Collapse Index (CI): A Diagnostic Framework for Bounded, Lightweight, and Reproducible Evaluation of System Instability

This upload contains the preprint “Collapse Index (CI): A Diagnostic Framework for Bounded, Lightweight, and Reproducible Evaluation of System Instability.”

The Collapse Index introduces a normalized instability metric designed to reveal hidden brittleness in machine learning systems under benign, non-adversarial perturbations. CI highlights reliability failures that appear stable under standard evaluation methods such as accuracy or confidence-based metrics.

The framework emphasizes:

 • a bounded instability score 0,1 for interpretability
 • lightweight evaluation requiring only model predictions
 • dataset-driven perturbation analysis
 • sealed, reproducible output bundles using cryptographic hashes

Each evaluation run produces standardized diagnostics including instability scores, summary tables, and full provenance metadata to support auditability and reproducibility.

This deposit includes the full preprint.
Associated evaluation artifacts are generated separately and delivered as sealed bundles.

Project page: https://collapseindex.org

Licensed under CC BY-NC-ND 4.0.