MetaX Weekly AI Paper Review Week 38 2025 -- Deeply Researched and Reasoning AI Agents Through Continuous Pre-training and Reinforcement Learning, 4D World Modeling and 3D Asset Generation Automation Based on Large-Scale Datasets: OmniWorld Multi-Domain and Multi-Modal Dataset for 4D World Modeling (arxiv 2509.12201): To advance 4D world modeling technology where AI understands 3D space and time flow together, high-quality data is essential but existing datasets lacked complexity. OmniWorld was released -- a large-scale dataset containing diverse environments and realistic interactions; AI models trained on this dataset showed significantly improved performance in 4D spatial reconstruction and video generation tasks; this is an important resource accelerating development of more advanced 4D world models. Additional paper reviews covered: RLVR methods for improving LLM agent reasoning through reinforcement learning from verifiable rewards; advances in agentic AI systems capable of multi-step tool use and planning; multimodal foundation models combining vision and language for complex reasoning; and 3D asset generation pipelines using diffusion models and AI-powered automation for game and simulation content creation. The week 38 research theme: the papers collectively demonstrate the rapid convergence of reasoning capability improvements, world modeling, and agentic behavior -- the combination that defines the path toward more capable AI systems that can understand and act in complex real-world environments.