The field of multimodal reasoning and knowledge editing is rapidly advancing, with a focus on developing more accurate and efficient models for vision-language tasks. Recent research has highlighted the importance of addressing challenges such as hallucinations in model responses, inefficiencies in fixed-depth reasoning, and the need for multi-institutional collaboration. To address these challenges, innovative frameworks and architectures are being proposed, including those that utilize multimodal preference optimization, federated meta-cognitive reasoning, and compositional knowledge editing. These advances have the potential to significantly improve the performance of vision-language models in a variety of applications, including medical visual question answering and sign language translation. Notable papers in this area include MedAlign, which proposes a novel framework for ensuring visually accurate responses in medical visual question answering, and MemEIC, which enables compositional editing of both visual and textual knowledge in large vision-language models. Additionally, the development of new benchmarks and evaluation metrics, such as PISA-Bench and MedVLSynther, is providing valuable resources for advancing research in this field.