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This paper designs a Dual-Branch Spatio-Temporal Graph Convolutional Network with Transformer with a robust loss function to suppress the effect of extreme outliers and proposes a feature-separation prediction method for bus arrival time.
The RLVR across domains exhibits small mutual interferences, and reasoning-intensive domains have mutually synergistic effects, and the internal mechanisms from the perspectives of information constraints, model prediction behavior and self-verification are analyzed.
SAGE (Stability-Aware Gradient Efficiency), which maintains difficulty-stratified candidate pools refreshed during training and selects pairs within each pool by a forward-pass signal-to-curvature score, outperforms full-data and size-matched baselines while producing substantially smoother optimization trajectories.
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