The field of georeferencing and geometry problem solving is experiencing significant advancements with the integration of deep learning techniques. Recent developments have focused on leveraging large multi-modal models to improve the accuracy and efficiency of georeferencing complex locality descriptions. Additionally, neural representations such as Einstein Fields are being explored for compressing computationally intensive simulations in general relativity. The application of deep learning methods to geometry problem solving is also gaining traction, with surveys highlighting the potential of multimodal large language models. Noteworthy papers in this area include:

• A novel method that utilizes large multi-modal models to georeference complex locality descriptions with impressive results.
• Einstein Fields, a neural representation designed to compress computationally intensive four-dimensional numerical relativity simulations into compact implicit neural network weights.
• A comprehensive survey of deep learning for geometry problem solving, providing a thorough review of related methods and evaluation metrics.
• A systematic literature review of transformer-based spatial grounding approaches, identifying dominant model architectures and prevalent datasets.