The field of vision-language models and machine-generated text detection is moving towards developing more robust and generalizable models. Researchers are exploring new methods to improve domain generalization, such as latent domain clustering and multi-prompt learning, to enable models to adapt to unseen domains and tasks. Additionally, there is a growing focus on detecting machine-generated text, with a emphasis on addressing the challenges of paraphrase attacks and domain shift. Noteworthy papers in this area include: PADBen, which introduces a comprehensive benchmark for evaluating AI text detectors against paraphrase attacks, and DEER, which proposes a disentangled mixture-of-experts framework for generalizable machine-generated text detection. These advancements have the potential to significantly improve the performance and reliability of vision-language models and machine-generated text detection systems.