PixelRAG beats text parsers on accuracy and cuts AI agent token costs 10x

Most enterprise RAG pipelines start the same way: a text parser converts web pages and documents into plain text so they can be chunked and indexed for retrieval. That conversion step destroys retrieval signals — and according to new research, it's responsible for the majority of wrong answers.A research team from UC Berkeley, Princeton University, EPFL and Databricks published a paper this week introducing PixelRAG, a system that skips that conversion entirely. Instead of parsing pages into text, PixelRAG renders them as screenshots, indexes those images and feeds retrieved tiles directly to a vision-language model reader. Tested across 30 million screenshot tiles covering all of Wikipedia, it outperforms text-based RAG across six benchmarks, improving accuracy by up to 18.1% over text-ba