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Seasonal priors in a hybrid Convolutional-Transformer model boost Antarctic sea ice concentration forecasting accuracy beyond conventional methods.
Black-box RL can boost agent performance by nearly 15 points on complex tasks, revealing a new frontier for scalable optimization.
By aligning attention patterns between intact and corrupted image processing paths, CrystaL crystallizes task-relevant visual semantics in MLLM latent spaces without needing extra annotations.
