# LGWM

For GUI agents, the safety signal may lie not in the screen but in the transition that produced it: the same credential screen can be a legitimate result of tapping Sign in or a hijacked result of tapping View order. LGWM skips rendering the next screen and predicts its representation directly in the observation encoder's space, reducing verification to a vector comparison; on RSWT-Bench, where each credential screen appears pixel-identically under both labels, the training-free consistency score reaches 0.987 AUC at 17 ms per decision, on par with the strongest closed-source vision-language models and about ten AUC points above generative GUI world models.

For GUI agents, the safety signal may lie not in the screen but in the transition that produced it: the same credential screen can be a legitimate result of tapping Sign in or a hijacked result of tapping View order. LGWM skips rendering the next screen and predicts its representation directly in the observation encoder's space, reducing verification to a vector comparison; on RSWT-Bench, where each credential screen appears pixel-identically under both labels, the training-free consistency score reaches 0.987 AUC at 17 ms per decision, on par with the strongest closed-source vision-language models and about ten AUC points above generative GUI world models.