Current AI evaluation of world models is too narrow, favoring static representations learned from massive datasets over efficient learning through interaction

However, current understanding and evaluation of world models in artificial intelligence (AI) remains narrow, often focusing on static representations learned from training on massive corpora of data, instead of the efficiency and efficacy in learning these representations through interaction and exploration within a novel environment.