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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.
Researchers introduce BIASEDTALES-ML, a large-scale multilingual dataset of ~350,000 LLM-generated children's stories across eight languages, designed to analyze narrative attribute distributions and cross-lingual bias patterns in language model outputs. The work reveals significant cross-lingual variability, highlighting limitations of English-centric bias evaluations.
This paper proposes CAP-TTA, a test-time adaptation framework that uses preconditioned LoRA updates triggered by bias-risk scores to mitigate toxicity and bias in large language models during narrative generation, achieving faster optimization and better fluency than standard baselines.
ArcDeck is a multi-agent framework that generates presentation slides from academic papers by modeling logical flow through discourse trees and iterative agent refinement, outperforming direct summarization methods. The paper introduces ArcBench, a new benchmark for evaluating paper-to-slide generation with emphasis on narrative coherence and logical structure.