The recent advancements in the field of AI-generated text detection and manipulation have shown significant progress in both the robustness and adaptability of detection methods. Researchers are increasingly focusing on developing multi-level contrastive learning frameworks and reinforcement learning strategies to enhance the detection of AI-generated content, while also exploring ways to bypass these detection systems through sophisticated proxy-attack strategies. Notably, the integration of large language models (LLMs) into scam detection and cryptocurrency abuse classification has demonstrated high accuracy and efficiency, contributing to safer digital ecosystems. Additionally, the feasibility of paraphrase inversion and the challenges posed by diverse writing styles have been highlighted, emphasizing the need for more nuanced detection methods. The field is also witnessing a shift towards online detection algorithms that provide real-time analysis and statistical guarantees, addressing the dynamic nature of content generation and dissemination. Overall, the research is moving towards more sophisticated, adaptable, and real-time solutions that can keep pace with the evolving capabilities of AI-generated text.