Researchers have proposed a new framework to address modality imbalance in multimodal learning. They introduce a metric called the Modality Gap to quantify prediction discrepancies between different modalities and use a Gaussian Mixture Model (GMM) to model this gap distribution. The GMM is used to dynamically reallocate optimization priorities, imposing stronger penalties on imbalanced samples while prioritizing multimodal fusion for balanced ones. Experimental results show that their method outperforms current state-of-the-art baselines. --- Why it matters: This matters because modality imbalance can significantly impact the performance of multimodal models, leading to biased predictions and poor generalization. By mitigating this imbalance, researchers can develop more robust and accurate models for applications such as image classification, natural language processing, and computer vision. Source: https://arxiv.org/abs/2510.21797