## 5 Discussion and Conclusions
In conclusion, our method innovatively employs a Gaussian-Voronoi map and a chain-of-thought behavior tree to facilitate the direct synthesis of 3D terrain from textual descriptions, successfully bypassing the limitations associated with traditional diffusion models. This approach not only demonstrates superior performance in generating complex 3D terrains with high accuracy but also provides a flexible framework for secondary terrain editing, enabling dynamic adjustments based on updated textual input. The introduction of multiagent strategies further enhances the local updating capabilities, ensuring that the generated terrains are both detailed and consistent with the provided text specifications. Our findings suggest that the direct interpretation of text into 3D data is not only viable but also highly efficient, offering a new perspective in the text-to-3D domain. The ability to generate detailed and controllable 3D terrains from text opens up new avenues for applications in virtual reality, gaming, and simulation environments. By advancing the state of the art in text-to-3D synthesis, our work paves the way for future research in the field, promising enhancements in both the quality of 3D model generation and the efficiency of the process, particularly in real-time and interactive applications. [PERSON] was responsible for the conceptualization, methodology, software development, data curation, writing--original draft preparation, and visualization. [PERSON] contributed to validation, resources, writing--review and editing, and funding acquisition. [PERSON] carried out the formal analysis. [PERSON] was in charge of the investigation. [PERSON] supervised the project. [PERSON] managed the project administration. All authors have read and agreed to the published version of the manuscript. This work was funded by the Project of Humanities and Social Sciences of the Ministry of Education in China, grant number 22 YJAZH002. Not applicable. **Data Availability Statement**: Due to laboratory confidentiality agreements and considerations for participant privacy, we are unable to publicly disclose our experimental data. However, we welcome inquiries from researchers, including anonymous reviewers, who wish to access the complete dataset. Such requests should be directed to the second author of this paper, after stating the necessary reasons for data access. Regarding the project source code, it will be made available on our project homepage link after the end of the project cycle and upon acceptance and publication of the paper. The dataset used in the project has already been made available at the following URL: [[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/))FI3 GS/Zero-Shot-Text-to-3D-Terrain-Synthesis-Using-Only-Large-Language-Model, accessed on 9 March 2024. We would like to express our gratitude to the pioneers in the field, the various research achievements cited in this paper. We extend our thanks to our family members for their unwavering support and encouragement. To the successors, your future citations and references to this work are crucial for broadening the impact of our research and extending its life. We are also grateful to our peers, the companions of our academic journey, whose shared efforts and struggles in learning and striving have left an indelible mark on our hearts. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
CoT chain-of-thought  
ToT tree-of-thought  
LLM large language model