Researchers have developed a neural-symbolic framework called FormalAnalyticGeo to generate multimodal analytic geometry problems. The framework uses a formal language to bridge problem text with precise diagram rendering and includes four specialized components: a generator, formalizer, measurer, and quality verifier. This system eliminates the need for human annotation and has produced a dataset of over 7K verified problems. Experiments show that generated problems are accurate, with a median ground-truth relative error of 0.70%. --- Why it matters: This framework matters to researchers in AI because it addresses a gap in multimodal learning by providing a large-scale dataset of annotated analytic geometry problems, which can be used to improve the performance of Multimodal Large Language Models (MLLMs). Source: https://arxiv.org/abs/2607.12982