From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
arXiv:2607.00918v1 Announce Type: new 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. In this work, we introduce a unified framework for long-form narrative generation and verification. MAGNET, a multi-agent goal-driven narrative engine for storytelling, generates stories with persona-grounded character agents that propose actions based on a shared world state and evolving story goals, while ATLAS is a graph-based pipeline that compares scene-l
LLMs continue to advance, necessitating more sophisticated methods for long-form creative consistency and coherence, which has been a persistent challenge.
This development represents a significant step towards enabling AI to generate complex, coherent narratives, impacting creative industries and AI agent development.
AI-generated long-form content can now better maintain character consistency and plot coherence, moving beyond short-form or episodic generation.
- · Entertainment industry
- · Content creators
- · AI research labs
- · Generative AI platforms
- · Human writers of formulaic content
AI models will be able to produce more compelling and lengthy narratives with less human intervention.
The cost and time associated with generating high-quality long-form content will decrease significantly, democratizing content creation.
This could lead to a proliferation of AI-generated stories and media, blurring the lines between human and artificial creativity and potentially altering traditional content consumption patterns.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL