The paper introduces a framework that learns readerly dynamics—how readers respond over time—from high‑quality human stories and uses these dynamics to steer large language model (LLM) story generation. Instead of modeling full story content, a small LLM predicts low‑dimensional trajectories of future reader responses, which a larger, instruction‑following writer LLM then follows to produce story segments. Experiments show the approach improves trajectory‑based fidelity to human stories by 12%–35% while largely maintaining coherence.
arXiv:2605. 17064v2 Announce Type: replace Abstract: Large language models are optimized for instruction following and agentic tasks remain poorly aligned with the requirements of high-quality creative writing.
arXiv:2610.06729v2 Announce Type: replace Abstract: Large language models (LLMs) can generate accurate responses, but these are void of diversity. We attempt to address this for the task of creative...
arXiv:2608. 15654v1 Announce Type: cross Abstract: Large language models can write fluent stories, but open-ended storytelling requires more than local fluency.
arXiv:2606. 17350v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested.
The paper proposes a hybrid narrative framework that blends pre‑authored content with real‑time LLM generation, using semantic similarity to balance authorial control and player agency. It also introduces a self‑evolving, feedback‑driven optimization system that refines prompts through in‑context learning, removing the need for manual prompt engineering. A case study and participant evaluation demonstrate that this approach improves narrative coherence while maintaining player agency and aligning with authorial intent.
arXiv:2608. 12336v1 Announce Type: cross Abstract: A story premise is the creative spark from which a full narrative can grow.
arXiv:2610.32002v1 Announce Type: new Abstract: The rapid growth of microdramas has increased demand for high-quality script production. Long-form microdrama script generation with LLMs requires both...
The paper introduces a reinforcement learning method to improve the quality of feedback generated by large language models for creative writers. By training with group relative policy optimization and a multi‑component reward that emphasizes tailored, actionable, and critical‑issue‑focused feedback, the authors demonstrate that their approach outperforms existing LLMs and baselines on three story corpora. The study shows that actionable suggestions are the key factor driving constructive feedback.
The paper investigates how large language models (LLMs) engage with long-form narratives by comparing their generated novel summaries to human-authored ones. Researchers align sentences from 150 human-written summaries to specific chapters, highlighting the challenge of this alignment task and the complexity of summarization. They find stylistic differences and that LLMs tend to focus more on the ends of texts, suggesting insights into why models may struggle with narrative comprehension.
arXiv:2601. 17226v2 Announce Type: replace-cross Abstract: Counterfactual story retelling exposes LLM shortcomings in constrained narrative solution spaces where they can no longer rely on recalling memorised training data.
The paper investigates how Large Language Models (LLMs) construct fictional worlds, specifically examining setting as a measurable aspect of storyworld creation. By generating 1,000 AI stories per model in English and German and comparing them to human-authored fiction from Project Gutenberg, the authors classify narrative space into five categories—action, perceived, visual, descriptive, and no space—using fine‑tuned BERT classifiers. Results show that human texts mainly use action space, grounding narratives in character-environment interaction, while LLMs consistently overproduce perceived space, focusing on atmosphere and affect, with this pattern varying by model and language.
arXiv:2607. 00009v1 Announce Type: cross Abstract: Despite the remarkable proficiency of large language models (LLMs) in basic writing assistance, their utility in creative writing is fundamentally hindered by a persistent binary failure.