Neurosymbolic Approach

Neurosymbolic AI integrates the strengths of neural networks and symbolic reasoning to build more robust, reliable, and explainable AI systems. Current research focuses on enhancing large language models with symbolic knowledge representations and logical reasoning capabilities, often using architectures that combine neural networks with formal logic systems or domain-specific languages to improve accuracy and consistency in tasks like question answering, knowledge graph reasoning, and program synthesis. This approach addresses limitations of purely neural methods by incorporating structured knowledge and enabling more transparent and verifiable decision-making, with applications ranging from financial analysis to robotic control and biomedical recommendations.