Visual Text Compression as Measure Transport
The research investigates whether compressing text into images for AI models is useful by using a transport-theoretic framework. It moves beyond simple compression ratios to create a score that predicts when visual encoding helps or hurts performance. The outcome is a method to decide when to trust the visual path versus the text path, leading to better accuracy and token efficiency across different tasks.