Researchers have developed a framework for multimodal classification that balances accuracy and interpretability. The framework uses tree-based ensembles to fuse text, audio, and visual streams, and assigns meaningful importance weights to individual features. This is done by encoding each modality into tokens, extracting and clustering concepts, and routing the fused modalities through tree-based ensemble classifiers. The proposed method achieves higher accuracy and better interpretability compared to existing models, including Transformers, which are known for their strong predictive performance but lack transparency. --- Why it matters: This work matters because it addresses a critical gap in AI research: developing models that can provide both accurate and interpretable results. This is particularly important in applications where trust is essential, such as clinical affect monitoring and educational assessment. Source: https://arxiv.org/abs/2608.20384