# Uncertainty Estimation

Cross-modal remote sensing retrieval must match semantically identical scenes between synthetic aperture radar and multispectral imagery, yet earlier JEPA-style methods regress a single deterministic latent for each masked patch and assume every region is equally predictable. CAPE-JEPA instead outputs a per-patch Gaussian mean and variance, trains it by negative log-likelihood, and adds adversarial cross-modal distributional regularization plus in-training contrastive learning. On BEN-14K and fMoW it beats baselines including X-JEPA, SkySense and CROMA across all eight retrieval directions, gaining up to 13.74 points over X-JEPA on BEN-14K cross-modal directions and up to 15.59 on fMoW, with 407.30M parameters versus 432.72M.

Cross-modal remote sensing retrieval must match semantically identical scenes between synthetic aperture radar and multispectral imagery, yet earlier JEPA-style methods regress a single deterministic latent for each masked patch and assume every region is equally predictable. CAPE-JEPA instead outputs a per-patch Gaussian mean and variance, trains it by negative log-likelihood, and adds adversarial cross-modal distributional regularization plus in-training contrastive learning. On BEN-14K and fMoW it beats baselines including X-JEPA, SkySense and CROMA across all eight retrieval directions, gaining up to 13.74 points over X-JEPA on BEN-14K cross-modal directions and up to 15.59 on fMoW, with 407.30M parameters versus 432.72M.

Subtle pediatric wrist fractures often appear on radiographs only as a focal cortical interruption or a fine linear lucency, and the epiphyses, growth plates and overlapping bones make these signs hard to separate. MV-CortexGuard compares cortical continuity across frontal and lateral views in a shared anatomical coordinate frame and refers high-risk examinations for expert review. On the internal GRAZPEDWRI-DX test set it reached 52.7% average precision on the size-defined small-fracture subset, 6.6 points above YOLOv10m, and 63.4% AP@50–95 overall.

Subtle pediatric wrist fractures often appear on radiographs only as a focal cortical interruption or a fine linear lucency, and the epiphyses, growth plates and overlapping bones make these signs hard to separate. MV-CortexGuard compares cortical continuity across frontal and lateral views in a shared anatomical coordinate frame and refers high-risk examinations for expert review. On the internal GRAZPEDWRI-DX test set it reached 52.7% average precision on the size-defined small-fracture subset, 6.6 points above YOLOv10m, and 63.4% AP@50–95 overall.

In safety-of-the-intended-functionality analysis under ISO 21448, an uncertainty threshold that keeps a LiDAR detector within a performance target under one condition had no procedure to check whether it still does under another. The threshold is transferred unchanged into the condition of use, and a statistical test decides at a stated error probability whether the target is applicable, not applicable or not determined, together with a lower bound on the independent units needed to show the target is met. On PointPillars ensembles over KITTI and nuScenes, counted in frames, the threshold transferred from nuScenes to KITTI was not applicable on all five splits; counted in recorded sequences, neither dataset held enough units to show any target limit met.

In safety-of-the-intended-functionality analysis under ISO 21448, an uncertainty threshold that keeps a LiDAR detector within a performance target under one condition had no procedure to check whether it still does under another. The threshold is transferred unchanged into the condition of use, and a statistical test decides at a stated error probability whether the target is applicable, not applicable or not determined, together with a lower bound on the independent units needed to show the target is met. On PointPillars ensembles over KITTI and nuScenes, counted in frames, the threshold transferred from nuScenes to KITTI was not applicable on all five splits; counted in recorded sequences, neither dataset held enough units to show any target limit met.

Automated angle-of-progression (AoP) measurement from intrapartum ultrasound is accurate to a few degrees on average, yet a small fraction of frames fails silently by tens of degrees. On the 501 test files of the public IUGC2025 benchmark, an ensemble of six landmark detectors ranked frames by disagreement between detectors; referring the most suspicious 5% for manual review lowered the share of errors above 20 degrees among retained frames from 1.2% to 0.2%, catching five of six. The missed case was one all six detectors underestimated with little spread between them; no difference was detected between angle-space and coordinate-space disagreement at the primary endpoint, and thresholds set on validation frames did not hold their intended referral rate on test frames.

Automated angle-of-progression (AoP) measurement from intrapartum ultrasound is accurate to a few degrees on average, yet a small fraction of frames fails silently by tens of degrees. On the 501 test files of the public IUGC2025 benchmark, an ensemble of six landmark detectors ranked frames by disagreement between detectors; referring the most suspicious 5% for manual review lowered the share of errors above 20 degrees among retained frames from 1.2% to 0.2%, catching five of six. The missed case was one all six detectors underestimated with little spread between them; no difference was detected between angle-space and coordinate-space disagreement at the primary endpoint, and thresholds set on validation frames did not hold their intended referral rate on test frames.

The work proposes SAUCE, a training-free uncertainty estimator that casts uncertainty in parallel multi-agent reasoning as sequential inference over a latent system-level belief: each round's cross-agent agreement serves as evidence and its generation uncertainty sets the evidence weight, updated through a filtering-style recursion, improving misclassification detection, selective prediction, and calibration across five backbones, five benchmarks, and the Debate and DyLAN protocols.

The work proposes SAUCE, a training-free uncertainty estimator that casts uncertainty in parallel multi-agent reasoning as sequential inference over a latent system-level belief: each round's cross-agent agreement serves as evidence and its generation uncertainty sets the evidence weight, updated through a filtering-style recursion, improving misclassification detection, selective prediction, and calibration across five backbones, five benchmarks, and the Debate and DyLAN protocols.

The study adapts the LLM Uncertainty Head framework to two Persian medical models built on Aya-Expanse-8B, Gaokerena-V and Gaokerena-R; it first observes on a 168-question Iranian medical entrance examination that Gaokerena-V has substantially lower five-run consistency than Aya-Expanse-8B while Gaokerena-R is comparable, then builds Persian claim-level hallucination datasets with 1,600 responses per backbone and trains lightweight claim-level heads on frozen backbone attention maps and token probabilities, obtaining held-out PR-AUCs of 0.4820 and 0.4652 (2.30 and 2.66 times their random baselines) and ROC-AUCs of 0.7852 and 0.7810, with no retrieval or repeated sampling at inference.

The study adapts the LLM Uncertainty Head framework to two Persian medical models built on Aya-Expanse-8B, Gaokerena-V and Gaokerena-R; it first observes on a 168-question Iranian medical entrance examination that Gaokerena-V has substantially lower five-run consistency than Aya-Expanse-8B while Gaokerena-R is comparable, then builds Persian claim-level hallucination datasets with 1,600 responses per backbone and trains lightweight claim-level heads on frozen backbone attention maps and token probabilities, obtaining held-out PR-AUCs of 0.4820 and 0.4652 (2.30 and 2.66 times their random baselines) and ROC-AUCs of 0.7852 and 0.7810, with no retrieval or repeated sampling at inference.

The study introduces VRPTR, a three-dimensional encoder-decoder combining a compressed Transformer bottleneck, variational latent sampling, and multiscale skip connections, trained on 360 healthy adults from the WU-Minn Human Connectome Project and evaluated on 40 held-out participants for the story-versus-math language contrast, achieving mean voxel-map Pearson r=0.642 and Dice AUC=0.519, exceeding compact volumetric BrainSurfCNN-like and SWIFUN-like comparators by Δr=0.0376 and 0.0335 respectively, while raw 95% intervals covered only 4.7% of observed values and five-fold calibration within the held-out cohort raised coverage to 94.8%.

The study introduces VRPTR, a three-dimensional encoder-decoder combining a compressed Transformer bottleneck, variational latent sampling, and multiscale skip connections, trained on 360 healthy adults from the WU-Minn Human Connectome Project and evaluated on 40 held-out participants for the story-versus-math language contrast, achieving mean voxel-map Pearson r=0.642 and Dice AUC=0.519, exceeding compact volumetric BrainSurfCNN-like and SWIFUN-like comparators by Δr=0.0376 and 0.0335 respectively, while raw 95% intervals covered only 4.7% of observed values and five-fold calibration within the held-out cohort raised coverage to 94.8%.