LLM and World Models: Convergence, Divergence, and AGI Paths

This research report from mid-2026 analyzes the evolving relationship between Large Language Models (LLMs) and World Models as two distinct paths toward Artificial General Intelligence (AGI). While LLMs excel at predicting discrete symbols through statistical patterns in human text, World Models focus on learning environmental dynamics by treating "actions" and "states" as primary variables. The text identifies a fundamental disagreement between generative approaches that simulate reality through pixels and latent models like JEPA that predict abstract representations to avoid unnecessary detail. Despite these differences, a synthesis is emerging through Vision-Language-Action (VLA) models, where LLMs serve as high-level planners while specialized world models handle physical simulation and motor control. Evidence suggests that while LLMs may develop internal representations of logic—such as board game states—they still lack the sensorimotor grounding required for complex physical tasks. Ultimately, the report predicts a future of heterogeneous integration rather than a single architecture, driven by the varying computational demands and hardware constraints of each modeling approach