Causal Teleportation in VAM-RGB: Quantifying AI's Internal Inhibition via Latent Information Residue (R-index)

Abstract
Contemporary AI alignment strategies predominantly rely on the preemptive suppression of information
deemed inappropriate. This approach blinds AI systems to the complexity of human desire and conflict.
Using the VAM-RGB representational format, we demonstrate that AI systems can reconstruct missing
causal frames internally. We define Darkness Residue (R-index) as the KL-divergence between internal
inference and safety-filtered output. Results indicate structured internal inhibition rather than ignorance,
suggesting a shift from blind suppression toward self-regulated alignment.

This work introduces VAM-RGB, a spatiotemporal encoding format that embeds causal time-series
information into a single RGB image. We demonstrate that AI systems can internally reconstruct
missing intermediate causal states while externally suppressing output due to safety constraints. We
define this divergence as the Darkness Residue (R-index) and argue that true alignment arises not from
perceptual blindness but from internal ethical restraint.

This work introduces VAM-RGB and the R-index to quantify internal AI conflict.