The field of large language models (LLMs) is rapidly evolving, with a growing focus on transparency and accountability. Recent research has highlighted the need for innovative solutions to address issues such as token count inflation, authorship privacy, and model ownership verification. One of the key directions in this area is the development of verification frameworks that can audit the quantity and semantic validity of hidden tokens in commercial LLM APIs. Additionally, there is a growing interest in watermarking techniques that can ensure the provenance and accountability of AI-generated text. Another important area of research is the analysis of interwoven roles of LLMs in authorship privacy, including authorship obfuscation, mimicking, and verification. Noteworthy papers in this regard include CoIn, which proposes a verification framework to detect token count inflation, and Invisible Entropy, which introduces a lightweight feature extractor and an entropy tagger to predict whether the entropy of the next token is high or low. Furthermore, papers such as Unraveling Interwoven Roles of Large Language Models in Authorship Privacy and Can Large Language Models Really Recognize Your Name? highlight the importance of understanding the limitations and potential risks of relying on LLMs for privacy-related tasks.