Sending every uncertain AGN spectral fit to a vision language model makes single-epoch black hole masses worse, and gating it to the fits a rule pass flags removes the harm, so the gate matters more than the model.

In a low signal-to-noise SDSS sample, routing every non-passing fit to the model (about 93 per cent of them) widened the scatter against literature masses from 0.35 to 0.47 dex, broke twice as many fits as it rescued, and pulled masses down by 0.19 dex. Restricting the model to fits a deterministic rule pass had already flagged, about half as many calls, left it level with the rule baseline instead (McNemar p = 0.8). The mechanism is specific. Broad H-beta is fit with several Gaussians, one of them a wide low-amplitude pedestal that never returns to zero inside the line window, and the model's "use fewer broad components" edit narrows a genuinely broad line, lowering the width and so the mass by 0.15 to 0.25 dex on the flagged subset. The claim worth testing is that quality-control gains come from where a model is allowed to act, not from which model is used. Testing it needs a deterministic fitting engine with a closed set of edits, a truth sample, and one model run under two gates.

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