Jo Kristian Bergum

GPT-5 is shockingly good at search, and that changes the “BM25 as a baseline” story. Using GPT-5 search trajectories from BrowseComp-Plus, I’ll show how default BM25 parameters and evaluation harnesses can make lexical retrieval look weak, while real agent queries often play directly to BM25’s strengths.

Extracting information from complex document formats like PDFs usually involves a multi-step process, including text extraction, OCR, layout analysis, chunking, and embedding. This extraction process is resource-intensive, and the quality can vary, resulting in poor retrieval quality (garbage-in, garbage-out).

Neural search, often known by various names, including semantic search, has reached a stage where it is primarily associated with learned vector representations of queries and documents. This dense representational method reduces the scoring process to a vector similarity function.

Semantic search using AI-powered vector embeddings of text, where relevancy is measured using a vector similarity function, has been a hot topic for the last few years.