visual elements on relevant pages, then employs a sampling-adjudication mechanism to generate a single, reliable answer. Paired with Gemini-2.5-Pro, DocLens achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, surpassing even human experts. The framework's superiority is particularly evident on vision-centric and unanswerable queries, demonstrating the power of its enhanced localization capabilities.

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Dawei Zhu, Rui Meng, Jiefeng Chen, Sujian Li, Tomas Pfister, Jinsung Yoon. 2025-11-14. DocLens : A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding. https://arxiv.org/abs/2511.11552

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