Multimodal learning often faces modality competition, where stronger modalities dominate and degrade overall performance. To address this, we propose the Cross-modal Competition Regularizer with Permutation - based Latent Regularizer (CCR-PLR), a game-theoretic information-driven framework that balances modality contributions. CCR models learning as a constant-sum game, encouraging each modality to enhance unique, task-relevant information while reducing redundancy. The PLR applies within-batch latent permutations and measures prediction shifts using Jensen - Shannon Divergence to approximate combinational mutual information efficiently. Combined with modality-specific and shared encoders, CCR-PLR explicitly optimizes complementary and discriminative representations. Experiments on a dual-modal pressure - vibration dataset show CCR-PLR consistently surpasses ResNet, Conformer, and CNN baselines in accuracy and robustness under corrupted inputs. Ablation results further verify that CCR-PLR effectively mitigates modality imbalance and improves generalization in cross-modal learning.

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