Bridging Latent Space Reasoning to External World Model Representation for Language Models with Iterative Hypothesis Cycles
This paper explores how language models generate and refine internal hypotheses while constructing world models, aiming to bridge their latent reasoning with structured external representations. By analyzing iterative hypothesis cycles, we investigate whether fundamental system rules emerge from latent space dynamics and propose methods to extract and refine these representations for structured reasoning.