LLMs Use World Model-like Representations

We investigate whether large language models (LLMs) employ world model-like representations during inference. Specifically, we evaluate the ability of recent LLMs to navigate grid environments and analyze their activations during action generation. We train linear, MLP, and transformer-based probes to decode environment states, goals, and progress from activations. More complex probes achieve higher accuracy, which indicates a nonlinear structure. Furthermore, we reveal a difference in representational focus across models. For Qwen3, our probes achieve higher accuracy for goals, but lower accuracy for state prediction, suggesting a goal-oriented focus. In contrast, we achieve higher state prediction accuracy for Phi-4 models, suggesting a more world model-like encoding of state-action pairs.Notably, the world model-like representations of Phi-4 correlate with higher success rates in our task. Overall, our results suggest that LLMs incorporate approximate world models that enhance performance in navigation tasks.