SAMLens: Canonical Quality Can Hide Negative-Point Reversal in Specialized Segment Anything Models

Specialized versions of the Segment Anything Model (SAM) keep their point-prompt interface, yet release checks usually validate only the single positive prompt used in training. Among the prompts that remain available, a negative point has the least ambiguous meaning: the mask should not grow into the object it marks. We introduce SAMLens, a paired audit that gives reference and specialized endpoints identical images and prompts and reports each prompt relation both as an absolute response and as a change. On dense cell images, full mask-decoder tuning reverses this instruction: a negative point inside a neighboring object raises the predicted overlap with that object from 0.03 to 0.53, whereas endpoints of other SAM-family adaptations whose canonical Dice lies within 0.03 of this endpoint keep the object excluded or add only a small part of it. The reversal recurs in two further training seeds on cell images and in three on histopathology nuclei, and no other audited adaptation shows a reversal of comparable size. Changes in same-target stability and target switching do not separate the reversed endpoint from the others. A candidate decomposition places the reversal in the returned masks rather than in score-based selection, and shows that the relative decline seen after partial tuning is mostly response-range compression. Canonical Dice and change-only summaries of prompt behavior can therefore hide whether a specialized segmenter still follows negative points.