Workshop on Conversational Search for Complex Information Needs

We present a workshop proposal on \textit{Conversational AI and Search for Complex Information Needs}. Recent advances in conversational systems have expanded research beyond generative dialogue and factoid question-answering tasks, towards addressing complex information needs and improving user experience by providing adaptive answers to users based on style and personalization. In conversational search, user queries first go through intermediary steps (e.g., with agentic flows) before providing an answer that fits the user's expectations. As a result, research has shifted its focus to conversations that support exploratory, multi-step information needs, and to challenges such as long-form answer generation, personalization, the orchestration of agentics, and societal considerations, which remain largely open areas of research. This workshop is open to researchers and practitioners from the information retrieval, natural language processing, and human-computer interaction backgrounds to exchange results and prospective ideas around conversational AI by discussing the impacts on users and the need for adaptivity. The goal and expected outcome of the workshop is to bring together diverse perspectives, foster mutual learning and collaboration, and build better systems in the future. The program will combine poster presentations, interactive breakout sessions, and invited speakers.