First Order

First-order logic (FOL) is a foundational framework for representing knowledge and reasoning, currently experiencing renewed interest due to its integration with machine learning. Research focuses on leveraging FOL's expressive power within various applications, including improving large language model reasoning, optimizing neural network training, and enhancing knowledge graph querying through techniques like neuro-symbolic architectures and novel optimization algorithms (e.g., Adam variants, zeroth-order methods). These advancements are significant because they enable more robust, explainable, and efficient solutions to complex problems across diverse fields, from automated theorem proving to reinforcement learning and beyond.