Reduced-Precision Deployment Can Keep the First Mask and Still Change How Interactive Segmenters Correct

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Edge deployments of promptable segmenters are checked by the quality of the first mask or by simulated click counts, yet an interactive tool is used through its corrections. We measure deployment on the correction itself: from the same image, click history and starting mask, we compare the next mask of a TensorRT engine with that of its PyTorch reference, splitting the change into the part of the starting error removed and the new error introduced. Across 6 segmenters on a Jetson Orin Nano and two benchmarks, the explicit INT8 engines of SAM 2.1-T, SAM 2.1-S, EdgeTAM and MobileSAM and the implicit INT8 engines of EdgeTAM, MobileSAM and EdgeSAM change both parts of the next correction, each under Holm control within its test family, and EfficientViT-SAM-L0 does so already at FP16 (+0.028 of the starting error removed and +0.056 introduced); the changes largely persist on objects whose first masks score alike, and simulated users need more clicks for most of these engines. For EfficientViT-SAM-L0 the change traces to 46 convolutions in the last two backbone stages: constraining only those to FP32 in an FP16-enabled engine removes 95% and 100% of the change on held-out data and keeps both mean differences of the repaired engine from the reference within ±0.01 of the starting error, at 20.9 ms against 13.8 ms for FP16. A deployment check for an interactive segmenter should replay corrections, not only score the first output.