This article discusses a new method for understanding user intent by breaking down interactions on mobile devices into two stages. By summarizing individual screens and then extracting intent from those summaries, small models can achieve results similar to larger models without needing server processing. The approach improves efficiency and maintains user privacy.

Dots.ocr, a new 3B parameter OCR model from RedNote, enables competitive on-device optical character recognition, leveraging Apple's Neural Engine for efficiency. The article outlines the challenges and processes involved in converting the model from PyTorch to Core ML, detailing the steps taken to optimize its performance for on-device use. Future parts of the series will focus on further integration and optimization strategies.