Compositional Structure

Compositional structure research investigates how artificial intelligence models can learn to understand and generate complex concepts by combining simpler, learned components, mirroring human cognitive abilities. Current research focuses on improving the compositional generalization of various models, including vision-language models (VLMs), diffusion models, and transformers, often through techniques like contrastive learning, in-context learning, and the development of specialized loss functions and training datasets. This work is significant because improved compositional understanding is crucial for building more robust, adaptable, and human-like AI systems with applications ranging from image generation and natural language processing to scientific discovery and robotic control.