Arm Introduces ALeWM, a JEPA World Model with Adaptive Latent Prefixes
Arm · hf · 2026-10-07
Arm researchers introduce ALeWM, a JEPA-based world model that learns to concentrate predictive information in compact prefixes of a wide latent representation. It learns a sequence-conditioned distribution over prefix lengths and trains the predictor to estimate the full next embedding from a sampled prefix.
To organize latent coordinates by predictive importance, they propose MixSIGReg, regularizing masked embeddings against a prior-weighted mixture of Gaussian active prefixes and zeros. Analysis shows this assigns higher variance to earlier coordinate blocks and minimizes prediction error.
In controlled dynamical systems and goal-conditioned visual control, ALeWM consistently beats tuned fixed-width LeWM with lower average planning capacity.
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