The field of artificial intelligence is witnessing significant advancements in interactive world modeling and query extraction. Recent developments have focused on creating more realistic and interactive models, such as those using visual-action autoregressive Transformers, which can generate new scenes based on actions taken in a virtual environment. Another area of focus is on improving the accuracy of query extraction, particularly in complex scenarios involving multi-table joins and nested queries. Researchers are also exploring new approaches to training models, including reinforcement learning and unsupervised self-training frameworks. These advancements have the potential to revolutionize the way we interact with virtual environments and extract information from databases. Noteworthy papers include MineWorld, which proposes a real-time interactive world model on Minecraft, and Xpose, which presents a bi-directional engineering approach for hidden query extraction. Additionally, papers such as Genius and ReZero demonstrate the effectiveness of unsupervised self-training and retry-based approaches for enhancing large language model reasoning. Overall, these developments are pushing the boundaries of what is possible in interactive world modeling and query extraction, and are expected to have a significant impact on the field in the coming years.