AI RESEARCH
Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing
arXiv CS.AI
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ArXi:2603.24014v1 Announce Type: new 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.