Story engine improves plot and character with theory based guidance
MUSE: A Theory-Harnessed Story Engine for Vibe Narrativizing
Computation and Language
Summary
Writing a good story needs careful choices about plot, characters, and language that work well together. The authors created a system called MUSE that uses a famous story theory to guide these choices throughout writing, from planning to editing. MUSE breaks down story advice into clear rules and helps keep these ideas consistent while creating the story. It was tested with several language models and improved story quality and consistency compared to writing without such guidance.
What this means in practice
- •For fiction writers: Use MUSE to get structured, theory-based guidance on plot and character choices during story writing and revision.
- •For game narrative designers: Incorporate MUSE’s story guidance and decision tracking to develop coherent character paths and scene compositions in interactive storytelling.
Authors
Jianxiang Ma, Xiaocui Yang, Daling Wang, Yuesong Hou, Mingfu Zhang, Yichen Gao, Junzhao Huang
Abstract
LLMs can generate fluent prose. Story quality depends on how decisions about plot, character, and language work together across planning, drafting, and revision. Guiding these decisions presents two bottlenecks: the quality of story guidance and its sustained use. We formulate Vibe Narrativizing as the task of turning natural-language writing requirements into a finished story and present MUSE, a Theory-Harnessed Story Engine. MUSE organizes story knowledge as guidance for specific decisions and carries those decisions into subsequent creative work. Knowledge engineering develops Robert McKee's story theory through rule atomization, semantic consolidation, and mechanism abstraction; a single source of truth and layered disclosure organize the resulting guidance. Typical examples complement principles that depend on context and aesthetic judgment. An agent harness organizes design, character performance, scene composition, and revision through intermediate deliverables that preserve story decisions. Context engineering supplies each role with the relevant guidance and decisions, while a masterwork corpus provides inspiration and prose references. A worked example follows one requested object from its thematic role to the characters' climactic actions. Across four base models, MUSE improves WritingBench by 1.6-4.8 points over zero-shot generation and raises LongStoryEval by more than ten points on three. ConStory-Bench consistency error density remains in the low single digits for all four models, below every reproduced story-system baseline on three. Component ablations locate the largest quality contribution in structural design, voice-specific effects in the character path, and further gains in revision. Code is available at https://github.com/RoadtoAGI/MUSE.