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Evidence record 6540 · automatically gathered

Empirical Validation of the Classification-Verification Dichotomy for AI Safety Gates

Can classifier-based safety gates maintain reliable oversight as AI systems improve over hundreds of iterations? We provide comprehensive empirical evidence that they cannot. On a self-improving neural controller (d=240), eighteen classifier configurations -- spanning MLPs, SVMs, random forests, k-NN, Bayesian classifiers, and deep networks -- all fail the dual conditions for safe self-improvement. Three safe RL baselines (CPO, Lyapunov, safety shielding) also fail. Results extend to MuJoCo benc

Record details

Published: 31 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment

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How to cite this record

ethics.ai (31 March 2026), “Empirical Validation of the Classification-Verification Dichotomy for AI Safety Gates,” evidence record 6540, https://ethics.ai/record/6540 (originally published by arXiv).

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