The field of natural language processing is witnessing significant developments in the detection of AI-generated text and the deployment of decentralized AI platforms. Researchers are exploring innovative approaches to detect and mitigate the risks associated with AI-generated text, including the use of adversarial training and perturbation-invariant feature engineering. Meanwhile, decentralized AI platforms are being proposed to address the trust and cost issues associated with centralized LLM services. These platforms leverage blockchain technology and crowdsourcing to enable secure and efficient model deployment and inference. Noteworthy papers in this area include Modeling the Attack, which presents a novel detection framework that achieves state-of-the-art performance in detecting AI-generated text, and PolyLink, which introduces a blockchain-based decentralized AI platform for LLM inference. Other notable works include EditLens, which proposes a regression model to quantify the extent of AI editing in text, and Audit the Whisper, which introduces a calibrated auditing pipeline to detect steganographic collusion in multi-agent LLMs.