AI RESEARCH
RobustVisRAG: Causality-Aware Vision-Based Retrieval-Augmented Generation under Visual Degradations
arXiv CS.CV
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ArXi:2602.22013v3 Announce Type: replace Vision-based Retrieval-Augmented Generation (VisRAG) leverages vision-language models (VLMs) to jointly retrieve relevant visual documents and generate grounded answers based on multimodal evidence. However, existing VisRAG models degrade in performance when visual inputs suffer from distortions such as blur, noise, low light, or shadow, where semantic and degradation factors become entangled within pretrained visual encoders, leading to errors in both retrieval and generation stages. To address this limitation, we