Hybrid reasoning refers to the integration of different approaches to reasoning in artificial intelligence, such as combining symbolic reasoning (which uses explicit rules and logical structures) with neural networks (which recognize patterns in data through training). This combination leverages the strengths of both methodologies, making the system more robust and versatile in tackling complex problems by balancing clear, logical decisions with nuanced insights from large datasets[1].