My research focuses on mechanistic interpretability-understanding and improving multi-modal models by analyzing their internal computations. I will present recent studies, including Diffusion Lens (ACL 2024), Padding Tone (NAACL 2025), and Follow the Flow (ACL 2026), and DeLeaker (ICL 2026). These works propose methods to reveal what text-to-image models learn and how computations are performed internally. These insights are then applied to solve concrete problems, such as semantic leakage, paving the way for more reliable and transparent generative AI.