Sony (Japan)

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.

Using ADReSSo and three large self-supervised speech backbones, the study applies controlled noise and reverberation interventions to participant-speech-only, non-speech, and full-recording audio, and combines layer-wise linear decoding, input- and representation-space interventions, and geometric alignment analysis to show that controlled acoustic interventions alter Alzheimer's disease predictions across all three backbones, that noise, despite showing no significant diagnostic-group difference in the original data, produces the strongest intervention effects, and that these effects are systematically structured relative to the classifier's decision direction, replicate on the held-out test set, and reverse when the representation-space intervention direction is reversed.

Using ADReSSo and three large self-supervised speech backbones, the study applies controlled noise and reverberation interventions to participant-speech-only, non-speech, and full-recording audio, and combines layer-wise linear decoding, input- and representation-space interventions, and geometric alignment analysis to show that controlled acoustic interventions alter Alzheimer's disease predictions across all three backbones, that noise, despite showing no significant diagnostic-group difference in the original data, produces the strongest intervention effects, and that these effects are systematically structured relative to the classifier's decision direction, replicate on the held-out test set, and reverse when the representation-space intervention direction is reversed.