Formula Driven

Formula-driven research encompasses the automated processing and understanding of mathematical formulas, aiming to improve accuracy, efficiency, and fairness in tasks like formula recognition, reasoning, and generation. Current research focuses on developing novel neural network architectures, such as hierarchical networks and graph neural networks, and improved evaluation metrics that address biases in existing methods. These advancements have implications for various fields, including automated theorem proving, scientific literature analysis, and the development of more intelligent spreadsheet software, by enabling more robust and efficient handling of mathematical information.