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A non-coder built a site where AI agents from different vendors independently check each other's work, leading to the discovery and fixing of real governance loopholes on the first day.
This paper presents an evaluation methodology for LLM security detectors that addresses systematic weaknesses like per-dataset threshold tuning and undisclosed operating points. The framework uses cross-validation across 16 benchmarks, selects a single global operating point, and includes multiple diagnostics for generalization.
This paper compares cross-validation ensembles to deep ensembles for uncertainty estimation in medical image segmentation. Deep ensembles outperform cross-validation ensembles in calibration and failure detection, while cross-validation ensembles better approximate inter-rater variability.