Multi Modal

Multimodal research focuses on integrating and analyzing data from multiple sources (e.g., text, images, audio, sensor data) to achieve a more comprehensive understanding than any single modality allows. Current research emphasizes developing robust models, often employing large language models (LLMs) and graph neural networks (GNNs), to handle the complexity of multimodal data and address challenges like error detection in mathematical reasoning, long-horizon inference, and efficient data fusion. This field is significant for advancing AI capabilities in diverse applications, including improved recommendation systems, assistive robotics, medical diagnosis, and autonomous driving, by enabling more nuanced and accurate interpretations of complex real-world scenarios.