SOURCE-LINKED INTELLIGENCE
Semantic Fibers and Cross-Gram Interference: A Calculus of Safety Drift in Overcomplete Representations
A deployed language model may refuse a harmful request in English yet comply with its faithful translation, revealing a cross-lingual safety failure that cannot be characterized reliably by output behavior alone. We formalize this phenomenon through an audited equivalence relation and show that, for a declared quotient, representation, metric, feature dictionary, scoring head, threshold, and contrast model, the resulting safety drift admits an exact linear-algebraic characterization. Specifically, the drift is a cross-Gram functional of the within-fiber contrast; its worst admissible value is
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T00:23:32.000Z
First collected: 2026-09-20T12:21:05.240Z. This is not the publication date.