geometric_safety_features v1.0.0: Production ReleaseThis is the production release of geometric_safety_features, a Python library for computing geometric uncertainty signals from embedding spaces to detect high-uncertainty regions in AI models, with applications to AI safety diagnostics.Key Features- 7 Core Geometric Features: k-NN based metrics including knn_std_distance (neighborhood spread), knn_mean_distance, knn_min_distance, knn_max_distance, local_curvature, ridge_proximity, and dist_to_ref_nearest- Advanced Baselines: S-score (density-scaled dispersion), class-conditional Mahalanobis distance, and conformal prediction for uncertainty quantification- Scalable Backends: sklearn (default) and FAISS (optional) for high-performance nearest neighbor search on large datasets- Comprehensive Validation: Boundary-stratified evaluation methodology with reproducible experimentsValidation ResultsRigorous evaluation demonstrates significant improvements in detecting high-uncertainty regions:- +12.5% improvement in borderline cases (p < 0.001)- +11.4% improvement in unsafe regions (p < 0.001)- knn_std_distance identified as the most consistent uncertainty signal across datasetsTechnical Details- Language: Python 3.9+- Dependencies: NumPy, SciPy, scikit-learn- Optional: FAISS for performance scaling- License: MIT- Documentation: Complete API reference and usage examplesInstallation# Install from PyPIpip install geometric-safety-features# For high-performance backendpip install geometric-safety-features[faiss]Usage Examplefrom mirrorfield.geometry import GeometryBundleimport numpy as np# Load embeddingsreference = np.random.randn(1000, 256)query = np.random.randn(100, 256)# Compute geometric featuresbundle = GeometryBundle(reference, k=50)features = bundle.compute(query)# Access uncertainty signalsuncertainty_scores = features['knn_std_distance']Files Included- geometric_safety_features-1.0.0.tar.gz: Complete source distribution- geometric_safety_features-1.0.0-py3-none-any.whl: Python wheel for easy installation- Full test suite and documentationRelated Resources- GitHub Repository: https://github.com/DillanJC/geometric_safety_features- PyPI Package: https://pypi.org/project/geometric-safety-features/- Technical Report: See docs/TECHNICAL_REPORT.md for complete methodology and resultsCitation DOI will be assigned by Zenodo after this release.