Extensible Framework

Extensible frameworks are software architectures designed to facilitate the development and adaptation of various computational models and tools, primarily by enabling easy integration of new algorithms, datasets, and functionalities. Current research emphasizes the creation of such frameworks for diverse applications, including federated learning, spiking neural networks, large language model evaluation, and reinforcement learning environments, often leveraging modular designs and standardized APIs. These frameworks are crucial for accelerating research progress and fostering collaboration by providing standardized platforms for benchmarking, experimentation, and the development of more robust and adaptable systems across various scientific and engineering domains. Their impact is seen in improved efficiency, reproducibility, and the ability to tackle increasingly complex problems.