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Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing

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arXiv:2603.24014v1 Announce Type: new Abstract: 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 auton

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Computer Science > Artificial Intelligence [Submitted on 25 Mar 2026] Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing Xusen Guo, Mingxing Peng, Hongliang Lu, Hai Yang, Jun Ma, Yuxuan Liang 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. Comments: 19 pages, 12 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.24014 [cs.AI] (or arXiv:2603.24014v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.24014 Focus to learn more Submission history From: Xusen Guo [view email] [v1] Wed, 25 Mar 2026 07:19:45 UTC (2,115 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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