Testing Attribution Claims in Diffusion-LM Remasking: A Localized Effect Without a Demonstrated Benefit

Masked diffusion language models decode in parallel, and training-free overlays improve them by re-decoding committed tokens. The strongest, CoRe, jointly masks a pool of low-margin candidates and re-predicts them, so a token that confidently holds a neighbor's error in place is rarely perturbed. We ask whether widening the perturbation to each candidate's neighbors helps, and test the resulting method, Local-Context-Dropout (LCD), against the controls a headline comparison omits. It fails them: its +2.44 pp HumanEval gain over CoRe is indistinguishable from spending the same extra compute on more CoRe verify calls, no gate configuration dominates, and the frozen method loses 0.86 pp on held-out MBPP problems. What survives is an attribution result. Removing one masked neighbor recovers a median 0.08 of the full-mask effect while restoring it inside the mask removes 0.52, so neighbors act in combination, and adjacent positions carry more of the effect than non-adjacent ones from the same mask, with commit confidence balanced at mask time, across two backbones and three tasks. The association is positional, not proof that proximity is the operative variable, and has no detectable signature in program correctness. Code: https://anonymous.4open.science/r/LCD-iclr27-1EE0/.