Imagine the scene from a movie where Captain America, with a single command in an elevator, alters not just his immediate actions but the very fate of the universe. This captivating scenario serves as a springboard for introducing Yang Likun’s groundbreaking H-JEPA model. Comprised of hierarchically stacked JEPAs, this model diverges from traditional methods by compressing images into abstract vectors rather than generating content directly at the pixel level. It predicts future states across various time frames within a latent representation space. Once the high-level layer establishes macro goals, these are refined progressively into sub-goals, culminating in specific actions generated by the bottom layer.

Contrasting sharply with the prevalent Agent-based approach—which dismantles lengthy tasks into smaller, trial-and-error steps dependent on reward-driven training—H-JEPA ascends by enhancing abstraction levels. It eschews the need for external rewards or pixel reconstruction, focusing solely on predicting states within the representation space. This strategy minimizes error propagation in extended tasks, lightens the computational burden of planning, and significantly outperforms single-layer models in complex scenarios like maze navigation. Yet, it's worth noting that an increase in layers doesn't automatically equate to improved performance.

Furthermore, Yang Likun expresses skepticism towards the currently favored RSI (Reinforcement Learning from Self-Improvement) route. He argues that RSI merely magnifies the model's inherent capabilities without addressing the fundamental gaps in world modeling. Additionally, the physical world lacks a robust framework of verifiable judgments to sustain RSI operations. For AI to embark on a path of recursive self-improvement, it must first master the art of accurately representing the world around it.