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Main Authors: Cheng, Yuyang, Cai, Linyue, Peng, Changwei, Xu, Yumiao, Bie, Rongfang, Zhao, Yong
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2509.26461
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author Cheng, Yuyang
Cai, Linyue
Peng, Changwei
Xu, Yumiao
Bie, Rongfang
Zhao, Yong
author_facet Cheng, Yuyang
Cai, Linyue
Peng, Changwei
Xu, Yumiao
Bie, Rongfang
Zhao, Yong
contents We present CreAgentive, an agent workflow driven multi-category creative generation engine that addresses four key limitations of contemporary large language models in writing stories, drama and other categories of creatives: restricted genre diversity, insufficient output length, weak narrative coherence, and inability to enforce complex structural constructs. At its core, CreAgentive employs a Story Prototype, which is a genre-agnostic, knowledge graph-based narrative representation that decouples story logic from stylistic realization by encoding characters, events, and environments as semantic triples. CreAgentive engages a three-stage agent workflow that comprises: an Initialization Stage that constructs a user-specified narrative skeleton; a Generation Stage in which long- and short-term objectives guide multi-agent dialogues to instantiate the Story Prototype; a Writing Stage that leverages this prototype to produce multi-genre text with advanced structures such as retrospection and foreshadowing. This architecture reduces storage redundancy and overcomes the typical bottlenecks of long-form generation. In extensive experiments, CreAgentive generates thousands of chapters with stable quality and low cost (less than $1 per 100 chapters) using a general-purpose backbone model. To evaluate performance, we define a two-dimensional framework with 10 narrative indicators measuring both quality and length. Results show that CreAgentive consistently outperforms strong baselines and achieves robust performance across diverse genres, approaching the quality of human-authored novels.
format Preprint
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institution arXiv
publishDate 2025
record_format arxiv
spellingShingle CreAgentive: An Agent Workflow Driven Multi-Category Creative Generation Engine
Cheng, Yuyang
Cai, Linyue
Peng, Changwei
Xu, Yumiao
Bie, Rongfang
Zhao, Yong
Computation and Language
We present CreAgentive, an agent workflow driven multi-category creative generation engine that addresses four key limitations of contemporary large language models in writing stories, drama and other categories of creatives: restricted genre diversity, insufficient output length, weak narrative coherence, and inability to enforce complex structural constructs. At its core, CreAgentive employs a Story Prototype, which is a genre-agnostic, knowledge graph-based narrative representation that decouples story logic from stylistic realization by encoding characters, events, and environments as semantic triples. CreAgentive engages a three-stage agent workflow that comprises: an Initialization Stage that constructs a user-specified narrative skeleton; a Generation Stage in which long- and short-term objectives guide multi-agent dialogues to instantiate the Story Prototype; a Writing Stage that leverages this prototype to produce multi-genre text with advanced structures such as retrospection and foreshadowing. This architecture reduces storage redundancy and overcomes the typical bottlenecks of long-form generation. In extensive experiments, CreAgentive generates thousands of chapters with stable quality and low cost (less than $1 per 100 chapters) using a general-purpose backbone model. To evaluate performance, we define a two-dimensional framework with 10 narrative indicators measuring both quality and length. Results show that CreAgentive consistently outperforms strong baselines and achieves robust performance across diverse genres, approaching the quality of human-authored novels.
title CreAgentive: An Agent Workflow Driven Multi-Category Creative Generation Engine
topic Computation and Language
url https://arxiv.org/abs/2509.26461