Attention as Selection: Semantic-Guided Time Series Forecasting
This work defines cross-modal attention as a selection process, where semantic prompts are employed to induce a sparse temporal attribution distribution over temporal positions and employs low-entropy regularization alongside global cross-modal consistency constraints to regulate the selection behavior.