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We introduced , a topology-aware Vision Transformer that dynamically adapts its token grid to image content. By replacing the rigid patch paradigm with a Dynamic Adaptive Lattice, DaleNet preserves the geometric integrity of visual data while reducing computational redundancy. DaleNet sets a new benchmark for efficient vision architectures, proving that the structure of the tokenization step is as critical as the attention mechanism itself.

Unlike standard neural networks, the Bi-LSTM layer analyzes data in both forward and backward directions, which is critical for understanding time-dependent biomarkers found in EEG signals. dalenet