UrbanAgent framework uses multi-agent reasoning for urban profiling · 2 sources tracked

Researchers have introduced UrbanAgent, a novel framework for urban region profiling that moves beyond traditional multimodal representation learning. This agentic system utilizes independent agents for each data modality to perform collaborative reasoning, explicitly addressing cross-modal inconsistencies. UrbanAgent also incorporates active evidence acquisition and iterative reasoning, allowing agents to retrieve and verify external knowledge using tools, which is optimized through reinforcement learning. Experiments demonstrate that UrbanAgent significantly outperforms existing methods in estimating carbon emissions, GDP, and population, showing an average R2 improvement of 8.1% and strong generalization to unseen cities. AI

IMPACT This agentic approach to urban region profiling could improve the accuracy and generalization of AI models in complex, real-world data scenarios.

RANK_REASON The cluster contains a research paper detailing a new framework and experimental results.

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• Connected Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• Influence Flower
• Litmaps
• ScienceCast
• scite Smart Citations
• UrbanAgent

• paper
• other

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →