Documents spanning tens to hundreds of pages are pervasive in real-world workflows, from scientific papers to financial reports and technical manuals. In this setting, answer-relevant evidence is sparse and distributed across pages, and the document itself often exceeds a backbone MLLM’s context window, making MP-VRDU a distinct problem from single-page VRDU. We survey MP-VRDU through the lens of evidence management, the organising principle that distinguishes multi-page from the single-page setting: the problem of deciding how document-scale evidence is selected and reasoned over when it cannot all be held at once. We introduce a taxonomy spanning four architectural families, multimodal representation, inference-time adaptation, and training strategies for cross-page evidence. We further consolidate MP-VRDU datasets and outline open challenges in efficiency, faithfulness, evidence pipelines, supervision integrity, and reproducible evaluation that define the frontier.