Core Insights - The article emphasizes the necessity of large-scale, causally rich interaction data for developing world models with true physical intuition, contrasting with current models that rely on passive observation [2][3] Group 1: WoW Model Overview - WoW is a generative world model trained on 2 million robot interaction trajectories, featuring 14 billion parameters [2] - The model's understanding of physical laws is probabilistic, leading to random instability and physical illusions [2] - The SOPHIA framework is introduced to evaluate the physical plausibility of generated results and guide the model towards physical reality through iterative language instructions [2] Group 2: Evaluation and Performance - WoWBench benchmark was created to systematically assess the model's physical consistency and causal reasoning capabilities [3] - WoW achieved leading performance in both manual and automated evaluations, particularly excelling in adherence to physical laws (80.16%) and instruction comprehension (96.53%) [3] - The research provides solid evidence that large-scale real-world interactions are essential for cultivating AI's physical intuition [3] Group 3: Live Event and Discussion - A live session is scheduled to discuss the latest open-source embodied world model WoW 1.0, covering trends in world model development and breakthroughs in causal and physical consistency [7] - Key highlights include the architecture of agents that imagine, act, and reflect, as well as practical application scenarios [7]