ArXivDoc LaTeX-Native Scientific Document Retrieval
Figure 1: Comparison of document representation paradigms for scientific retrieval. Standard document-as-image approaches process rendered pages through a vision encoder, which handles figure-based queries but struggles to accurately ground text- and table-based evidence. In contrast, ArXivDoc, leverages underlying LaTeX source files to process text and figures natively. This preserves fine-grained document structure, enabling robust retrieval across all multimodal query types.
Paper context
Paper title: Document-as-Image Representations Fall Short for Scientific Retrieval Abstract: 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 representations, LaTeX provides direct access to structured elements (e.g., sections, tables, figures, equations), enabling controlled query construction grounded in specific evidence types. We systematically compare text-only, image-based, and multimodal representations across both single-vector and multi-vector retrieval models. Our results show that: (1) document-as-image representations are consistently suboptimal, especially as document length increases; (2) text-based representations are most effective, even for figure-based queries, by leveraging captions and surrounding context; and (3) interleaved text+image representations outperform document-as-image approaches without requiring specialized training. Passages referencing this figure: val models. Our results show that: (1) document-as-image representations are consistently suboptimal, especially as document length increases; (2) text-based representations are most effective, even for figure-based queries, by leveraging captions and surrounding context; and (3) interleaved text+image representations outperform document-as-image approaches without requiring specialized training. Figure 1: Comparison of document representation paradigms for scientific retrieval. Standard document-as-image approaches process rendered pages through a vision encoder, which handles figure-based queries but struggles to accurately ground text- and table-based evidence. In contrast, ArXivDoc, leverages underlying LaTeX source files to process text and figures natively. This preserves fine-graine
