Multimodal Models and VLMs

A vision-language model lets an LLM see by running images through a vision encoder and a projection layer that turns them into tokens the language model reads alongside text. CLIP-style contrastive training aligns image and text into one embedding space, powering image search and zero-shot classification. FDE loops probe this because document, chart, and screenshot understanding is a common deployment, and the failure modes (counting, fine detail, hallucinated visual facts) are specific.