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By explicitly aligning the graph structures of predicted and actual time series correlations, GCGNet achieves superior forecasting accuracy in noisy, real-world datasets compared to methods that model temporal and channel dependencies separately.
Forget static agent communication structures: ST-EVO dynamically reshapes multi-agent dialogue flows in both space and time, boosting accuracy by up to 25%.
