CreAgentive: An Agent Workflow Driven Multi-Category Creative Generation Engine

• URL: http://arxiv.org/abs/2509.26461v1
• Date: Tue, 30 Sep 2025 16:12:32 GMT
• Title: CreAgentive: An Agent Workflow Driven Multi-Category Creative Generation Engine
• Authors: Yuyang Cheng, Linyue Cai, Changwei Peng, Yumiao Xu, Rongfang Bie, Yong Zhao,
• Abstract summary: CreAgentive addresses four key limitations of contemporary large language models in writing stories, drama and other categories of creatives.<n>At its core, CreAgentive employs a Story Prototype, which is a genre-agnostic, knowledge graph-based narrative representation.<n>In extensive experiments, CreAgentive generates thousands of chapters with stable quality and low cost.
• Score: 4.644735042881366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models [15.245564064908903]
  We propose a novel approach to long-form story generation, termed hybrid bottom-up long-form story generation.<n>In our method, agents interact within a dynamic sandbox environment, where their behaviors and interactions with one another and the environment generate emergent events.
  arXiv  Detail & Related papers  (2025-10-13T16:57:32Z)
• Long Story Generation via Knowledge Graph and Literary Theory [5.844556001202481]
  The generation of a long story consisting of several thousand words is a sub-task in the field of long text generation(LTG)<n>Previous research has addressed this challenge through outline-based generation, which employs a multi-stage method for generating outlines into stories.<n>This approach suffers from two common issues: almost inevitable theme drift caused by the loss of memory of previous outlines, and tedious plots with incoherent logic that are less appealing to human readers.
  arXiv  Detail & Related papers  (2025-08-05T06:35:14Z)
• StoryWriter: A Multi-Agent Framework for Long Story Generation [53.80343104003837]
  Long story generation remains a challenge for existing large language models.<n>We propose StoryWriter, a multi-agent story generation framework, which consists of three main modules.<n>StoryWriter significantly outperforms existing story generation baselines in both story quality and length.
  arXiv  Detail & Related papers  (2025-06-19T16:26:58Z)
• ViStoryBench: Comprehensive Benchmark Suite for Story Visualization [23.274981415638837]
  ViStoryBench is a comprehensive benchmark designed to evaluate story visualization models across diverse narrative structures, visual styles, and character settings.<n>The benchmark features richly annotated multi-shot scripts derived from curated stories spanning literature, film, and folklore.<n>To enable thorough evaluation, ViStoryBench introduces a set of automated metrics that assess character consistency, style similarity, prompt adherence, aesthetic quality, and generation artifacts.
  arXiv  Detail & Related papers  (2025-05-30T17:58:21Z)
• STORYANCHORS: Generating Consistent Multi-Scene Story Frames for Long-Form Narratives [82.19488717416351]
  This paper introduces StoryAnchors, a unified framework for generating high-quality, multi-scene story frames.<n>StoryAnchors employs a bidirectional story generator that integrates both past and future contexts to ensure temporal consistency.<n>It also integrates Multi-Event Story Frame Labeling and Progressive Story Frame Training, enabling the model to capture both overarching narrative flow and event-level dynamics.
  arXiv  Detail & Related papers  (2025-05-13T08:48:10Z)
• StoryAgent: Customized Storytelling Video Generation via Multi-Agent Collaboration [88.94832383850533]
  We propose a multi-agent framework designed for Customized Storytelling Video Generation (CSVG) StoryAgent decomposes CSVG into distinct subtasks assigned to specialized agents, mirroring the professional production process. Specifically, we introduce a customized Image-to-Video (I2V) method, LoRA-BE, to enhance intra-shot temporal consistency. Our contributions include the introduction of StoryAgent, a versatile framework for video generation tasks, and novel techniques for preserving protagonist consistency.
  arXiv  Detail & Related papers  (2024-11-07T18:00:33Z)
• Agents' Room: Narrative Generation through Multi-step Collaboration [54.98886593802834]
  We propose a generation framework inspired by narrative theory that decomposes narrative writing into subtasks tackled by specialized agents.<n>We show that Agents' Room generates stories preferred by expert evaluators over those produced by baseline systems.
  arXiv  Detail & Related papers  (2024-10-03T15:44:42Z)
• MovieDreamer: Hierarchical Generation for Coherent Long Visual Sequence [62.72540590546812]
  MovieDreamer is a novel hierarchical framework that integrates the strengths of autoregressive models with diffusion-based rendering. We present experiments across various movie genres, demonstrating that our approach achieves superior visual and narrative quality.
  arXiv  Detail & Related papers  (2024-07-23T17:17:05Z)
• Model Criticism for Long-Form Text Generation [113.13900836015122]
  We apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of generated text. We perform experiments on three representative aspects of high-level discourse -- coherence, coreference, and topicality. We find that transformer-based language models are able to capture topical structures but have a harder time maintaining structural coherence or modeling coreference.
  arXiv  Detail & Related papers  (2022-10-16T04:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.