LLMs trained with simple rewards often latch onto just a few ways of solving problems and stop exploring, which hurts their ability to find other correct answers.
This paper builds a medical image segmentation system that uses both pictures (like X-rays) and words (short clinical text) at the same time.
Modern AI models can get very good at being correct, but in the process they often lose their ability to think in many different ways.
This paper shows a simple, math-guided way to turn image pieces into tidy symbols (tokens) using points spread evenly on a sphere.