Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents. Existing OCR-free methods face a trade-off between capacity and precision: end-to-end models scale poorly with document length, while visual retrieval-based pipelines are brittle and passive. We propose Doc-V∗, an \textbf{OCR-free agentic} framework that casts multi-page DocVQA as sequential evidence aggregation. Doc-V∗ begins with a thumbnail overview, then actively navigates via semantic retrieval and targeted page fetching, and aggregates evidence in a structured working memory for grounded reasoning. Trained by imitation learning from expert trajectories and further optimized with Group Relative Policy Optimization, Doc-V∗ balances answer accuracy with evidence-seeking efficiency. Across five benchmarks, Doc-V∗ outperforms open-source baselines and approaches proprietary models, improving out-of-domain performance by up to \textbf{47.9\%} over RAG baseline. Other results reveal effective evidence aggregation with selective attention, not increased input pages.
@article{arxiv.2604.13731,
title = {Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA},
author = {Yuanlei Zheng and Pei Fu and Hang Li and Ziyang Wang and Yuyi Zhang and Wenyu Ruan and Xiaojin Zhang and Zhongyu Wei and Zhenbo Luo and Jian Luan and Wei Chen and Xiang Bai},
journal= {arXiv preprint arXiv:2604.13731},
year = {2026}
}