Action-Consequence Alignment for Reliable Planning and Self-Improving in Latent World Models

Excerpt

arXiv:2610.04539v1 Announce Type: new Abstract: 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 analogous action consequence correspondence.The results show nearby alternatives can receive lower prediction errors despite producing