New Framework Guides Language Models Through Symbolic Perception and Logical Deduction in Geometry

Researchers have posted an arXiv paper presenting a framework that helps language models solve plane geometry problems by separating visual perception of geometric symbols from logical deduction. The approach aims to reduce reliance on computationally heavy large multimodal models by using symbolic representations to guide text-only reasoning. The work addresses a long-standing AI challenge that requires combining perception with rigorous mathematical reasoning.