Abstract

Multimodal reasoning models have emerged as powerful frameworks for integrating and interpreting information from heterogeneous data sources—text, images, audio, video, structured data, and sensor streams—to support complex decision‑making across domains. Traditional unimodal models are limited in their capacity to relate patterns across modalities, whereas multimodal reasoning leverages complementary strengths of each modality to form richer representations and deeper semantic understanding. This paper examines the theoretical foundations, architectures, and applications of multimodal reasoning models, emphasizing their role in cross‑domain knowledge integration. We survey early and contemporary multimodal fusion techniques, from early statistical co‑occurrence methods to sophisticated neural architectures like attention‑based transformers and joint embedding spaces. A structured methodology for designing, training, and evaluating multimodal models is presented, addressing challenges such as modality heterogeneity, alignment, interpretability, and dataset bias. We analyze advantages including enriched semantic context, improved generalization, and enhanced interpretability, alongside disadvantages such as computational complexity, data scarcity, and modality imbalance. Empirical results across use cases—medical diagnosis, autonomous systems, and multimedia search—highlight the effectiveness of multimodal reasoning in bridging domain gaps. The paper concludes with future research directions focused on scalable architectures, zero‑shot cross‑domain transfer, and ethical considerations in multimodal inference.