Hybrid Reasoning

Hybrid reasoning integrates diverse reasoning methods, such as symbolic logic and large language models (LLMs), to enhance complex problem-solving capabilities beyond those of individual approaches. Current research focuses on developing frameworks that combine fast, intuitive reasoning with slower, more deliberate methods, often employing dynamic workflows to decompose complex tasks and leveraging techniques like reinforcement learning and program-based prompting to improve accuracy and explainability. This interdisciplinary field holds significant promise for advancing artificial intelligence in areas like autonomous driving, question answering, and biomedical applications by enabling more robust, transparent, and efficient reasoning systems.