How Murky Before a VLM Knows It Cannot See? Accuracy, Confidence and Abstention on a Physical Visibility Axis

Mochamad Rizal Fauzan, Irgi Surya

Robustness studies of vision-language models (VLMs) grade degradation on nominal severity scales ("level 1-5") that carry no physical units, and they rarely ask whether a model's confidence follows the loss of visibility. We measure accuracy, internal (token-probability) confidence, verbalized confidence and an explicit abstain option on a visibility axis in metres: fog follows the Koschmieder model, an underwater arm scales the absorption spectrum of pure water, both are applied with a monocular metric depth map, and the visibility range V is swept over eight log-spaced levels from 250 m to 1.95 m. On 1000 COCO scenes and five small open VLMs (2-5 B parameters), accuracy on a boxed-object identification task falls from near its clean value to chance within a narrow band (half-way visibility V_acc of 12.1-15.9 m in fog and 20.5-38.6 m under water) while, for the four models that are confident on clean images, the confidence stays high: at V=1.95 m in fog their accuracy is at chance but their mean internal confidence is 0.68-0.84 and their verbalized confidence 0.61-1.00, a blind-spot width W=log10(V_acc/V_aware) of at least 0.79 decades, i.e. a factor of 6.2 in visibility. The…