CV algorithm development by the masses for the masses

Why Multimodal Reasoning Matters for Vision-Language Tasks Multimodal reasoning enables models to make informed decisions and answer questions by combining both visual and textual information. This type of reasoning plays a central role in interpreting charts, answering image-based questions, and understanding complex visual documents. The goal is to make machines capable of using vision as…

While VLMs are strong at understanding both text and images, they often rely solely on text when reasoning, limiting their ability to solve tasks that require visual thinking, such as spatial puzzles. People naturally visualize solutions rather than describing every detail, but VLMs struggle to do the same. Although some recent models can generate both…