MUSE is a story‑generation engine that applies Robert McKee’s narrative theory to guide decisions about plot, character, and language throughout planning, drafting, and revision. It structures story knowledge into rule atoms, semantic consolidations, and mechanisms, and uses intermediate deliverables to preserve decisions across creative stages. Experiments show MUSE improves benchmark scores over zero‑shot generation and maintains low consistency errors across multiple models.
SAGE is a framework that automates storyboard creation by learning and evolving directing rules from expert demonstrations. It attributes each narrative group’s decisions to specific rules, refines those rules with localized feedback, and routes only relevant rules to each group during generation. In tests, SAGE matched professional directors on a rubric and reduced authoring time by over 83%.
arXiv:2609.38406v1 Announce Type: new Abstract: Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct mi...
arXiv:2608. 15654v1 Announce Type: cross Abstract: Large language models can write fluent stories, but open-ended storytelling requires more than local fluency.
arXiv:2609.39333v1 Announce Type: cross Abstract: Autonomous AI agents can turn authors' goals into interactive narratives by independently organizing and carrying out generation and revision. As age...
ConWriter is a training‑free framework that generates long‑form stories scene by scene, using static requirements, dynamic memory, symbolic state reasoning, and uncertainty‑aware risk signals to enforce consistency. It checks each new scene against required narrative transitions and repairs local errors before they propagate. Evaluations on ConStory‑Bench show that ConWriter matches or outperforms direct generation and a recent baseline, improving narrative consistency across multiple models and story lengths.
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.
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:2606. 17391v1 Announce Type: cross Abstract: Long-form serialized audio drama, with arcs that run for 200 to 800 episodes, is a major creative medium and a setting where frontier large language models (LLMs) fail.
arXiv:2606. 05724v1 Announce Type: cross Abstract: Long-form narrative QA requires reasoning over evolving story worlds rather than isolated passages: answers may depend on earlier goals, changing character states, social relations, causal triggers, temporal position, and later consequences.
arXiv:2608. 12336v1 Announce Type: cross Abstract: A story premise is the creative spark from which a full narrative can grow.
arXiv:2607. 00918v1 Announce Type: cross Abstract: Although large language models (LLMs) have demonstrated impressive creative fiction generation, they struggle to maintain narrative consistency and coherent plot lines in long-form stories.
arXiv:2602. 15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks.