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Action-Consequence Alignment for Reliable Planning and Self-Improving in Latent World Models
Latent world models learn to predict observed transitions, yet low prediction error alone does not guarantee reliable planning. Inspired by self tickling experiments in neuroscience showing that disrupting motor sensory correspondence increases prediction mismatch, we examine whether learned world models preserve an an...
Beyond One-Step Accuracy: State-Affine Latent Transition for Reliable Visual Planning
This work decomposes multi-step rollout error into the errors introduced at individual steps and their propagation through subsequent transitions, showing that state-affine dynamics are precisely the differentiable transitions with state-independent Jacobians, eliminating the nonlinear propagation residual and making t...