The field of Large Language Models (LLMs) is rapidly advancing, with a focus on integrating LLMs into various applications and automating tasks. Recent developments have centered around improving the coordination and verification of LLMs, enabling them to work together seamlessly and produce reliable results. This has led to breakthroughs in areas such as CPU verification, code review, and network protocol testing. Notably, researchers have proposed novel frameworks and protocols to enhance the capabilities of LLMs, including the use of attention-based predictors, hierarchical protocol understanding, and domain-informed prompting. These innovations have significant implications for the future of software development, autonomous systems, and AI-powered agents. Noteworthy papers include: ISAAC, which presents a full-stack LLM-aided CPU verification framework with FPGA parallelism, achieving up to 17,536x speed-up over software RTL simulation. SpecGPT, which leverages LLMs to automatically extract protocol state machines from 3GPP documents, outperforming existing approaches and demonstrating the effectiveness of LLMs for protocol modeling at scale.