This research paper introduces **temporal straightening**, a technique designed to improve **latent planning** in AI world models by regularizing the curvature of agent trajectories. While standard visual encoders often produce highly curved paths in latent space, this approach uses a **curvature regularizer** to create a representation where feasible transitions follow straighter lines. This geometric transformation ensures that **Euclidean distance** serves as a more accurate proxy for the actual distance to a goal, significantly improving the stability of **gradient-based optimization**. Theoretical analysis demonstrates that straightening the latent space leads to a better-conditioned **planning objective**, allowing planners to converge more efficiently. Empirical tests across several goal-reaching tasks, such as **PointMaze** and **PushT**, show that this method substantially increases success rates for both open-loop and closed-loop planning. Ultimately, the work suggests that the **geometric structure** of learned representations is a critical factor in the effectiveness of autonomous planning systems.