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This work studies action-conditioned predictors built on five frozen self-supervised visual backbones, finding that a world model guides action selection reliably only when the goal lies within, or slightly beyond, the trajectory it imagines during planning.
This work measures both directions, repair and corruption, of the reasoning chain, a working control surface that moves the robot, repairing behaviour when the text is right and corrupting it when the text is wrong.
The results reveal that attention-space regulation offers a considerably more promising path to safer diffusion transformer based image generation than the existing concept erasing mechanism.
Intermediate Text Representation (IR)-guided diffusion is proposed, which injects intermediate hidden states of the text encoder into the conditioning signal during early denoising steps, recovering suppressed concepts without any additional training, optimization, or external models.
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