无需OCR，通过主动检索实现多页文档的精准问答
Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA
- 从缩略图开始，逐步主动检索关键页面进行推理
- 在5个基准上超越开源模型，跨域性能提升47.9%
- 适合需要高效处理长篇文档的智能问答系统
多页文档视觉问答需对语义、版式和视觉元素进行综合推理。现有无OCR方法在模型容量与精度间存在权衡：端到端模型随文档长度扩展性差，基于视觉检索的流程则脆弱且被动。我们提出Doc-V*，一种无OCR的智能体框架，将多页文档问答转化为证据逐级聚合过程。该框架先通过缩略图概览，再利用语义检索主动导航并定向获取页面，将证据存入结构化工作内存以实现可解释推理。通过模仿专家轨迹训练，并用组相对策略优化进一步优化，实现了答案准确率与证据获取效率的平衡。在五个基准测试中，其表现优于开源基线，接近专有模型，跨域性能相比RAG基线最高提升47.9%。结果还表明，有效证据聚合依赖选择性注意力，而非增加输入页数。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。