The field of artificial intelligence and autonomy is rapidly evolving, with a focus on developing innovative solutions for complex tasks and systems. Recent research has explored the integration of large language models (LLMs) with various applications, including embodied agents, thermal-fluid systems, and industrial automation. These advancements have led to improved performance, robustness, and adaptability in autonomous systems. Notably, the use of LLMs has enabled the development of conditional multi-stage failure recovery frameworks, AI-driven thermal-fluid testbeds, and agentic frameworks for next-generation industrial automation. Noteworthy papers include: The Conditional Multi-Stage Failure Recovery for Embodied Agents paper, which introduces a framework that achieves state-of-the-art performance on the TfD benchmark. The Autonomous Control Leveraging LLMs paper, which presents a unified agentic framework that leverages LLMs for both discrete fault-recovery planning and continuous process control.