Iterative Refinement

Iterative refinement is a computational technique that improves results through successive iterations, leveraging feedback to correct errors and enhance accuracy. Current research focuses on applying this approach across diverse fields, employing various methods including diffusion models, neural networks (with architectures like transformers and residual networks), and multi-agent systems to refine predictions, generate high-quality outputs, and improve model robustness. This methodology is proving significant for advancing numerous applications, from improving large language model reasoning and code generation to enhancing medical image analysis and autonomous driving systems.