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Research on "One-to-many" problems focuses on efficiently generating diverse outputs from a single input, addressing challenges in data sparsity and computational cost. Current efforts involve developing novel model architectures, such as diffusion models and transformers, often incorporating techniques like self-supervised learning, knowledge distillation, and multi-view fusion to improve accuracy and generalization. These advancements have significant implications for various fields, including computer vision (3D reconstruction, pose estimation), natural language processing (entailment graph construction), and recommender systems, by enabling more robust and efficient solutions to complex tasks.