Scientific-Search

TL;DR for operators A team building RAG over scientific PDFs has at least two separate decisions to make: how a page should be found, and what representation of that page the answering model should read. Those decisions do not receive the same answer. In the IRPAPERS benchmark,1 text and image retrieval recover partially different correct pages. Fusing their signals raises open-source retrieval to 49% Recall@1 and 95% Recall@20, above either modality alone. But once retrieved material is passed to GPT-4.1 for question answering, OCR text is substantially stronger: TextRAG scores 0.82 alignment at five retrieved pages versus 0.71 for ImageRAG. ...

TL;DR for operators Most enterprise RAG systems still behave like diligent interns with a search box: they retrieve a handful of plausible snippets, hand them to a language model, and hope the synthesis does not quietly forget half the question. That works for narrow Q&A. It fails when the user asks for a relationship chain, a complete list, or a decision-ready map of who did what, funded by whom, connected to which topic. ...