让居民在模拟生活中持续反馈，自动优化城市规划。
LiPUP-MA: A Residential Experience-centric Multi-Agent Framework for Living-in-the-loop Participatory Urban Planning
- 构建闭环框架，结合生活模拟与反馈迭代优化
- 实验显示新方案在静态与真实体验指标上均更优
- 适合关注社区参与和智慧城市的城市规划者
参与式城市规划（PUP）正越来越多依赖基于大语言模型的智能体，但现有方法多采用静态偏好收集和一次性利益相关方讨论，忽视了现实中居民生活、体验收集与规划调整的循环互动。本文提出‘在环中居住’参与式城市规划（LiPUP），一种闭环范式，交替进行模拟居住与基于体验的规划修订。该框架面临两大挑战：如何将分散的居住体验锚定于具体城市环境，以及如何将主观反馈转化为空间一致的规划动作。为此，我们提出 LiPUP-MA——一个基于 LLM 的多智能体系统，通过构建以规划为中心的图结构体验库，组织来自居住模拟的具象化反馈，并配备空间约束的技能增强型规划智能体，综合体验、视觉与地理空间证据进行规划修正。实验表明，LiPUP-MA 在传统静态规划指标与基于生活的评估指标上均优于基线，且多次迭代的 LiPUP 循环进一步提升了规划质量。
原文摘要 · Abstract (English)
Participatory Urban Planning (PUP) is increasingly supported by LLM-based agents, yet existing methods largely rely on static preference elicitation and one-shot stakeholder discussions, overlooking the cyclical nature of real-world planning, where residential life, experience collection, and plan adjustment continually interact. We propose Living-in-the-loop Participatory Urban Planning (LiPUP), a closed-loop paradigm that alternates between simulated residential living and experience-driven plan revision, while posing two key challenges: grounding scattered living experience in concrete urban contexts and translating subjective feedback into spatially coherent planning actions. To instantiate LiPUP, we introduce LiPUP-MA, an LLM-based multi-agent framework that constructs a Plan-centric Graph-based Experience Bank to organize urban-grounded residential feedback from living simulation and equips a Spatially-constrained Skill-augmented Planner agent to revise plans by harmonizing experiential, visual, and geospatial evidence. Experiments show that LiPUP-MA consistently outperforms baselines on both conventional static planning metrics and living-based metrics, while iterative LiPUP cycles further improve plan quality.
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