AI RESEARCH
Document-as-Image Representations Fall Short for Scientific Retrieval
arXiv CS.CL
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ArXi:2604.18508v1 Announce Type: cross Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying source. Meanwhile, existing benchmarks for scientific document retrieval, such as ArXivQA and ViDoRe, treat documents as images of pages, implicitly favoring such representations. In this work, we argue that this paradigm is not well-suited for text-rich multimodal scientific documents, where critical evidence is distributed across structured sources, including text, tables, and figures.