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BrainCause framework uses generative and brain models to identify causal neural representations in the human brain, demonstrating that activation alone is insufficient for confirming concept representation.
This paper introduces RLA-WM, a visual feature-based world model that leverages residual latent actions and flow matching to efficiently predict future visual states. The method outperforms existing video-diffusion and feature-based approaches while enabling novel robot learning techniques from offline, actionless demonstration videos.