Lots of confusion about what a world model is. Here is my definition: Given: - an observation x(t) - a previous estimate of the state of the world s(t) - an action proposal a(t) - a latent variable proposal z(t) A world model computes: - representation: h(t) = Enc(x(t)) - prediction: s(t+1) = Pred( h(t), s(t), z(t), a(t) ) Where - Enc() is an encoder (a trainable deterministic function, e.g. a neural net) - Pred() is a hidden state predictor (also a trainable deterministic function). - the latent variable z(t) represents the unknown information that would allow us to predict exactly what happens. It must be sampled from a distribution or or varied over a set. It parameterizes the set (or distribution) of plausible predictions. The trick is to train the entire thing from observation triplets (x(t),a(t),x(t+1)) while preventing the Encoder from collapsing to a trivial solution on which it ignores the input. Auto-regressive generative models (such as LLMs) are a simplified special case in which 1. the Encoder is the identity function: h(t) = x(t), 2. the state is a window of past inputs 3. there is no action variable a(t) 4. x(t) is discrete 5. the Predictor computes a distribution over outcomes for x(t+1) and uses the latent z(t) to select one value from that distribution. The equations reduce to: s(t) = [x(t),x(t-1),...x(t-k)] x(t+1) = Pred( s(t), z(t), a(t) ) There is no collapse issue in that case.