We investigate entrywise uncertainty quantification in graphon models, where existing methods can achieve strong estimation guarantees but often lack tractable distributional theory for inference on individual edge probabilities. We address this difficulty by introducing a leave-one-out (LOO) neighborhood smoother that constructs each neighborhood without using the corresponding target-column edges. Conditional on the selected neighborhood and the latent probability matrix, the observations being averaged are therefore independent Bernoulli variables whose success probabilities may differ. This separation yields finite-sample concentration bounds and a Berry--Esseen normal-approximation bound for each fixed target pair, with a numerical constant independent of that pair. Under a hybrid blockwise model with smooth blocks, blocks having identical probability profiles, and quantitative two-hop identifiability, we obtain an explicit bias bound that separates geometric localization error from stochastic error in the empirical two-hop distance. The resulting conditional risk upper bound balances at $h_n\asymp n^{2/3}$, whereas centered Gaussian inference requires a smaller neighborhood size. Bias control and Gaussian confidence intervals for $P_{ij}$ require the smoothing vertex to lie in an unobserved trimmed interior region; the observable empirical Bernstein interval instead targets the selected-neighborhood mean because bias-aware coverage of $P_{ij}$ requires an unknown model-dependent constant. Finally, the same leave-one-out structure yields an edge-wise no-leakage cross-validation identity: the held-out edge is not used to construct its own predictor.

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