AI model trick: running text twice lets it see the whole sentence instead of just what came before

Researchers found a way to make language models process text more efficiently for identifying named entities — the technical task of tagging which words in a sentence refer to people, places, or organizations. By running the same text through the model twice, the second pass can see the entire sentence instead of just prior context, making it 20 times faster than current methods while improving accuracy by 7.9 percentage points.

Language models normally work like reading left-to-right, one word at a time, which means they sometimes miss clues that appear later in the sentence. This trick solves that without requiring companies to build or buy new models — it's a software-only change that works on models already deployed. The speed gain matters because entity recognition is a foundational task for real applications: it powers search indexing, document processing, and customer data extraction, and every millisecond of latency compounds at scale.