## 5 Discussion and Conclusions  
From the perspective of model quality, our method provides the highest quality in the specialized task of generating 3D terrain models in the text-to-3D domain, significantly surpassing the capabilities of existing diffusion-model-based methods and offering highly flexible secondary editing capabilities for the models. This superior performance is primarily based on the three steps of our method that completely bypass diffusion models. First, we introduced the Gaussian-Voronoi map, which receives data in the form of Gaussians and maps them onto a map segmented by Voronoi, ensuring the method's ability to generate complex terrains from simple outputs. Then, we utilized the chain-of-thought behavior tree to extract consistent terrain information from diverse text sequences, ensuring that the method can extract terrain features from text sequences, and adopted a multiagent strategy to segment and process terrain features, achieving high-quality local updating of 3D terrains. Our research demonstrates that abandoning diffusion models and directly reading 3D data from text to generate 3D models is feasible and holds considerable promise, warranting further investigation. However, our method also presents some issues that merit further research. Our method did not exhibit good robustness when faced with text inputs with significant metric  
Figure 9: We attempted to input a piece of text containing both comparative and positional information into the LLM. The LLM, which applied the chain-of-thought behavior tree, effectively identified this type of information and generated data that matched the description. On the other hand, using only the chain-of-thought-based LLM, although it recognized the three types of information, it did not arrange the positions of the three mountains well during data generation, causing them to blend together. In contrast, the natural-language-processing-based LLM was unable to fully recognize these contents. This result further demonstrates the effectiveness of the chain-of-thought behavior tree and its relative accuracy and stability in processing data compared with the chain-of-thought. scale errors. We believe that designing a more sophisticated chain-of-thought strategy will greatly contribute to addressing the aforementioned issues. Specifically, to improve the robustness of our method when faced with text inputs containing significant metric scale errors, we propose two approaches. First, we can preprocess the input text by comparing it with historical data to check for consistency in metric information. This preliminary step would help identify and rectify scale discrepancies before they affect the terrain generation process. On the other hand, we can enhance the model's understanding of metric data through fine-tuning. By preloading a set of feasible metric data and fine-tuning the large language model, we aim to achieve self-correction of metric errors. These strategies will contribute to a more accurate interpretation of spatial dimensions and enhance the overall robustness of our terrain synthesis method. In our forthcoming research, our goal is to explore methods for transferring Gaussians into three-dimensional space for representation, which will allow us to generate terrains with more complex features, such as caves and waterfalls, to our 3D terrain. In terms of method speed, we believe that employing a strategy of dividing the map into sections and using a multithreaded approach to concurrently invoke multiple LLM processes for individual map modifications can significantly enhance the efficiency of our method, approaching real-time performance. Regarding computational performance, the primary limitation lies in the performance overhead of the large language models. The inherent overhead of our method is relatively low. Therefore, utilizing online large language model service APIs or switching to smaller large language models could greatly improve our efficiency.