UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization
Kai Yang, Zelin Zhu, Chengtao Jian, Hui Ma, Shengjie Zhao, Xiaozhou Ye, Ye Ouyang

TL;DR
UrbanMind introduces a novel AI framework combining retrieval-augmented generation and multilevel optimization to enable autonomous, adaptable urban intelligence capable of perceiving, reasoning, and acting in complex city environments.
Contribution
The paper proposes C-RAG-LLM, a new architecture integrating domain knowledge and urban data with a hierarchical optimization framework for urban general intelligence.
Findings
Effective in real-world urban tasks
Supports long-term adaptability and data drift handling
Flexible training and optimization options
Abstract
Urban general intelligence (UGI) refers to the capacity of AI systems to autonomously perceive, reason, and act within dynamic and complex urban environments. In this paper, we introduce UrbanMind, a tool-enhanced retrieval-augmented generation (RAG) framework designed to facilitate UGI. Central to UrbanMind is a novel architecture based on Continual Retrieval-Augmented MoE-based LLM (C-RAG-LLM), which dynamically incorporates domain-specific knowledge and evolving urban data to support long-term adaptability. The architecture of C-RAG-LLM aligns naturally with a multilevel optimization framework, where different layers are treated as interdependent sub-problems. Each layer has distinct objectives and can be optimized either independently or jointly through a hierarchical learning process. The framework is highly flexible, supporting both end-to-end training and partial layer-wise…
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