PAVER: Preserving Across-scale Visual Evidence with Risk-conditioned Interface for Remote-Sensing Tiny-Object Detection

Remote-sensing detectors must recover few-pixel targets from cluttered wide-area imagery and assign informative confidence to each candidate. Both demands share an evidence-retention bottleneck: weak cues must survive feature formation and cross-scale propagation, then remain accessible at inference. Preserving Across-scale Visual Evidence with Risk-conditioned Interface (PAVER) is a framework combining an evidence-preserving detector with a separately fitted Risk-conditioned Interface. The Complementary State-Flow Mixer (CSFM), Semantic–Contour Recalibration Gate (SCRG), and High-Resolution Pyramid Extension (HRPE) maintain complementary, localization-sensitive, and high-resolution pathways. The interface makes candidate confidence more trustworthy by recalibrating each fixed detection toward its likelihood of being a true positive, using a false-positive–true-positive prototype margin from P2 and P3 ROI evidence without changing boxes or classes. Under controlled reproduction, PAVER improves YOLOv12-s by 5.7 and 4.0 points in $mA{P}_{50}$ and $mA{P}_{50:95}$, respectively, on VisDrone, with complementary evaluation on RSOD and DIOR. On VisDrone test-dev, risk conditioning gives slightly favorable calibration point estimates over Platt scaling while keeping ${\mathrm{AP}}_{50}$ essentially unchanged; all paired 95% CIs include zero, and temperature scaling retains the best ECE and NLL. PAVER combines strong detection with candidate-local confidence conditioning through its optional Risk-conditioned Interface. Code is publicly available at [https://anonymous.4open.science/r/PAVER-F47C/](https://anonymous.4open.science/r/PAVER-F47C/).