SOURCE-LINKED INTELLIGENCE

Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Visual Retrieval-Augmented Generation (VRAG) empowers models to navigate and answer queries about visually rich documents by retrieving relevant page images as visual evidence and reasoning over their content. However, effectively utilizing this visual evidence is usually impeded by two main challenges. First, answer-relevant evidence is sparse and may be concentrated in a small region of one page or dispersed across multiple pages. Second, existing agentic methods often generate answers based on raw exploration trajectories or compressed textual memories rather than an explicitly organized se

Read original source ↗ Open in workspace

recordType

paper

region

Global