Path-traced synthetic stereo is a primary training substrate for disparity networks, and the pipelines that consume it assume Monte~Carlo (MC) rendering noise is independent across the two views. The assumption is correct at the level it is stated---individual samples---but silent about the object a network actually sees. We show that the per-pixel MC \emph{variance fields}, though built from independent samples, are strongly correlated once aligned by the ground-truth disparity, and that a network can learn to match with that correlation. Across 20 indoor scenes the warped correlation is $0.754\pm0.016$ against $0.360$ unwarped; it replicates on a second renderer with a different sampler and sample budget ($0.743\pm0.044$), and a seed-count analysis puts the population value near $0.85$, making the measurement a lower bound. The effect is field-level, not sample-level: at the warp correspondence the signed per-seed residual correlation is $-0.0004$ while the residual envelope correlates at $0.38$. A capacity-limited siamese probe given nothing but variance-field patches reaches $78.8\%$ two-alternative forced-choice accuracy on held-out scenes and falls to chance once the alignment is destroyed; under the ordinary single-render condition, decorrelation still costs $2.40$ percentage points in a difference-in-differences design, positive in all six held-out scenes. A real sensor's variance is fixed by its own signal rather than by transport difficulty, so across views it is redundant with intensity: the cue is specific to rendered data.

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