Urban regeneration presents significant challenges within the context of urbanization, requiring adaptive approaches to tackle evolving needs. Leveraging advancements in large language models (LLMs), we propose Cyclical Urban Planning (CUP), a new paradigm that continuously generates, evaluates, and refines urban plans in a closed-loop. Specifically, our multi-agent LLM-based framework consists of three key components: (1) Planning, where LLM agents generate and refine urban plans based on contextual data; (2) Living, where agents simulate the behaviors and interactions of residents, modeling life in the urban environment; and (3) Judging, which involves evaluating plan effectiveness and providing iterative feedback for improvement. The cyclical process enables a dynamic and responsive planning approach. Experiments on the real-world dataset demonstrate the effectiveness of our framework as a continuous and adaptive planning process.

本研究解决了城市再生过程中面临的适应性需求问题，提出了一种新的循环城市规划（CUP）范式。该框架利用多智能体大语言模型，通过生成、评估和完善城市规划，实现动态响应的规划过程。实验表明，该框架有效支持持续且适应性的城市规划。

规划、生活与评估：基于多智能体大语言模型的循环城市规划框架