The field of autonomous systems is witnessing significant advancements with the integration of Large Language Models (LLMs). Recent developments indicate a shift towards more robust, adaptive, and generalizable autonomous agents. The use of LLMs is enabling autonomous systems to better understand and interact with their environment, making them more reliable and efficient. Notably, the incorporation of LLMs in robotics and autonomous driving is leading to improved performance in complex tasks such as task planning, motion planning, and decision-making. Furthermore, the development of frameworks that combine LLMs with other techniques, such as reinforcement learning and computer vision, is enhancing the capabilities of autonomous systems. Overall, the field is moving towards more intelligent and autonomous systems that can operate effectively in real-world environments. Noteworthy papers include: Think, Act, Learn, which introduces a novel framework for autonomous robotic agents using closed-loop LLMs, achieving a 97% success rate on complex tasks. VLMPlanner is also notable, as it integrates visual language models with motion planning for autonomous driving, demonstrating superior planning performance in challenging scenarios.