Declarative Model

Declarative models represent a growing area of research aiming to improve the explainability, efficiency, and adaptability of AI systems, particularly large language models. Current work focuses on developing frameworks that allow AI agents to learn and reason using declarative knowledge, rather than solely relying on procedural instructions, often employing techniques like in-memory learning and the compilation of declarative language model calls into self-improving pipelines. This shift towards declarative approaches promises to enhance AI transparency, improve generalization capabilities, and facilitate the development of more robust and reliable AI systems across diverse applications, including disaster response and security analysis.