MUSE: A Theory-Harnessed Story Engine for Vibe Narrativizing

arXiv:2609.15188 · cs.CL · Submitted 2026-09-14 · Read on arXiv

cs.CL

Submitted: 2026-09-14

Updated: 2026-09-17

Comments: 52 pages, including appendices; 3 figures. Revised exposition throughout the paper and appendices. Code: https://github.com/RoadtoAGI/MUSE

Code: https://github.com/RoadtoAGI/MUSE

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Consistency — 20× in this paper · explained in 3 episodes
• Ablation — 15× in this paper · explained in 1 episode
• Trace — 12× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Abstraction — 9× in this paper · explained in 1 episode
• Distillation — 8× in this paper · explained in 2 episodes
• Agent Harness — 6× in this paper · explained in 2 episodes
• Core — 6× in this paper · explained in 2 episodes
• Cooperation — 4× in this paper · explained in 2 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Fidelity — 3× in this paper · explained in 2 episodes
• Archetype — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• RAG — 2× in this paper · explained in 3 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes

Sources

• Agents' Room: Narrative Generation through Multi-step Collaboration
• HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing
• Towards Enhanced Immersion and Agency for LLM-based Interactive Drama
• WritingBench: A Comprehensive Benchmark for Generative Writing
• What Matters in Evaluating Book-Length Stories? A Systematic Study of Long Story Evaluation
• Lost in Stories: Consistency Bugs in Long Story Generation by LLMs
• Event Representations for Automated Story Generation with Deep Neural Nets
• Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement
• StoryWriter: A Multi-Agent Framework for Long Story Generation
• SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models
• Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models
• RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
• CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models
• BookWorld: From Novels to Interactive Agent Societies for Creative Story Generation
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
• Dense Passage Retrieval for Open-Domain Question Answering
• Learning To Retrieve Prompts for In-Context Learning
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• Improving Pacing in Long-Form Story Planning
• Same Author or Just Same Topic? Towards Content-Independent Style Representations

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