ViSAR让文档问答系统更聪明，能根据问题复杂度自动调整检索页面数，速度提升近六成还不影响准确率。
ViSAR是一种针对视觉文档问答的检索增强生成方法，通过构建查询条件化的页面级相似矩阵，动态确定检索页面数量。该方法在多个编码器和大型视觉语言模型上测试，可将RAG延迟降低58.7%，同时保持或提高答案准确性。研究还发现相似矩阵结构与答案准确性相关，为检索质量感知的文档理解提供新方向。
ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering
Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large Vision-Language Model (LVLM). Existing approaches typically retrieve a fixed top-$k$ number of pages regardless of query complexity, which increases LVLM latency and may degrade answer accuracy. We introduce ViSAR (Visual Semantic Activation Retrieval), a training-free adaptive-$k$ retrieval method for late-interaction visual document retrieval. ViSAR operates directly in the embedding space to construct a query-conditioned page-level similarity matrix that highlights query-relevant semantics and dynamically determines the number of pages to retrieve. Across multiple encoders and LVLMs, ViSAR retrieves compact, query-adapted page sets that reduce RAG latency by up to 58.7\%, while maintaining or improving answer accuracy compared with fixed top-$k$ and adaptive retrieval heuristics. Furthermore, we show that the similarity matrix structure correlates with answer accuracy, suggesting future directions for retrieval quality-aware document understanding.