The field of e-commerce search is moving towards more sophisticated and nuanced approaches to understanding user intent and behavior. Recent developments focus on improving the accuracy and relevance of search results by incorporating large language models, probabilistic graded relevance distillation, and reinforcement learning. These innovations enable more effective query reformulation, document retrieval coherence, and query-relevant document summaries. Notable papers include: AI Guided Accelerator For Search Experience, which proposes a novel framework for modeling transitional queries and applies generative Large Language Models for scalable query expansion. BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation, which introduces a simple and effective pointwise training method that optimizes binary cross-entropy over LLM-generated graded relevance scores. Generating Query-Relevant Document Summaries via Reinforcement Learning, which proposes a novel reinforcement learning framework designed to generate concise, query-relevant summaries of product descriptions optimized for search relevance.