The field of detecting Large Language Model (LLM)-generated text is rapidly advancing, with a focus on developing efficient, robust, and interpretable methods. Recent research has explored various approaches, including signal processing techniques, collaborative adversarial frameworks, and linguistic fingerprint analysis. These innovative methods have shown promising results in detecting LLM-generated text, outperforming state-of-the-art models and demonstrating improved robustness in out-of-distribution scenarios. Notable papers in this area include SpecDetect, which introduces a novel spectral analysis approach, and CAMF, which employs a collaborative adversarial multi-agent framework. Additionally, RepreGuard and MGT-Prism have proposed efficient statistics-based detection methods and spectral alignment strategies, respectively. DA-MTL is also noteworthy for its multi-task learning framework that addresses both text detection and authorship attribution.