Learning Search

Learning to search (L2S) focuses on developing algorithms that efficiently explore search spaces, particularly for complex problems like routing or information retrieval. Current research emphasizes integrating large language models (LLMs) and multimodal models with search engines, improving personalization, and addressing challenges like handling infeasible solutions and mitigating biases in retrieval. This field is significant because it promises more efficient and effective search across diverse domains, impacting areas from e-commerce and scientific data analysis to autonomous systems and network security.