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.
99papers
Papers
February 25, 2025
BoxRL-NNV: Boxed Refinement of Latin Hypercube Samples for Neural Network Verification
Sarthak DasHRR: Hierarchical Retrospection Refinement for Generated Image Detection
Peipei Yuan, Zijing Xie, Shuo Ye, Hong Chen, Yulong WangJianghan University●Huazhong University of Science and Technology●Huazhong Agricultural University●Engineering Research Center of Intelligent...+1AIR: Complex Instruction Generation via Automatic Iterative Refinement
Wei Liu, Yancheng He, Hui Huang, Chengwei Hu, Jiaheng Liu, Shilong Li, Wenbo Su, Bo ZhengAlibaba Group
February 8, 2025
February 5, 2025
Reflection-Window Decoding: Text Generation with Selective Refinement
Zeyu Tang, Zhenhao Chen, Loka Li, Xiangchen Song, Yunlong Deng, Yifan Shen, Guangyi Chen, Peter Spirtes, Kun ZhangPolicy Abstraction and Nash Refinement in Tree-Exploiting PSRO
Christine Konicki, Mithun Chakraborty, Michael P. Wellman