World Model Revolution: From Embodied Interaction and Virtual Rendering to the "Camera Outward" AGI Learning Paradigm

Current research on Artificial General Intelligence (AGI) centered on World Models has generally fallen into bottlenecks of high cost, excessive reliance on embodied interaction, and over-dependence on labeled data. This paper proposes an original "Camera Outward" AGI learning paradigm. Starting from the underlying logic of biological intelligence, it demonstrates that the world itself is the perfect world model. By comparing three mainstream approaches—Fei-Fei Li’s virtual world rendering, Elon Musk’s embodied intelligent robots, and Yann LeCun’s JEPA self-supervised learning—this paper points out the limitations of existing research. Combined with the analogy to Full Self-Driving (FSD) technology, it clarifies that the core goal of this paradigm is not task-oriented, but to enable AI to understand the continuity of the physical world through continuous visual observation, and to fit and verify physical formulas. Meanwhile, this paper presents a subversive insight: the root cause why AI cannot perceive physical continuity lies in its excessively high computing speed, and reducing the processing speed during training is the key to breaking this dilemma. The paper emphasizes that labels distract AI’s attention from the laws of the world, and unsupervised learning is the core path to building a general world model. This paradigm is highly consistent with Yann LeCun’s cutting-edge research ideas and provides a more thorough and original expansion on the core logic, offering a new lightweight, low-cost, and generalized implementation path for the construction of AGI world models.