Multi Modal Reasoning

Multi-modal reasoning focuses on enabling artificial intelligence systems to understand and reason using information from multiple sources, such as text, images, and audio. Current research emphasizes improving the ability of large language and vision-language models to handle ambiguous instructions, perform complex scientific reasoning across various disciplines, and overcome biases in datasets. This involves developing novel architectures like independent inference units and feature swapping modules, as well as leveraging techniques such as chain-of-thought prompting and retrieval-augmented reasoning to enhance model performance and interpretability. Advances in multi-modal reasoning are crucial for building more robust and versatile AI systems with applications spanning education, scientific discovery, and various other fields requiring complex information integration.