Texts are widely used in aphasia rehabilitation to support the recovery of comprehension and narrative planning. In routine practice, clinical impact depends strongly on patient motivation and on the availability of age-appropriate reading materials: adults are often offered child-oriented texts, which can be perceived as demeaning and may reduce engagement. We present a controllable generation pipeline for building a repository of Russian therapeutic reading texts for adult aphasia therapy. An anonymized repository with code and data is available at https://github.com/z00logist/aphasia-exercises-generation. The pipeline conditions each story on an explicit semantic triplet (12 topics-10 archetypes-11 objects) and enforces three clinically motivated complexity regimes (Basic/Intermediate/Advanced). Using batched prompting, we generate 1,296 unique stories. We evaluate the corpus with classical linguistic metrics and a LLM-as-a-judge protocol (18 binary criteria); on a stratified sample of 198 stories, overall rubric compliance is 80.0%. Surface metrics show a monotonic increase in lexical and syntactic complexity across regimes, and Basic texts closely match a small clinical anchor set of 10 therapist-authored texts. Judge-based analysis indicates near-perfect adherence to high-level narrative constraints but persistent limitations in fine-grained phonotactic control, motivating hybrid neuro-symbolic enforcement.

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.