Recent advances in multimodal large language models (MLLMs) have significantly improved emotion recognition across textual, acoustic, and visual modalities. However, most existing systems conceptualize emotion understanding as a static classification problem, limiting interpretability and structured reasoning capability. This paper proposes a unified theoretical framework that reformulates multimodal emotion understanding as an instruction-conditioned reasoning task. By conditioning multimodal representations on structured natural language instructions, the proposed framework jointly generates emotion predictions and logically grounded explanatory traces. The architecture integrates modality-aware alignment, temporal dialogue modelling, and instruction-guided reasoning within a coherent inference pipeline. Rather than optimizing solely for predictive accuracy, the framework emphasizes reasoning coherence, interpretability, and adaptability. Extensive experiments on benchmark datasets demonstrate statistically significant improvements in reasoning consistency and F1 performance compared to existing multimodal baselines. The proposed paradigm establishes a foundation for transparent, human-cantered, and ethically deployable emotion- aware conversational AI systems.
Shubhangi Vikas Kumbhar (Mon,) studied this question.