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Using a fruit fly's brain connectome, a detailed neural map, researchers and hobbyists have created AI applications like generating story ideas and trading stocks, showcasing innovative and humorous uses of biological intelligence.
This paper presents an unsupervised framework for identifying and characterizing competing narratives in political discourse on social media, focusing on German politicians' tweets and using NLP techniques to analyze divergent viewpoints.
This paper evaluates multilingual LLMs in Urdu story generation, revealing significant linguistic, semantic, and cultural weaknesses, which highlights limitations for low-resource languages.
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.