The field of hate speech detection and mitigation is rapidly evolving, with a focus on developing more accurate and robust models that can handle complex and nuanced forms of hate speech. Recent research has explored the use of large language models, multimodal representation learning, and adaptive feature gating to improve detection capabilities. Notably, the incorporation of contextual information and the development of persona-infused models have shown promise in reducing bias and improving fairness in hate speech detection. Furthermore, the use of reinforcement learning and automated red-teaming pipelines has enabled the generation of diverse implicit samples, which can be used to develop more comprehensive defense systems against joint-modal implicit malicious attacks. Overall, the field is moving towards more sophisticated and human-centric approaches to hate speech detection and mitigation. Noteworthy papers include: Leveraging LLMs for Context-Aware Implicit Textual and Multimodal Hate Speech Detection, which introduced a novel approach to incorporating contextual information into hate speech detection models. Learning from Mistakes: Enhancing Harmful Meme Detection via Misjudgment Risk Patterns, which proposed a new approach to improving harmful meme detection by learning from misjudgment risk patterns.