Imagine trying to navigate a maze blindfolded. That’s essentially how today’s AI agents often approach tasks in simulated environments. They stumble around, making random moves, because they lack a fundamental understanding of how the world works. Researchers are tackling this challenge by giving AI agents something akin to a “mental model” – a World Knowledge Model (WKM). This model provides the agent with both global knowledge about the task (like knowing where to find an egg in a kitchen) and local knowledge about the current situation (like remembering that the egg has already been retrieved). The WKM is trained using data from both successful and failed attempts at completing a task. This allows the agent to learn not only what works, but also what doesn’t, much like how humans learn from experience. The results are impressive. Agents equipped with a WKM perform significantly better on simulated tasks, reducing their reliance on random trial-and-error and avoiding nonsensical actions. This research opens exciting new doors for AI. By giving agents a better grasp of the world around them, we can unlock their full potential for solving complex problems, from navigating virtual environments to assisting with real-world tasks. However, challenges remain. Defining exactly what knowledge an AI model possesses is still an open question. Furthermore, current WKMs are limited to text-based information, while real-world knowledge is often multi-modal. Future research will focus on addressing these limitations, paving the way for even more sophisticated and capable AI agents.