Creative-Agent: A Creative Prototype Generation System Driven by Objectives and Key Results

In this study, we introduce the concept of creative agents, aiming to enhance the capabilities of large language models (LLMs) in tasks related to the generation of prototypes of creative works. Our approach utilizes both self-collaboration and self-correction mechanisms, facilitated by hierarchical agents, to address the inherent complexities of target tasks. Our key observations are two-fold: first, effective task solving demands in-depth domain knowledge and intricate reasoning, for which deploying specialized agents for individual sub-tasks can markedly enhance LLM performance. Second, task solving intrinsically adheres to a hierarchical execution structure, comprising both high-level strategic planning and detailed task execution. Towards this end, our Creative-Agent paradigm aligns closely with this hierarchical structure, promising enhanced efficacy and adaptability across a wide range of scenarios. Specifically, our framework includes two novel modules, i.e., hierarchical Objects and Key Results generation and multi-level evaluation, both contributing to more efficient and robust task-solving. In practice, hierarchical OKR generation decomposes objects into multiple sub-objects and assigns new agents based on key results and agent responsibilities. These agents subsequently elaborate on their designated tasks and may further decompose them as necessary. Such generation operates recursively and hierarchically, culminating in a comprehensive set of detailed solutions. The multi-level evaluation module of Creative-Agent refines the solution by leveraging feedback from all associated agents, optimizing each step of the process. This scheme ensures the solution is accurate, practical, and meets the requirements of intricate tasks, enhancing the overall reliability and quality of the outcome. Experimental results demonstrated the effectiveness of Creative-Agent in completing the task of creating prototype generation works.

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Publication Details

Published
2025-05-05
DOI
https://doi.org/10.1109/cscwd64889.2025.11033384
Primary Topic
Artificial Intelligence in Games
Type
article
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article

Creative-Agent: A Creative Prototype Generation System Driven by Objectives and Key Results

Kanle Shi, Chongyang Ma, X. P. Huang, Jingwei Chen et al.
Artificial Intelligence in Games
article

Creative-Agent: A Creative Prototype Generation System Driven by Objectives and Key Results

Kanle Shi, Chongyang Ma, X. P. Huang, Jingwei Chen, Yi Zheng, Yan Han, Haibin Huang
article en

Abstract

In this study, we introduce the concept of creative agents, aiming to enhance the capabilities of large language models (LLMs) in tasks related to the generation of prototypes of creative works. Our approach utilizes both self-collaboration and self-correction mechanisms, facilitated by hierarchical agents, to address the inherent complexities of target tasks. Our key observations are two-fold: first, effective task solving demands in-depth domain knowledge and intricate reasoning, for which deploying specialized agents for individual sub-tasks can markedly enhance LLM performance. Second, task solving intrinsically adheres to a hierarchical execution structure, comprising both high-level strategic planning and detailed task execution. Towards this end, our Creative-Agent paradigm aligns closely with this hierarchical structure, promising enhanced efficacy and adaptability across a wide range of scenarios. Specifically, our framework includes two novel modules, i.e., hierarchical Objects and Key Results generation and multi-level evaluation, both contributing to more efficient and robust task-solving. In practice, hierarchical OKR generation decomposes objects into multiple sub-objects and assigns new agents based on key results and agent responsibilities. These agents subsequently elaborate on their designated tasks and may further decompose them as necessary. Such generation operates recursively and hierarchically, culminating in a comprehensive set of detailed solutions. The multi-level evaluation module of Creative-Agent refines the solution by leveraging feedback from all associated agents, optimizing each step of the process. This scheme ensures the solution is accurate, practical, and meets the requirements of intricate tasks, enhancing the overall reliability and quality of the outcome. Experimental results demonstrated the effectiveness of Creative-Agent in completing the task of creating prototype generation works.

Beijing Institute of Graphic Communication (CN), OriginWater (China) (CN), Kuaishou (China), Communication University of China (CN)
Openalex Percentile: Top 93%
Artificial Intelligence in Games
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