According to Yann LeCun, a new paper details the development of a JEPA model for planning in latent space using Variance-Covariance regularization, which can enhance predictive accuracy in machine learning applications. This has potential implications for improving algorithmic trading strategies by optimizing data-driven decision-making processes.