Autonomous aerial vehicles (AAVs) have played a pivotal role in coverage operations and search missions. Recent advances in large language models (LLMs) offer promising opportunities to augment AAV intelligence. These advances help address complex challenges like area coverage optimization, dynamic path planning, and adaptive decision-making. However, the absence of physical grounding in LLMs leads to hallucination and reproducibility problems in spatial reasoning and decision-making. To tackle these issues, we present Skypilot, an LLM-enhanced two-stage framework that grounds language models in physical reality by integrating monte carlo tree search (MCTS). In the first stage, we introduce a diversified action space that encompasses generate, regenerate, fine-tune, and evaluate operations, coupled with physics-informed reward functions to ensure trajectory feasibility. In the second stage, we fine-tune Qwen3-4B on 23,000 MCTS-generated samples, achieving substantial inference acceleration while maintaining solution quality. Extensive numerical simulations and real-world flight experiments validate the efficiency and superiority of our proposed approach. Detailed information and experimental results are accessible at https://sky-pilot.top.

According to Ion Stoica, co-founder of Databricks and Anyscale, and also a senior professor of computer science at Berkeley, 2023 will be the year of "distributed AI frameworks." Needless to say, he has already had a hand in creating such a tool, in the form of Anyscale's open source Ray platform. Among other uses, Ray helps power OpenAI's groundbreaking ChatGPT. I interviewed Stoica to find out what Ray does exactly and, more generally, what is needed to scale AI software in this new era of generative AI. We also discuss the latest in "sky computing," a term Stoica and his Berkeley team introduced in 2021, in a paper that proposed a new form of cloud computing based around interoperability and distributed computing.