The field of information retrieval is undergoing significant changes with the advent of generative search engines and large language models. Researchers are exploring new methods to improve query rewriting, particularly for long-tail queries, and to optimize web content for generative search engines. Studies have shown that reputable news websites and misinformation sites differ in their configuration of robots.txt files, which can impact the training data available to large language models. Additionally, there is a growing concern about the consumption of incomplete information on digital platforms and the need for awareness of information completeness. Noteworthy papers include: CardRewriter, which introduces a framework for long-tail query rewriting on short-video platforms, and AutoGEO, which proposes a framework for generative engine optimization. Assessing Web Search Credibility and Response Groundedness in Chat Assistants is also notable for its evaluation of chat assistants' web search behavior and source credibility.