Humans use a comprehensive world model for planning and decision-making, a concept explored in AI research by figures like Jurgen Schmidhuber and Yann Lecun through 'World Models'. These models are predominantly applied in the physical realm, particularly within the video and image AI spheres, rather than directly in decision-making or planning. Large Language Models (LLMs), which primarily predict the next token in a sequence, inherently lack the capability to plan or make decisions. However, a new research paper on Hierarchical Planning demonstrates a method that employs world modeling to outperform leading LLMs in a planning benchmark, suggesting a potential pathway for integrating world modeling with LLMs for enhanced planning capabilities. This matters because it highlights the limitations of current LLMs in planning tasks and explores innovative approaches to overcome these challenges.
