One-Step Next-Latent Prediction Is Not a World Model
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper investigates how autoregressive language models can iterate a hidden state without emitting tokens, identifying three distinct risks associated with a latent process: token risk, teacher‑forced latent risk, and open‑loop risk. It shows that when the Bayes next‑token posterior depends on a full‑rank $k$‑dimensional statistic, a width‑$k$ latent with linear least‑squares transition and a refitted unembedding can match the Bayes token predictor, but this match is only up to an invertible gauge. The study also highlights sharp obstructions: a constant latent can drive teacher‑forced risk to zero while token cross‑entropy reverts to marginal entropy, and a perfect token predictor may still conceal a null‑space walk that causes open‑loop error to grow linearly with horizon, indicating that small latent or token losses alone do not guarantee accurate latent rollout without additional rank or injectivity conditions.
arXiv:2610.26979v1 Announce Type: new Abstract: Latent world models let an agent plan in latent space. The planner searches for an action sequence that minimizes a cost, and that cost is often the $\...
arXiv:2607. 10362v1 Announce Type: new Abstract: Latent world models are trained to predict future states in a learned representation and are then deployed inside a planner that selects actions by simulating them forward.
arXiv:2609.25541v1 Announce Type: new Abstract: Tabular foundation models learn to predict cell values in context, whereas world-model self-supervision asks for prediction in representation space (Le...
arXiv:2606. 03003v1 Announce Type: cross Abstract: A latent world model built from an equivariant encoder $E$ and an equivariant predictor $f$ inherits a provable symmetry of its training loss: when the world's dynamics genuinely carries a group $G$ acting on latents by an orthogonal representation $\rho(g)$, the one-step prediction relMSE is exactly invariant across the whole group, so fitting the dynamics on a restricted slice of orientations mathematically determines it on the entire orbit (j\v{u} y\=i f\v{a}n s\=an).
The paper challenges the common practice of selecting latent world models based on one‑step prediction loss, arguing that this metric poorly predicts planning performance. Using a frozen‑decoder protocol, the authors decompose one‑step error into encoder‑inherent and predictor‑added components and evaluate goal‑conditioned planning across three continuous‑control environments. Their experiments show that improvements in one‑step fidelity do not correlate with planning success, with correlation coefficients near zero and inconsistent exchange rates across tasks.