This report describes the setup and results of the shared task of human-like long story generation, the LSG Challenge, which asks to generate a consistent, human-like long story (a Harry Potter fanfic in English for a general audience) given a prompt of about 1,000 tokens. We evaluated the submissions using both automated metrics and human evaluation protocols. The automated metrics, including the GAPELMAPER score, assessed the structuredness of the generated texts, while human annotators rated stories on dimensions such as relevance, consistency, fluency, and coherence. Additionally, annotators evaluated the models’ understanding of abstract concepts, causality, the logical order of events, and the avoidance of repeated plot elements. The results highlight the current strengths and limitations of state-of-the-art models in long-form story generation, with key challenges emerging in maintaining coherence over extended narratives and handling complex story dynamics. Our analysis provides insights into future directions for improving long story generation systems.