Modular Framework

Modular frameworks are increasingly used to design and implement complex systems across diverse scientific domains, aiming to enhance scalability, flexibility, and reproducibility. Current research focuses on developing modular architectures for various applications, including neurosymbolic learning (leveraging vectorized computations and PyTorch), robotics (integrating planning, control, and simulation), and large language model evaluation (incorporating diverse methodologies and ensuring reliability). This approach facilitates easier integration of components, allows for rapid prototyping and experimentation, and ultimately accelerates scientific discovery and technological advancement in various fields.