Multimodal Retrieval

TL;DR for operators An AI research assistant’s accuracy cannot be reliably predicted by selecting the strongest language model, connecting a powerful search service, and adding specialized agents. Those components must work together. In HyperBrowseComp1, replacing native search with Exa improved GPT-5.6 Sol accuracy from 19.15% to 26.71%, while reducing Gemini 3.7 Flash accuracy from 31.68% to 22.22%. A separate multi-agent configuration completed only 330 of 423 attempts and answered 17.73% correctly overall. ...

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. ...