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This research aims to identify and analyze a new failure mode in AI safety infrastructure that is distinct from known issues.

April 19, 2026Open Access

A Category-Level Failure Mode Not Captured by Distribution Metrics

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This research aims to identify and analyze a new failure mode in AI safety infrastructure that is distinct from known issues.
Specified three failure conditions: self-reference, anchor drift, and proxy displacement.
Demonstrated these conditions using the 2008 Gaussian copula collapse in credit markets.
Identified reinforcing paths in ML training infrastructure that emulate these failure conditions.
No current safety approach within ML meets all failure conditions simultaneously.
Current external safety methods do not adequately address the speed dimension related to shared substrate.
Pointed out limitations of viewing AI safety as merely a technical problem.

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Working paper reveals a new failure mode in AI safety infrastructure, suggesting deeper evaluation challenges.

Ghjuvan Ortulanu (2026) studied this question.

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