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The Hong Kong University of Science and Technology (Guangzhou)
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UrbanAgent outperforms traditional methods by leveraging multi-agent reasoning to tackle cross-modal inconsistencies in urban profiling tasks.
Current LLMs may appear accurate, but they often rely on inconsistent memory states that traditional benchmarks fail to reveal.
