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Introduces StorySpark, a module-wise evolutionary search framework for LLM-based story premise generation, demonstrating consistent gains in originality and downstream story quality.
Introduces Magnet, a multi-agent goal-driven narrative engine for long-form story generation with persona-grounded characters, and Atlas, a graph-based pipeline for detecting hallucinations in generated narratives. The framework improves coherence and reduces hallucinations compared to single-model baselines and IBSEN.
This paper investigates whether large language models generate diverse stories. Using narrative similarity analysis, the authors find that LLM-generated narratives are consistently more similar to each other than human-written stories, and that common mitigation strategies like negative prompting and temperature scaling fail to address this homogeneity.
This paper introduces the Proverb Aligned Narrative Dataset (PAND) for studying proverb-conditioned story generation in LLMs, finding a 'decompression gap' where models produce fluent stories but fail to faithfully capture the proverbs' moral and causal structure.
This paper diagnoses the low diversity in LLM-generated stories, finding that 88.3% of sampled stories contain one of 11 common words (e.g., Elias, lighthouse) across models, and traces this homogeneity to post-training data and alignment rather than prevalence in pre-training data.
A hierarchical multi-agent framework generates short dramas from single sentences by enforcing narrative pacing, ensuring spatial consistency, and implementing quality control through iterative refinement and reviewer loops. It introduces a new benchmark, Short-Drama-Bench, for evaluation.
This paper introduces NARRA-Gym, a benchmark and executable evaluation environment for assessing Large Language Models' abilities in sustaining interactive narratives, managing memory, and adapting to users over multiple turns.
A local MCP server lets LLMs browse Project Gutenberg books offline to improve creative writing.