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Shanghai Jiao Tong University
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Exploiting client heterogeneity as complementary observations leads to a breakthrough in spatio-temporal forecasting, outperforming traditional methods.
Synthesizing realistic human mobility in data-scarce regions is now possible thanks to a dual-LLM-agent framework that learns physical constraints via reinforcement learning.
