构建可自适应城市环境的AI智能体，通过动态知识更新与分层优化实现长期演化。
UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization
- 采用持续检索增强的混合专家模型，动态融合领域知识与实时城市数据。
- 分层优化框架支持独立或联合训练，适配不同资源条件下的部署需求。
- 具备增量语料更新能力，有效应对城市数据漂移，适合复杂城市场景应用。
城市通用智能（UGI）指AI系统在动态复杂的都市环境中自主感知、推理与行动的能力。本文提出UrbanMind，一种基于工具增强的检索增强生成（RAG）框架，以推动UGI发展。核心是连续检索增强的混合专家语言模型（C-RAG-LLM），能动态整合特定领域知识与演化的城市数据，支持长期适应性。该架构天然契合多层级优化框架，各层作为相互依赖的子问题，拥有不同目标，可通过分层学习过程独立或联合优化。框架高度灵活，支持端到端训练及按层部分优化，适应资源或部署约束。为应对数据漂移，进一步集成增量语料更新机制。在多种复杂度的真实城市任务上评估验证了该框架的有效性。本工作为未来城市环境中通用大模型智能体的实现迈出重要一步。
原文摘要 · Abstract (English)
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 optimization based on resource or deployment constraints. To remain adaptive under data drift, it is further integrated with an incremental corpus updating mechanism. Evaluations on real-world urban tasks of a variety of complexity verify the effectiveness of the proposed framework. This work presents a promising step toward the realization of general-purpose LLM agents in future urban environments.
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