AgentIR: 2nd Workshop on Agent-based Information Retrieval

Information retrieval (IR) systems are essential in modern society, aiding users to efficiently locate relevant information through query expansion, document retrieval, ranking, and re-ranking. User feedback from ranked outputs forms a dynamic interaction loop with IR systems, which can be modeled as either one-time or sequential decision-making problems. Over the past decade, deep reinforcement learning (DRL) has emerged as a promising approach to decision-making, leveraging the high model capacity of deep learning for complex tasks. While significant research has explored the application of DRL to IR tasks, several fundamental challenges remain underexplored, including the underlying information theory in DRL settings, the limitations of reinforcement learning methods for industrial IR applications, and the simulation of DRL-based IR systems. Concurrently, the advent of large language models (LLMs) has introduced new opportunities for optimizing and simulating IR systems. Building on the success of the Agent-based IR Workshop at SIGIR 2024, we propose hosting the second Agent-based IR Workshop at SIGIR 2025. This workshop will continue to provide a platform for researchers and practitioners from academia and industry to present cutting-edge advances in DRL-based and LLM-based IR systems from an agent-based perspective. By building on the foundation laid in the first workshop, the 2025 edition aims to delve deeper into emerging research challenges, foster collaborations, and explore innovative applications. Through engaging discussions and insightful presentations, the workshop seeks to further expand the boundaries of IR research and solidify its role as a premier venue for advancing agent-based IR systems.