Year: 2026
Type: conference-paper
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 toolaugmented 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 stateof-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.
Authors: Dawei Zhu, Rui Meng, Jiefeng Chen, Sujian Li, Tomas Pfister, Jinsung Yoon
Institution: Peking University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416331536