The Third Workshop on Long-Context Foundation Models

Foundation models have become a cornerstone in the advancement of artificial intelligence, widely used across both academic and practical applications. Across domains, challenging tasks require synthesizing information enormous amounts of data. These may take many forms, such as images, text, audio, and genomes. Much recent work has focused on developing long-context models capable of processing, understanding, and generating responses based on extensive inputs. However, complex tasks often require model to reason, plan, and interact with environments over extended horizons. This workshop will convene researchers to explore these challenges and foster developments in long-context foundation models. Key topics include new modeling architectures, training approaches, efficiency techniques, comprehensive evaluation methods, and applications in scientific fields such as genomics, climate science, scientific discovery, etc. Additionally, in this edition, special attention will be given to long-context reasoning and long-horizon agentic usages. By tackling these critical challenges, we aim to push the boundaries of long-context modeling and shape its future directions.