Okay, I think we are in agreement. Let's say current LLM architectures are clearly capable of developing internal representations, not just learning surface sta

Okay, I think we are in agreement. Let's say current LLM architectures are clearly capable of developing internal representations, not just learning surface statistics, and it can execute algorithmic computation as complex as computing Othello board states, and it can completely generalize out of distribution thanks to such algorithmic computation. (One experiment was to completely eliminate any Othello games starting with move C5 from the training data. The model had no trouble generating only legal moves for C5 games.) I agree whether LLMs trained on natural language do develop world model is an interesting and open empirical question. They clearly can, but we don't know whether they do. I do consider the evidence pretty suggestive that they do. But this is clearly just an empirical question, not a question that can be answered by arguments as useless as "they are statistical models", because statistical models can and do learn world model. I actually consider your considering this result not incredibly surprising pretty surprising.