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Sandwich-Residuals: Parameter-Efficient Test-time Adaptation of World Models

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Latent world models enable planning by predicting the effects of actions in a learned representation space, but their predictions can become unreliable when test-time conditions differ from training. Existing test-time adaptation methods address this by updating parts of the pretrained model, often modifying millions of parameters and requiring a choice of which internal components to adapt. We introduce Sandwich-Residuals, a lightweight alternative that keeps the pretrained world model frozen and learns only small residual corrections around the predictor. The residuals are optimized online u

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