Document-as-Image Representations Fall Short for Scientific Retrieval
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. To study this setting, we introduce ArXivDoc, a new benchmark constructed from the underlying LaTeX sources of scientific papers. Unlike PDF or image-based ...
| Authors | Ghazal Khalighinejad, Raghuveer Thirukovalluru, Alexander H. Oh et al. |
|---|---|
| Primary category | cs.IR |
| Published | 20 April 2026 |
| Last updated at source | 13 October 2026 |
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