参与式城市感知利用人类移动性实现大规模城市数据采集，但现有方法通常依赖中心化优化，并假设参与者同质化，导致任务分配僵化，忽视个人偏好及城市环境的异质性。我们提出 MAPUS——一种基于大语言模型（LLM）的多智能体框架，用于支持个性化与公平性的参与式城市感知。在该框架中，参与者被建模为具备个体档案与日程安排的自主智能体，而协调智能体则执行兼顾公平性的选择，并通过基于自然语言的协商机制优化感知路径。在真实世界数据集上的实验表明，MAPUS 在保持具有竞争力的感知覆盖率的同时，显著提升了参与者满意度与公平性，从而推动构建更以人为中心、更具可持续性的城市感知系统。

Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous participants, resulting in rigid assignments that overlook personal preferences and heterogeneous urban contexts. We propose MAPUS, an LLM-based multi-agent framework for personalized and fair participatory urban sensing. In our framework, participants are modeled as autonomous agents with individual profiles and schedules, while a coordinator agent performs fairness-aware selection and refines sensing routes through language-based negotiation. Experiments on real-world datasets show that MAPUS achieves competitive sensing coverage while substantially improving participant satisfaction and fairness, promoting more human-centric and sustainable urban sensing systems.