Multimodal sentiment analysis remains a challenging task due to the inherent heterogeneity across modalities. Such heterogeneity often manifests as asynchronous signals, imbalanced information between modalities, and interference from task-irrelevant noise, hindering the learning of robust and accurate sentiment representations. To address these issues, we propose a factorized multimodal fusion framework that first disentangles each modality into shared and unique representations, and then suppresses task-irrelevant noise within both to retain only sentiment-critical representations. This fine-grained decomposition improves representation quality by reducing redundancy, prompting cross-modal complementarity, and isolating task-relevant sentiment cues. Rather than manipulating the feature space directly, we adopt a mutual information–based optimization strategy to guide the factorization process in a more stable and principled manner. To further support feature extraction and long-term temporal modeling, we introduce two auxiliary modules: a Mixture of Q-Formers, placed before factorization, which precedes the factorization and uses learnable queries to extract fine-grained affective features from multiple modalities, and a Dynamic Contrastive Queue, placed after factorization, which stores latest high-level representations for contrastive learning, enabling the model to capture long-range discriminative patterns and improve class-level separability. Extensive experiments on multiple public datasets demonstrate that our method consistently outperforms existing approaches, validating the effecti veness and robustness of the proposed framework.

In dyadic interaction, listener reaction generation can be treated as a one-to-many mapping problem since multiple listener reactions can correspond to a given speaker action. The existing methods have not modeled the diversity of contextual factors well and fail to generate diverse appropriate listener reactions. In response, we introduce discrete latent variables to tackle this one-to-many mapping problem. We conducted experiments on the datasets provided by the REACT2024 Challenge, and the results demonstrated that our approach is capable of generating appropriate listening reactions with higher diversity. Our method achieved first place in the offline track and second in the online track.